{"paper_id":"0fdc2587-8a29-4cd6-b86c-e8cfb8adfc2c","body_text":"Detection of chronic cerebellar and cerebral cortical microinfarctions on T2 and FLAIR 1.5T MRI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Detection of chronic cerebellar and cerebral cortical microinfarctions on T2 and FLAIR 1.5T MRI Dimitri Renard, Yassine Serghine, Francois Louis Collemiche, Angelique Parayre, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7091069/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Feb, 2026 Read the published version in Acta Neurologica Belgica → Version 1 posted You are reading this latest preprint version Abstract Background: Detection rate of chronic cerebral cortical microinfarctions (CMI) is low. Few data are available on chronic cerebellar CMI. Our aim was to study the ability of 1.5T FLAIR and T2-weighted imaging to detect chronic CMI and compare cerebral and cerebellar CMI location. Methods: We prospectively included stroke patients with ≥1 acute cerebral or cerebellar CMI ≤10mm on DWI. Follow-up FLAIR and T2-weighted imaging with the same 1.5T-magnet was performed after six months. Images were evaluated unblinded to initial MRI to detect signal changes at the same location as initial acute CMI. Variables influencing chronic CMI detection were analyzed in uni- and multivariate analyses. Results: We analyzed 57 patients (median age 65, 44% women) with 111 acute CMI on initial MRI. These constituted 47 cerebellar and 64 cerebral acute CMI, with median DWI size of 5.9mm and initial FLAIR positivity of 70%, without difference between cerebellar and cerebral location. On 6-month follow-up MRI, chronic CMI detection on FLAIR and T2 was similar for cerebral lesions (p=0.81), whereas for cerebellar lesions T2 showed a 2.4-fold higher detection rate than FLAIR (p<0.0001). Univariate analysis using a logistic mixed model for T2 detection of chronic CMI was associated with cerebellar CMI location (p<0.0001) and hypertension history (p=0.008). Multivariate analysis confirmed the strong association between cerebellar location and T2 detection of chronic CMI (p<0.0001, AOR=7.25, 95% CI=2.83-18.56) adjusted for hypertension. Conclusion: Compared to FLAIR, T2-weighted imaging shows highest detection rates for cerebellar CMI, with cerebellar CMI location as strongest predictor of chronic CMI detection. cerebellar cerebral cortical microinfarction T2 FLAIR 1.5T MRI Figures Figure 1 Introduction Cerebral cortical microinfarctions (CMI) are frequently observed on MRI and histology studies, especially in older patients and those with cognitive dysfunction.( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) ( 9 ) ( 10 ) ( 11 ) ( 12 ) ( 13 ) ( 14 ) The majority of these studies analyzed chronic supratentorial CMI. The few studies reporting on temporal dynamics of MRI signal of acute cerebral CMI have shown disappearance of signal changes in most patients on follow-up FLAIR and T2-weighted imaging.( 12 ) ( 14 ) A retrospective study analyzing follow-up 3T MRI (mean time between acute and follow-up MRI was 33 months, range 0.5–142) in 25 patients with acute CMI defined as ≤ 10 mm on DWI, showed a chronic CMI detection rate of 5% and 16% on FLAIR and T2-weighted imaging, respectively.( 12 ) Another 3T MRI study including seven patients with acute CMI of < 5 mm on initial DWI, showed absence of chronic CMI detection after one month in all patients on T1- and T2-weighted and FLAIR imaging.( 14 ) To the best of our knowledge, there is no data available on detection rate on MRI of chronic cerebellar CMI. Recently, our group showed that chronic, relatively small (< 20 mm) cerebellar cortical infarctions were frequently observed in acute stroke patients, especially in cardioembolic stroke.( 15 ) ( 16 ) However, in large studies and meta-analyses, posterior circulation infarction is not associated with cardioembolic source of stroke.( 17 ) ( 18 ) We hypothesized that these frequently observed small chronic cerebellar infarctions in embolic stroke patients might be related to a higher detection rate of cerebellar compared with cerebral small-sized chronic cortical infarctions. In this study, we focused on CMI and aimed to analyze the ability to detect chronic CMI on follow-up 1.5T MRI on FLAIR and T2-weighted imaging, after initial acute cerebral or cerebellar CMI of ≤ 10 mm and to compare both CMI localizations. Methods Patient selection and study population Patients were prospectively included in our stroke unit (Nîmes University Hospital, France) between February 2021 and June 2023. Inclusion criteria were age ≥ 18 years, initial MRI performed within one week after symptom onset, symptomatic brain infarction confirmed by DWI, and presence of acute cerebral and/or cerebellar CMI. The study was approved by the local ethics review board (University Hospital Nîmes, IRB number 23.12.01). Neuroimaging acquisition and analysis Initial and follow-up MRI were performed with the same 1.5T MRI (Siemens Healthineers Magnetom Sola, Siemens, Erlangen, Germany), using the same parameters for each sequence. Radiological parameters used for the different sequences are shown in Table 1 . The picture archiving and communication system available for clinicians (PACS, XERO® Viewer, Agfa HealthCare) was used for viewing and analyses of the initial and follow-up MRI. Table 1 Radiological parameters used for the different MRI sequences DWI T2 FLAIR TR (ms) 3190 3490 7000 TE (ms) 71 109 124 Bandwidth (Hz per pixel) 919 130 130 Flip angle 150 144 150 Section thickness (mm) 4 4 4 Matrix size 272 x 272 348 x 348 320 x 320 FOV (mm) 230 240 230 Acquisition time (sec) 72 69 126 NEX 1 2 1 TR, repetition time; TE, echo time; FOV, field-of-view; NEX, number of excitations Initial MRI included axial DWI, FLAIR, and T2*-weighted images. To avoid hemorrhagic lesions (e.g. cerebral microbleed, hemorrhagic infarction) causing signal changes on DWI related to the susceptibility effect of these hemorrhagic lesions, DWI lesions were excluded in case of hypointensity on T2*-weighted imaging (performed in all patients) inside or nearby (< 10 mm) the DWI lesion on initial MRI. To minimize possible topographical interpretation errors on follow-up MRI, only acute CMI were included when located > 10 mm from any other acute DWI or chronic FLAIR lesion. Acute cerebral and cerebellar CMI on initial MRI was defined as a cortical DWI lesion of ≤ 10 mm with > 50% of the lesion involving the cortex. Initial DWI size screening was performed using axial DWI images. For lesions ≤ 10 mm on axial DWI, multiplanar reconstruction DWI images were generated and DWI lesions > 10 mm on coronal or sagittal views were excluded. Follow-up MRI was scheduled six months after initial MRI. Signal changes in the exact location of the initial acute CMI were sought on these follow-up MRI assessments, using axial FLAIR and T2-weighted sequences, unblinded to initial MRI. On each sequence of follow-up MRI, each acute CMI visible on initial DWI MRI was scored present or absent and the size was measured. Follow-up MRI images were evaluated alongside the initial MRI in order to explore the same brain area involved on initial DWI. Raters were free to adjust contrast and brightness and to zoom the images in order to assess the presence or absence of signal changes on follow-up MRI. Flowchart for patient inclusion and follow-up is shown in Fig. 1. Radiological assessment was performed by one rater (DR, with 24 years’ experience in neuroradiology). To assess the inter-rater reliability for chronic CMI detection, a randomly chosen subset of 15 patients with 31 acute CMI lesions were independently assessed by another rater (YS, with 7 years’ experience in neuroradiology) using the same methods. An additional analysis was performed by a third rater (FLC, with 10 years’ experience in neuroradiology) assessing the presence of chronic cerebral or cerebellar CMI on follow-up MRI in all patients, blinded to initial imaging, using the same methods. The only information this rater received was that all patients had one or more acute cerebral and/or cerebellar CMI (≤ 10 mm and > 50% of the lesion involving the cortex) on DWI six months earlier, located > 10 mm away from any other lesion. They were given no information about the number of CMI per patient. Clinical and radiological data The patients’ clinical and radiological characteristics recorded included: age, sex, cardiovascular risk factors, time interval between symptom onset and initial MRI performance, acute stroke treatment (i.e. intravenous thrombolysis or endovascular thrombectomy), stroke etiology (according to the TOAST classification), uni- or multiterritorial acute infarction on initial MRI, acute CMI characteristics (number of acute CMI per patient, axial DWI size, and initial FLAIR positivity), time interval between initial and follow-up MRI, and chronic CMI size when detected on FLAIR or T2-weighted imaging.( 19 ) Statistics Patient clinical and MRI data were described using median and interquartile range (IQR) for quantitative variables, and numbers and percentages for qualitative variables. Patients were classified into groups according to location (i.e. cerebellar or cerebral CMI). For patients with lesions in both locations, they were assigned to the group for which they had the most frequent lesions. The clinical and radiological acute and chronic CMI characteristics were compared between groups using the Chi2 test for qualitative variables and the Wilcoxon-Mann-Whitney test for quantitative variables. To avoid potential confounding bias and possible intra-individual correlation induced by multiple lesions in the same patient, univariate/multivariate mixed logistic regression models with patient-specific random intercept were performed to analyze the effect of location on FLAIR and T2 detection rate at six months. The McNemar test was used to compare detection on FLAIR and T2 according to lesion type. In the multivariate model, only variables with a p value of less than 10% were retained in the model. A specific model adjusting for initial lesion size was also performed to ensure that there was no confounding bias at this level. Inter-rater reliability was assessed using Kappa coefficient. The interpretation of the Kappa coefficient, according to Landis and Koch (1977), was as follows: values below 0 indicate poor agreement, values between 0 and 0.20 indicate slight agreement, values from 0.21 to 0.40 indicate fair agreement, values from 0.41 to 0.60 indicate moderate agreement, values from 0.61 to 0.80 indicate substantial agreement, and values from 0.81 to 1 indicate almost perfect agreement.( 20 ) Analyses were performed with a bilateral alpha level of 0.05 using SAS software, version 9.4 (SAS Institute, Cary, NC, USA). Results Of the 1376 stroke unit patients admitted between February 2021 and June 2023, 92 had cortical DWI lesion ≤ 10 mm on axial imaging. Of these, 57 patients with 111 acute (64 cerebral and 47 cerebellar) CMI lesions were included for analysis. Flowchart for inclusion is shown in Fig. 1. Clinical and radiological data of included patients are shown in Table 2 . Thirty-four (59.5%) patients had cerebral acute CMI only and 22 (38.5%) cerebellar acute CMI only. One (2%) patient had both cerebral and cerebellar acute CMI, but was subsequently classed as “cerebral” since they had three cerebral and only one cerebellar CMI. Median age was 65, 44% were women, with a median of 1 CMI per patient. Median time between symptom onset and initial MRI was 3 hours [2.00;9.75]. Most frequent stroke etiology was undetermined (44%), followed by cardioembolic (40%). There were no significant differences in clinical or radiological acute MRI characteristics between groups. Table 2 Clinical and radiological characteristics of included patients CMI, cortical microinfarction; IVT, intravenous thrombolysis; EVT, endovascular thrombectomy; CE, cardioembolic; LAA, large artery atherosclerosis Patients All patients, n = 57 Patients with cerebellar CMI, n = 22 Patients with cerebral CMI, n = 35 p value Age, median [q1;q3] (years) 65 [56;75] 67.5 [60.5;77.5] 62 [55;71.5] 0.31 Sex, female 25 (44%) 11 (50%) 14 (40%) 0.58 Hypertension 26 (46%) 13 (59%) 13 (37%) 0.17 Hypercholesterolemia 13 (23%) 4 (18%) 9 (26%) 0.75 Smoking 14 (25%) 4 (18%) 10 (29%) 0.53 Diabetes 11 (19%) 4 (18%) 7 (20%) 1 Stroke history 10 (18%) 2 (9%) 8 (23%) 0.29 Time between symptom onset and initial MRI, median [q1;q3] (hours) 3.00 [2.00;9.75] 3.38 [2.94;10.50] 2.88 [2.00;9.75] 0.35 IVT treatment 8 (14%) 3 (14%) 5 (14%) 1 EVT treatment 1 (2%) 0 (0%) 1 (3%) 1 Acute stroke etiology (TOAST) 0.25 CE 23 (40%) 6 (27%) 17 (49%) LAA 6 (11%) 4 (18%) 2 (6%) Lacunar 0 (0%) 0 (0%) 0 (0%) Other identified 3 (5%) 1 (5%) 2 (6%) Undetermined 25 (44%) 11 (50%) 14 (40%) Initial MRI Multiterritorial infarction 7 (12%) 2 (9%) 5 (14%) 0.69 Time between initial and follow-up MRI, median [q1;q3] (days) 184.00 [181.00;187.00] 184.00 [181.25;187.50] 185.00 [180.00;187.00] 0.99 Number of cerebellar or cerebral CMI per patient, median [q1;q3] 1 [1;2] 1 [1;2] 1 [1;2] 0.78 Number of CMI lesions 0.68 1 32 (56%) 12 (54.5%) 20 (57%) 2 12 (21%) 5 (23%) 7 (20%) 3 4 (7%) 1 (4.5%) 3 (9%) 4 5 (9%) 1 (4.5%) 4 (11%) 5 1 (2%) 1 (4.5%) 0 (0%) 6 3 (5%) 2 (9%) 1 (3%) Comparison of radiological characteristics, including DWI size and initial FLAIR positivity, of acute CMI are shown in Table 3 . Median DWI size of acute CMI was 5.9 mm and initial FLAIR positivity was observed in 70% of acute CMI lesions, without difference between cerebellar and cerebral CMI location (p = 0.28 for DWI size, and p = 0.83 for FLAIR positivity). Table 3 Radiological characteristics of acute CMI and detection rate and size of chronic CMI on FLAIR and T2-weighted imaging CMI, cortical microinfarction; * when p < 0.05 Overall, n = 111 Cerebellar CMI, n = 47 Cerebral CMI, n = 64 p value Initial MRI, acute CMI DWI size, median [q1;q3] (mm) 5.90 [5.00;7.25] 6.00 [5.10;7.90] 5.70 [4.98;7.00] 0.28 FLAIR positivity 78 (70%) 34 (72%) 44 (69%) 0.83 Follow-up MRI at 6 months, chronic CMI FLAIR detection rate 30 (27%) 14 (30%) 16 (25%) 0.67 FLAIR size of FLAIR positive lesions, median [q1;q3] (mm) 4.35 [3.00;6.15] 5.40 [4.40;6.90] 3.45 [2.55;4.03] 0.010* T2 detection rate 49 (44%) 34 (72%) 15 (23%) < 0.001* T2 size of T2 positive lesions, median [q1;q3] (mm) 3.70 [2.00;5.30] 4.65 [2.88;5.73] 2.10 [1.90;2.60] 0.003* For chronic CMI, data on CMI size and detection rates on FLAIR and T2 are shown in Table 3 . At 6 months, 30 (27%) of the initial CMI were detected using FLAIR, and 49 (44%) with T2. These chronic CMIs were larger for cerebellar compared with cerebral location (FLAIR, median 5.40 vs. 3.45 mm, respectively, p = 0.010; T2, median 4.65 vs. 2.10 mm, respectively, p = 0.003). Detection of chronic CMI on FLAIR and T2 was similar for cerebral lesions (p = 0.81), whereas for cerebellar chronic CMI T2 showed a 2.4-fold higher detection rate than FLAIR (p < 0.0001). For chronic CMI detection, inter-rater reliability was fair for FLAIR and substantial for T2 imaging (Cohen’s kappa coefficient 0.32 and 0.72, respectively). Supplemental Fig. 1 shows an example of cerebellar acute CMI, remaining at 6 months in chronic form, whilst Supplemental Fig. 2 shows a cerebral acute CMI, with no signal change on follow-up MRI. Data and results of univariate analyses on clinical and radiological characteristics for chronic CMI detection on FLAIR and T2 are shown in Tables 4 and 5 , respectively. Table 4 Univariate analysis on clinical and radiological characteristics of chronic CMI detection on FLAIR CMI, cortical microinfarction; OR, odds ratio: CI, confidence interval; IVT, intravenous thrombolysis; CE, cardioembolic; LAA, large artery atherosclerosis; * when p < 0.05 Chronic CMI, not detected (n = 81) Chronic CMI, detected (n = 30) OR 95% CI p value CMI, cerebellar location 33 (41%) 14 (47%) 1.27 [0.47;3.39] 0.63 Age, median [q1;q3] (years) 66.00 [60.00;75.00] 71.50 [61.25;79.75] 1.03 [0.99;1.07] 0.11 Sex, female 35 (43%) 11 (37%) 1.54 [0.55;4.28] 0.4 Hypertension 33 (41%) 18 (60%) 2.20 [0.82;5.85] 0.11 Diabetes 16 (20%) 5 (17%) 0.93 [0.26;3.30] 0.91 Hypercholesterolemia 14 (17%) 6 (20%) 1.36 [0.40;4.60] 0.62 Smoking 30 (28%) 7 (23%) 1.36 [0.43;4.30] 0.6 Stroke history 18 (22%) 6 (20%) 0.96 [0.28;3.25] 0.95 Acute CMI DWI size, median [q1;q3] (mm) 5.70 [4.80;6.90] 6.60 [5.48;8.53] 1.39 [1.06;1.81] 0.016* Multiterritorial infarction 16 (20%) 3 (10%) 0.48 [0.11;2.08] 0.32 Acute CMI FLAIR positivity 50 (62%) 28 (93%) 9.28 [1.91;44.96] 0.0065* Acute CMI FLAIR size when FLAIR positive 5.50 [4.50;6.38] 6.40 [3.95;8.70] 1.27 [0.97;1.66] 0.078 Time between symptom onset and initial MRI, median [q1;q3] (hours) 3.75 [2.00;7.00] 7.00 [3.00;48.00] 1.02 [1.00;1.04] 0.078 Time between initial and follow-up MRI, median [q1;q3] (days) 184.00 [181.00;187.00] 182.00 [180.00;187.50] 0.97 [0.92;1.02] 0.21 IVT treatment 10 (12%) 1 (3.3%) 0.24 [0.03;2.25] 0.21 Acute stroke etiology (TOAST) vs. CE etiology 0.0036* LAA 3 (3.7%) 8 (27%) 6.79 [1.46;31.45] Other identified 4 (4.9%) 4 (13%) 2.54 [0.52;12.44] Undetermined 46 (57%) 7 (23%) 0.39 [0.13;1.14] Table 5 Univariate analysis on clinical and radiological characteristics of chronic CMI detection on T2-weighted imaging Chronic CMI, not detected (n = 62) Chronic CMI, detected (n = 49) OR 95% CI p value CMI, cerebellar location 49 (79%) 15 (31%) 8.88 [3.46;22.79] < 0.0001* Age, median [q1;q3] (years) 66.00 [59.00;75.00] 68.00 [60.00;79.00] 1.01 [0.97;1.04] 0.69 Sex, female 27 (44%) 19 (39%) 1.32 [0.49;3.59] 0.57 Hypertension 20 (32%) 31 (63%) 3.59 [1.41;9.10] 0.008* Diabetes 11 (18%) 10 (20%) 1.21 [0.35;4.19] 0.76 Hypercholesterolemia 9 (15%) 11 (22%) 1.72 [0.51;5.82] 0.38 Smoking 14 (23%) 10 (20%) 2.07 [0.66;6.56] 0.21 Stroke history 14 (23%) 10 (20%) 0.77 [0.22;2.67] 0.68 Acute CMI DWI size, median [q1;q3] (mm) 5.65 [4.50;7.00] 6.10 [5.40; 8.00] 1.22 [0.96;1.56] 0.10 Multiterritorial infarction 11 (18%) 8 (16%) 0.95 [0.25;3.67] 0.94 Acute CMI FLAIR positivity 41 (66%) 37 (76%) 1.53 [0.59;3.98] 0.37 Time between symptom onset and initial MRI, median [q1;q3] (hours) 3.38 [2.00;16.00] 4.50 [2.94;27.50] 1.01 [0.99;1.04] 0.24 Time between initial and follow-up MRI, median [q1;q3] (days) 184.00 [181.00;187.00] 184.00 [180.00;189.00] 0.98 [-0.06;0.02] 0.25 IVT treatment 7 (11%) 4 (8%) 0.68 [0.14;3.24] 0.62 Acute stroke etiology (TOAST) vs. CE etiology 0.23 LAA 2 (3%) 9 (18%) 5.95 [0.82;43.44] Other identified 5 (8%) 3 (6%) 0.82 [0.19;6.22] Undetermined 33 (53%) 20 (41%) 0.77 [0.26;2.26] CMI, cortical microinfarction; OR, odds ratio: CI, confidence interval; IVT, intravenous thrombolysis; CE, cardioembolic; LAA, large artery atherosclerosis; * when p < 0.05 Univariate analysis using a logistic mixed model for FLAIR detection of chronic CMI was associated with larger acute CMI size on DWI (p = 0.016), initial FLAIR positivity of acute CMI (p = 0.0065), and stroke etiology (p = 0.0036), Detection rate of chronic CMI on FLAIR was similar between both CMI locations (p = 0.63). Univariate analysis using a logistic mixed model for T2 detection of chronic CMI was associated with cerebellar CMI location (p < 0.0001, OR = 8.88, 95% CI = 3.46–22.79) and history of hypertension (p = 0.008). Multivariate analysis confirmed the strong association between cerebellar location and T2 detection of chronic CMI (p < 0.0001, AOR = 7.25, 95% CI = 2.83–18.56) adjusted for history of hypertension (representing the only variable with a p value < 10% on univariate analysis). The association remained consistent (AOR = 8.58, 95% CI = 3.39–21.67) after adjustment for acute CMI DWI size (representing the only parameter approaching p value < 10% on univariate analysis). Chronic CMI detection blinded to initial MRI was very low for both FLAIR (5%) and T2-weighted imaging (8%), with similar detection rates for cerebellar and cerebral lesions on FLAIR (6% and 3%, respectively, p = 0.65), whereas T2 imaging detected more cerebellar than cerebral lesions (15% and 3%, respectively, p = 0.035). Discussion In our 1.5T MRI study, T2-weighted appeared to be better suited than FLAIR for chronic cerebellar CMI detection. On multivariate mixed logistic regression analysis cerebellar CMI location was the only parameter among the assessed clinical and radiological characteristics associated with chronic CMI detection on T2-weighted imaging. Detection rates for chronic CMI in our study were higher than previously reported for FLAIR and T2-weighted imaging.( 12 ) ( 14 ) In these earlier studies, a 3T magnet was used for both acute and follow-up MRI, probably influencing the detection (and inclusion in the study) of both acute and chronic CMI.( 21 ) Higher field strength MRI has been shown to have slightly lower accuracy acute in stroke diagnosis on DWI.( 21 ) ( 7 ) In a Dutch study assessing acute CMI in 7 patients, only very small (< 5 mm) acute DWI lesions were included, probably explaining the absence of chronic CMI detection on all sequences.( 14 ) The Japanese study reporting on chronic CMI detection in 25 patients had a very long (i.e. 33 months) and variable median time between acute and follow-up MRI, potentially leading to a low detection rate of chronic CMI.( 12 ) A major reason for higher detection rates for chronic CMI in our study, especially on T2, was likely the fact that cerebellar CMI were also included, in contrast with the earlier studies. Indeed, the highest chronic CMI detection rates on T2 imaging in our study were observed for cerebellar locations of CMI. Stroke of cardioembolic origin is associated with the presence of CMI.( 22 ) ( 23 ) ( 24 ) Our group earlier reported that the presence of chronic, relatively small cerebellar cortical infarctions was associated with acute stroke of cardioembolic origin.( 15 ) ( 16 ) However, large studies have shown that acute cardioembolic infarctions were similarly distributed between the anterior and posterior brain circulation.( 18 ) Our current study showed that chronic CMI seemed to be better detected when located in the cerebellum as compared to the neocortex. Thus, the association between presence of relatively small chronic cerebellar cortical infarctions and cardioembolic stroke is probably related to earlier chronic asymptomatic cardioembolic infarctions, more easily detected in the cerebellum than in the neocortex on follow-up MRI. Since cardioembolic infarcts similarly involve the anterior and the posterior circulation, these visible chronic cerebellar CMI probably are proof of more widespread chronic CMI, also involving the cerebral neocortex, less or not visible on MRI. The cerebellar and cerebral cortex show opposing characteristics, both in vivo and ex vivo. The cerebellum represents approximately one-eighth of the volume of the cerebral cortex, while the surfaces of both brain structures are similar, explaining the higher degree of folding of the cerebellar cortex.( 25 ) In addition, the cortical thickness of the cerebellum is thinner (varying between 0.5 and 1.0 mm) than the cerebral neocortex (varying between 1.5 and 4.5 mm), and the internal architecture of the cortex varies substantially between both brain structures.( 26 ) ( 27 ) ( 28 ) We hypothesize that these acute cerebellar, so-called cortical, microinfarctions of < 10 mm involve cortical and subcortical areas of several cerebellar folia, making detection in the chronic phase on T2 imaging more feasible. Indeed, these cerebellar chronic CMI show an ovoid or streak-like form, obliquely orientated to the cerebellar folia, each divided by several CSF spaces in case of involvement of multiple cerebellar folia. In contrast, cerebral CMI are more likely to involve mainly the cortex itself, without CSF space within the ischemic lesion. Limitations of our study were the relatively low patient numbers, and the modest inter-rater reliability of chronic CMI detection, especially on FLAIR. In our study, chronic CMI detection was performed after six months. Detection rates of chronic CMI may differ between different time intervals between initial and follow-up MRI. We used a 1.5T magnet for both acute and chronic CMI assessment. Higher field magnets should be tested to assess possible superiority for both acute and chronic CMI detection compared with 1.5T MRI. Our study showed that detection of acute CMI may not be restricted to high field-strength magnets, although sensitivity and specificity for acute CMI detection on 3T and 7T MRI might be higher compared with 1.5T MRI. Compared to FLAIR, T2-weighted imaging seemed to be the optimal MRI sequence for chronic CMI detection, showing the best inter-rater reliability and the highest detection rates, especially for cerebellar CMI. Declarations Acknowledgement We would like to thank Dr. Sarah Kabani (Clinical Research Support Unit (USMR), CHU Nîmes, Univ. Montpellier, Nîmes, France) for editing the manuscript, Dr. Thibault Mura (Clinical Research Support Unit (USMR), CHU Nîmes, Univ. Montpellier, Nîmes, France) for the supervision of methodological and statistical analyses, and Brigitte Lafont for regulatory assistance. Funding: Not applicable Informed Consent: Informed consent was obtained from all individual participants included in the study. Data Availability Statement: Not applicable Conflict of Interest: The authors declare that they have no conflict of interest. Clinical Trial Registration-URL: http://www.clinicaltrials.gov. 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BMC Neurol 19(1):100 Renard D, Ion I, Ricci JE, Mura T, Thouvenot E, Wacongne A (2020) Chronic Small Cortical Cerebellar Infarctions on MRI are Associated with Patent Foramen Ovale in Young Cryptogenic Stroke. Cerebrovasc Dis 49(1):105–109 Pierik R, Algra A, van Dijk E, Erasmus ME, van Gelder IC, Koudstaal PJ et al (2020) Distribution of Cardioembolic Stroke: A Cohort Study. Cerebrovasc Dis 49(1):97–104 Sharobeam A, Churilov L, Parsons M, Donnan GA, Davis SM, Yan B (2020) Patterns of Infarction on MRI in Patients With Acute Ischemic Stroke and Cardio-Embolism: A Systematic Review and Meta-Analysis. Front Neurol 11:606521 Adams HP, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL et al (1993) Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 24(1):35–41 Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174 Rosso C, Drier A, Lacroix D, Mutlu G, Pires C, Lehéricy S et al (2010) Diffusion-weighted MRI in acute stroke within the first 6 hours: 1.5 or 3.0 Tesla? Neurology 74(24):1946–1953 Wang Z, Van Veluw SJ, Wong A, Liu W, Shi L, Yang J et al (2016) Risk Factors and Cognitive Relevance of Cortical Cerebral Microinfarcts in Patients With Ischemic Stroke or Transient Ischemic Attack. Stroke 47(10):2450–2455 Van Veluw SJ, Hilal S, Kuijf HJ, Ikram MK, Xin X, Yeow TB et al (2015) Cortical microinfarcts on 3T MRI: Clinical correlates in memory-clinic patients. Alzheimer’s Dement 11(12):1500–1509 Hilal S, Chai YL, Van Veluw S, Shaik MA, Ikram MK, Venketasubramanian N et al (2017) Association Between Subclinical Cardiac Biomarkers and Clinically Manifest Cardiac Diseases With Cortical Cerebral Microinfarcts. JAMA Neurol 74(4):403 Sereno MI, Diedrichsen J, Tachrount M, Testa-Silva G, d’Arceuil H, De Zeeuw C (2020) The human cerebellum has almost 80% of the surface area of the neocortex. Proc Natl Acad Sci USA 117(32):19538–19543 Priovoulos N, Andersen M, Dumoulin SO, Boer VO, Van Der Zwaag W (2023) High-Resolution Motion-corrected 7.0-T MRI to Derive Morphologic Measures from the Human Cerebellum in Vivo. Radiology 307(2):e220989 Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 97(20):11050–11055 Wagstyl K, Larocque S, Cucurull G, Lepage C, Cohen JP, Bludau S et al (2020) BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices. Kennedy H, editor. PLoS Biol. ;18(4):e3000678 Additional Declarations No competing interests reported. Supplementary Files SupplementalFigure1CerebellarCMI.jpg Legend Supplemental Figure 1 Initial DWI (A) and FLAIR (B) MRI showing acute left cerebellar CMI (arrow). On follow-up MRI, FLAIR (C) shows absence of signal changes, whereas the chronic left cerebellar CMI can be clearly identified as an obliquely-oriented hyperintensity on T2-weighted imaging (D). SupplementalFigure2CerebralCMI.jpg Legend Supplemental Figure 2 Initial DWI (A) and FLAIR (B) MRI showing acute right prerolandic cortical CMI (arrow). Follow-up MRI shows disappearance of signal changes on both FLAIR (C) and T2-weighted imaging (D). Cite Share Download PDF Status: Published Journal Publication published 14 Feb, 2026 Read the published version in Acta Neurologica Belgica → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7091069\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":496215424,\"identity\":\"5f0505be-e2d6-46de-b36c-ee5b1ffa46ed\",\"order_by\":0,\"name\":\"Dimitri Renard\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDCCwwwMBxgYJCAcnooDYPrAA0JaDsC1nDnAwAPSkoBPywEoBgPeNogWBnxa+I5zJx7+uMcin1+6+dmHt/PuyNmLHX4ItMVOTrcBuxbJw7wbDhx4JmE5c84x45lztz0z5pFOMwBqSTY2O4BdiwFYywEJA4MbCcbMvNsOJ/ZIJ4C0HEjcRkiL/Y30z8y8c0Ba0j8Qp8VAIgdoSwNISw5+W8B+OQPUInEjp5hxzrHDxjy3cwoOJBjg9gvf+bObP1QcqDPgn5G+meFNzWE59tnpmz98qLCTw6UFFzAgTfkoGAWjYBSMAlQAAAxBabnLIW91AAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"CHU Nîmes, Univ. Montpellier\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Dimitri\",\"middleName\":\"\",\"lastName\":\"Renard\",\"suffix\":\"\"},{\"id\":496215425,\"identity\":\"3fe1e817-e7ad-4dae-97c9-f3a3fb1ba0ae\",\"order_by\":1,\"name\":\"Yassine Serghine\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"CHU Nîmes, Univ. Montpellier\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yassine\",\"middleName\":\"\",\"lastName\":\"Serghine\",\"suffix\":\"\"},{\"id\":496215426,\"identity\":\"148bcdb4-514a-4de3-a9ac-e896c8007d76\",\"order_by\":2,\"name\":\"Francois Louis Collemiche\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"CHU Montpellier, Univ. Montpellier\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Francois\",\"middleName\":\"Louis\",\"lastName\":\"Collemiche\",\"suffix\":\"\"},{\"id\":496215427,\"identity\":\"9d1a2b58-6ef9-41e9-9cc9-69995abf4736\",\"order_by\":3,\"name\":\"Angelique Parayre\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"CHU Nîmes, Univ. Montpellier\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Angelique\",\"middleName\":\"\",\"lastName\":\"Parayre\",\"suffix\":\"\"},{\"id\":496215428,\"identity\":\"1c6b99f4-5c6b-4c48-9abb-14fe453c8501\",\"order_by\":4,\"name\":\"Anne Wacongne\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"CHU Nîmes, Univ. Montpellier\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Anne\",\"middleName\":\"\",\"lastName\":\"Wacongne\",\"suffix\":\"\"},{\"id\":496215429,\"identity\":\"b9c35cf9-d260-4714-8245-d767120be288\",\"order_by\":5,\"name\":\"Teodora Parvu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"CHU Nîmes, Univ. 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Montpellier\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sabine\",\"middleName\":\"\",\"lastName\":\"Laurent-Chabalier\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-07-10 09:08:24\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7091069/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7091069/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s13760-026-03003-1\",\"type\":\"published\",\"date\":\"2026-02-14T15:58:18+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":88646973,\"identity\":\"f1a0a941-3ed5-4d98-8881-7f3ec5cf0e0b\",\"added_by\":\"auto\",\"created_at\":\"2025-08-08 16:37:10\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":95548,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlow chart for patient inclusion.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1Flowchart.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7091069/v1/68b5f8206fe9d4d0d42bbde5.jpg\"},{\"id\":102786187,\"identity\":\"96aa83da-71b4-41fa-a278-3e1ced69aa14\",\"added_by\":\"auto\",\"created_at\":\"2026-02-16 16:12:08\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":982835,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7091069/v1/79f7aab4-ce6d-4d9e-927c-3cf6a9d5d4d7.pdf\"},{\"id\":88644783,\"identity\":\"e43081bb-409f-421c-a8df-a2313a88005b\",\"added_by\":\"auto\",\"created_at\":\"2025-08-08 16:21:10\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":396522,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eLegend Supplemental Figure 1\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eInitial DWI (A) and FLAIR (B) MRI showing acute left cerebellar CMI (arrow). On follow-up MRI, FLAIR (C) shows absence of signal changes, whereas the chronic left cerebellar CMI can be clearly identified as an obliquely-oriented hyperintensity on T2-weighted imaging (D).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementalFigure1CerebellarCMI.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7091069/v1/77dc9c152bae2ba5a0cc00f2.jpg\"},{\"id\":88645749,\"identity\":\"cc37c28c-69fc-4867-8aeb-cce1e98b033e\",\"added_by\":\"auto\",\"created_at\":\"2025-08-08 16:29:11\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1391463,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eLegend Supplemental Figure 2\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eInitial DWI (A) and FLAIR (B) MRI showing acute right prerolandic cortical CMI (arrow). Follow-up MRI shows disappearance of signal changes on both FLAIR (C) and T2-weighted imaging (D).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementalFigure2CerebralCMI.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7091069/v1/5a28a8e25213005346b39860.jpg\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"\\u003cp\\u003eDetection of chronic cerebellar and cerebral cortical microinfarctions on T2 and FLAIR 1.5T MRI\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eCerebral cortical microinfarctions (CMI) are frequently observed on MRI and histology studies, especially in older patients and those with cognitive dysfunction.(\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) The majority of these studies analyzed chronic supratentorial CMI.\\u003c/p\\u003e\\u003cp\\u003eThe few studies reporting on temporal dynamics of MRI signal of acute cerebral CMI have shown disappearance of signal changes in most patients on follow-up FLAIR and T2-weighted imaging.(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) A retrospective study analyzing follow-up 3T MRI (mean time between acute and follow-up MRI was 33 months, range 0.5–142) in 25 patients with acute CMI defined as ≤ 10 mm on DWI, showed a chronic CMI detection rate of 5% and 16% on FLAIR and T2-weighted imaging, respectively.(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e) Another 3T MRI study including seven patients with acute CMI of \\u0026lt; 5 mm on initial DWI, showed absence of chronic CMI detection after one month in all patients on T1- and T2-weighted and FLAIR imaging.(\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) To the best of our knowledge, there is no data available on detection rate on MRI of chronic cerebellar CMI.\\u003c/p\\u003e\\u003cp\\u003eRecently, our group showed that chronic, relatively small (\\u0026lt; 20 mm) cerebellar cortical infarctions were frequently observed in acute stroke patients, especially in cardioembolic stroke.(\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e) However, in large studies and meta-analyses, posterior circulation infarction is not associated with cardioembolic source of stroke.(\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e) We hypothesized that these frequently observed small chronic cerebellar infarctions in embolic stroke patients might be related to a higher detection rate of cerebellar compared with cerebral small-sized chronic cortical infarctions. In this study, we focused on CMI and aimed to analyze the ability to detect chronic CMI on follow-up 1.5T MRI on FLAIR and T2-weighted imaging, after initial acute cerebral or cerebellar CMI of ≤ 10 mm and to compare both CMI localizations.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003ePatient selection and study population\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003ePatients were prospectively included in our stroke unit (Nîmes University Hospital, France) between February 2021 and June 2023. Inclusion criteria were age ≥ 18 years, initial MRI performed within one week after symptom onset, symptomatic brain infarction confirmed by DWI, and presence of acute cerebral and/or cerebellar CMI. The study was approved by the local ethics review board (University Hospital Nîmes, IRB number 23.12.01).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eNeuroimaging acquisition and analysis\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eInitial and follow-up MRI were performed with the same 1.5T MRI (Siemens Healthineers Magnetom Sola, Siemens, Erlangen, Germany), using the same parameters for each sequence. Radiological parameters used for the different sequences are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. The picture archiving and communication system available for clinicians (PACS, XERO® Viewer, Agfa HealthCare) was used for viewing and analyses of the initial and follow-up MRI.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eRadiological parameters used for the different MRI sequences\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003c/colgroup\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDWI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eT2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eFLAIR\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTR (ms)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3190\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3490\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7000\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTE (ms)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e71\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e109\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e124\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBandwidth (Hz per pixel)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e919\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e130\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e130\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFlip angle\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e150\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e144\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e150\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSection thickness (mm)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMatrix size\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e272 x 272\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e348 x 348\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e320 x 320\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFOV (mm)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e230\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e240\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e230\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcquisition time (sec)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e72\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e69\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e126\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNEX\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eTR, repetition time; TE, echo time; FOV, field-of-view; NEX, number of excitations\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eInitial MRI included axial DWI, FLAIR, and T2*-weighted images. To avoid hemorrhagic lesions (e.g. cerebral microbleed, hemorrhagic infarction) causing signal changes on DWI related to the susceptibility effect of these hemorrhagic lesions, DWI lesions were excluded in case of hypointensity on T2*-weighted imaging (performed in all patients) inside or nearby (\\u0026lt; 10 mm) the DWI lesion on initial MRI. To minimize possible topographical interpretation errors on follow-up MRI, only acute CMI were included when located \\u0026gt; 10 mm from any other acute DWI or chronic FLAIR lesion. Acute cerebral and cerebellar CMI on initial MRI was defined as a cortical DWI lesion of ≤ 10 mm with \\u0026gt; 50% of the lesion involving the cortex. Initial DWI size screening was performed using axial DWI images. For lesions ≤ 10 mm on axial DWI, multiplanar reconstruction DWI images were generated and DWI lesions \\u0026gt; 10 mm on coronal or sagittal views were excluded.\\u003c/p\\u003e\\u003cp\\u003eFollow-up MRI was scheduled six months after initial MRI. Signal changes in the exact location of the initial acute CMI were sought on these follow-up MRI assessments, using axial FLAIR and T2-weighted sequences, unblinded to initial MRI. On each sequence of follow-up MRI, each acute CMI visible on initial DWI MRI was scored present or absent and the size was measured. Follow-up MRI images were evaluated alongside the initial MRI in order to explore the same brain area involved on initial DWI. Raters were free to adjust contrast and brightness and to zoom the images in order to assess the presence or absence of signal changes on follow-up MRI. Flowchart for patient inclusion and follow-up is shown in Fig.\\u0026nbsp;1.\\u003c/p\\u003e\\u003cp\\u003eRadiological assessment was performed by one rater (DR, with 24 years’ experience in neuroradiology). To assess the inter-rater reliability for chronic CMI detection, a randomly chosen subset of 15 patients with 31 acute CMI lesions were independently assessed by another rater (YS, with 7 years’ experience in neuroradiology) using the same methods.\\u003c/p\\u003e\\u003cp\\u003eAn additional analysis was performed by a third rater (FLC, with 10 years’ experience in neuroradiology) assessing the presence of chronic cerebral or cerebellar CMI on follow-up MRI in all patients, blinded to initial imaging, using the same methods. The only information this rater received was that all patients had one or more acute cerebral and/or cerebellar CMI (≤ 10 mm and \\u0026gt; 50% of the lesion involving the cortex) on DWI six months earlier, located \\u0026gt; 10 mm away from any other lesion. They were given no information about the number of CMI per patient.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eClinical and radiological data\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe patients’ clinical and radiological characteristics recorded included: age, sex, cardiovascular risk factors, time interval between symptom onset and initial MRI performance, acute stroke treatment (i.e. intravenous thrombolysis or endovascular thrombectomy), stroke etiology (according to the TOAST classification), uni- or multiterritorial acute infarction on initial MRI, acute CMI characteristics (number of acute CMI per patient, axial DWI size, and initial FLAIR positivity), time interval between initial and follow-up MRI, and chronic CMI size when detected on FLAIR or T2-weighted imaging.(\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStatistics\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003ePatient clinical and MRI data were described using median and interquartile range (IQR) for quantitative variables, and numbers and percentages for qualitative variables. Patients were classified into groups according to location (i.e. cerebellar or cerebral CMI). For patients with lesions in both locations, they were assigned to the group for which they had the most frequent lesions. The clinical and radiological acute and chronic CMI characteristics were compared between groups using the Chi2 test for qualitative variables and the Wilcoxon-Mann-Whitney test for quantitative variables.\\u003c/p\\u003e\\u003cp\\u003eTo avoid potential confounding bias and possible intra-individual correlation induced by multiple lesions in the same patient, univariate/multivariate mixed logistic regression models with patient-specific random intercept were performed to analyze the effect of location on FLAIR and T2 detection rate at six months. The McNemar test was used to compare detection on FLAIR and T2 according to lesion type. In the multivariate model, only variables with a p value of less than 10% were retained in the model. A specific model adjusting for initial lesion size was also performed to ensure that there was no confounding bias at this level.\\u003c/p\\u003e\\u003cp\\u003eInter-rater reliability was assessed using Kappa coefficient. The interpretation of the Kappa coefficient, according to Landis and Koch (1977), was as follows: values below 0 indicate poor agreement, values between 0 and 0.20 indicate slight agreement, values from 0.21 to 0.40 indicate fair agreement, values from 0.41 to 0.60 indicate moderate agreement, values from 0.61 to 0.80 indicate substantial agreement, and values from 0.81 to 1 indicate almost perfect agreement.(\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003eAnalyses were performed with a bilateral alpha level of 0.05 using SAS software, version 9.4 (SAS Institute, Cary, NC, USA).\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eOf the 1376 stroke unit patients admitted between February 2021 and June 2023, 92 had cortical DWI lesion\\u0026thinsp;\\u0026le;\\u0026thinsp;10 mm on axial imaging. Of these, 57 patients with 111 acute (64 cerebral and 47 cerebellar) CMI lesions were included for analysis. Flowchart for inclusion is shown in Fig.\\u0026nbsp;1.\\u003c/p\\u003e\\u003cp\\u003eClinical and radiological data of included patients are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Thirty-four (59.5%) patients had cerebral acute CMI only and 22 (38.5%) cerebellar acute CMI only. One (2%) patient had both cerebral and cerebellar acute CMI, but was subsequently classed as \\u0026ldquo;cerebral\\u0026rdquo; since they had three cerebral and only one cerebellar CMI. Median age was 65, 44% were women, with a median of 1 CMI per patient. Median time between symptom onset and initial MRI was 3 hours [2.00;9.75]. Most frequent stroke etiology was undetermined (44%), followed by cardioembolic (40%). There were no significant differences in clinical or radiological acute MRI characteristics between groups.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eClinical and radiological characteristics of included patients CMI, cortical microinfarction; IVT, intravenous thrombolysis; EVT, endovascular thrombectomy; CE, cardioembolic; LAA, large artery atherosclerosis\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePatients\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eAll patients, n\\u0026thinsp;=\\u0026thinsp;57\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePatients with cerebellar CMI, n\\u0026thinsp;=\\u0026thinsp;22\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ePatients with cerebral CMI, n\\u0026thinsp;=\\u0026thinsp;35\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ep value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge, median [q1;q3] (years)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e65 [56;75]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e67.5 [60.5;77.5]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e62 [55;71.5]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.31\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex, female\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e25 (44%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11 (50%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14 (40%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypertension\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e26 (46%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13 (59%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e13 (37%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.17\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypercholesterolemia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e13 (23%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4 (18%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e9 (26%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.75\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmoking\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14 (25%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4 (18%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e10 (29%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.53\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11 (19%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4 (18%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7 (20%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStroke history\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10 (18%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2 (9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8 (23%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTime between symptom onset and initial MRI, median [q1;q3] (hours)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.00 [2.00;9.75]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.38 [2.94;10.50]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.88 [2.00;9.75]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.35\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIVT treatment\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8 (14%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3 (14%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5 (14%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEVT treatment\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1 (2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1 (3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcute stroke etiology (TOAST)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.25\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCE\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e23 (40%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6 (27%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17 (49%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLAA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6 (11%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4 (18%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2 (6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLacunar\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOther identified\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3 (5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1 (5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2 (6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUndetermined\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e25 (44%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11 (50%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14 (40%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eInitial MRI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMultiterritorial infarction\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7 (12%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2 (9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5 (14%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.69\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTime between initial and follow-up MRI,\\u003c/p\\u003e\\u003cp\\u003emedian [q1;q3] (days)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e184.00 [181.00;187.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e184.00 [181.25;187.50]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e185.00 [180.00;187.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNumber of cerebellar or cerebral CMI per patient, median [q1;q3]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1 [1;2]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1 [1;2]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1 [1;2]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNumber of CMI lesions\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e32 (56%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e12 (54.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e20 (57%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e12 (21%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5 (23%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7 (20%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4 (7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1 (4.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3 (9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5 (9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1 (4.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4 (11%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1 (2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1 (4.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0 (0%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3 (5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2 (9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1 (3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eComparison of radiological characteristics, including DWI size and initial FLAIR positivity, of acute CMI are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. Median DWI size of acute CMI was 5.9 mm and initial FLAIR positivity was observed in 70% of acute CMI lesions, without difference between cerebellar and cerebral CMI location (p\\u0026thinsp;=\\u0026thinsp;0.28 for DWI size, and p\\u0026thinsp;=\\u0026thinsp;0.83 for FLAIR positivity).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eRadiological characteristics of acute CMI and detection rate and size of chronic CMI on FLAIR and T2-weighted imaging CMI, cortical microinfarction; * when p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eOverall, n\\u0026thinsp;=\\u0026thinsp;111\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eCerebellar CMI, n\\u0026thinsp;=\\u0026thinsp;47\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eCerebral CMI, n\\u0026thinsp;=\\u0026thinsp;64\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ep value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eInitial MRI, acute CMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDWI size, median [q1;q3] (mm)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.90 [5.00;7.25]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.00 [5.10;7.90]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.70 [4.98;7.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.28\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFLAIR positivity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e78 (70%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e34 (72%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e44 (69%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.83\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFollow-up MRI at 6 months, chronic CMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFLAIR detection rate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e30 (27%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e14 (30%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e16 (25%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFLAIR size of FLAIR positive lesions, median [q1;q3] (mm)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.35 [3.00;6.15]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.40 [4.40;6.90]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.45 [2.55;4.03]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.010*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT2 detection rate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e49 (44%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e34 (72%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e15 (23%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT2 size of T2 positive lesions, median [q1;q3] (mm)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.70 [2.00;5.30]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.65 [2.88;5.73]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.10 [1.90;2.60]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.003*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eFor chronic CMI, data on CMI size and detection rates on FLAIR and T2 are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. At 6 months, 30 (27%) of the initial CMI were detected using FLAIR, and 49 (44%) with T2. These chronic CMIs were larger for cerebellar compared with cerebral location (FLAIR, median 5.40 vs. 3.45 mm, respectively, p\\u0026thinsp;=\\u0026thinsp;0.010; T2, median 4.65 vs. 2.10 mm, respectively, p\\u0026thinsp;=\\u0026thinsp;0.003). Detection of chronic CMI on FLAIR and T2 was similar for cerebral lesions (p\\u0026thinsp;=\\u0026thinsp;0.81), whereas for cerebellar chronic CMI T2 showed a 2.4-fold higher detection rate than FLAIR (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001).\\u003c/p\\u003e\\u003cp\\u003eFor chronic CMI detection, inter-rater reliability was fair for FLAIR and substantial for T2 imaging (Cohen\\u0026rsquo;s kappa coefficient 0.32 and 0.72, respectively).\\u003c/p\\u003e\\u003cp\\u003eSupplemental Fig.\\u0026nbsp;1 shows an example of cerebellar acute CMI, remaining at 6 months in chronic form, whilst Supplemental Fig.\\u0026nbsp;2 shows a cerebral acute CMI, with no signal change on follow-up MRI.\\u003c/p\\u003e\\u003cp\\u003eData and results of univariate analyses on clinical and radiological characteristics for chronic CMI detection on FLAIR and T2 are shown in Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, respectively.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eUnivariate analysis on clinical and radiological characteristics of chronic CMI detection on FLAIR CMI, cortical microinfarction; OR, odds ratio: CI, confidence interval; IVT, intravenous thrombolysis; CE, cardioembolic; LAA, large artery atherosclerosis; * when p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eChronic CMI, not detected (n\\u0026thinsp;=\\u0026thinsp;81)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eChronic CMI, detected (n\\u0026thinsp;=\\u0026thinsp;30)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e95% CI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003ep value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCMI, cerebellar location\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e33 (41%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e14 (47%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.47;3.39]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.63\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge, median [q1;q3] (years)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e66.00 [60.00;75.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e71.50 [61.25;79.75]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.99;1.07]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.11\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex, female\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e35 (43%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11 (37%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.55;4.28]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypertension\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e33 (41%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18 (60%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.82;5.85]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.11\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e16 (20%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5 (17%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.26;3.30]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.91\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypercholesterolemia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14 (17%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6 (20%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.40;4.60]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmoking\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e30 (28%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7 (23%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.43;4.30]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStroke history\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e18 (22%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6 (20%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.28;3.25]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.95\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcute CMI DWI size, median [q1;q3] (mm)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.70 [4.80;6.90]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.60 [5.48;8.53]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[1.06;1.81]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.016*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMultiterritorial infarction\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e16 (20%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3 (10%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.48\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.11;2.08]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.32\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcute CMI FLAIR positivity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e50 (62%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e28 (93%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e9.28\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[1.91;44.96]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.0065*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcute CMI FLAIR size when FLAIR positive\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.50 [4.50;6.38]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.40 [3.95;8.70]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.97;1.66]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.078\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTime between symptom onset and initial MRI, median [q1;q3] (hours)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.75 [2.00;7.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7.00 [3.00;48.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[1.00;1.04]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.078\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTime between initial and follow-up MRI, median [q1;q3] (days)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e184.00 [181.00;187.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e182.00 [180.00;187.50]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.92;1.02]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIVT treatment\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10 (12%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1 (3.3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.03;2.25]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcute stroke etiology (TOAST) vs. CE etiology\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e0.0036*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLAA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3 (3.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8 (27%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.79\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[1.46;31.45]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOther identified\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4 (4.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4 (13%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.52;12.44]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUndetermined\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e46 (57%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7 (23%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.13;1.14]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eUnivariate analysis on clinical and radiological characteristics of chronic CMI detection on T2-weighted imaging\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eChronic CMI, not detected (n\\u0026thinsp;=\\u0026thinsp;62)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eChronic CMI, detected (n\\u0026thinsp;=\\u0026thinsp;49)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e95% CI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003ep value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCMI, cerebellar location\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e49 (79%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e15 (31%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8.88\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[3.46;22.79]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge, median [q1;q3] (years)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e66.00 [59.00;75.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e68.00 [60.00;79.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.97;1.04]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.69\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex, female\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e27 (44%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e19 (39%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.32\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.49;3.59]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.57\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypertension\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e20 (32%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e31 (63%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[1.41;9.10]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.008*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11 (18%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e10 (20%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.35;4.19]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.76\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypercholesterolemia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e9 (15%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11 (22%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.72\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.51;5.82]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.38\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmoking\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14 (23%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e10 (20%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.66;6.56]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStroke history\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14 (23%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e10 (20%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.22;2.67]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcute CMI DWI size, median [q1;q3] (mm)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.65 [4.50;7.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.10 [5.40; 8.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.96;1.56]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMultiterritorial infarction\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11 (18%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8 (16%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.95\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.25;3.67]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.94\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcute CMI FLAIR positivity\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e41 (66%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e37 (76%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.53\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.59;3.98]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTime between symptom onset and initial MRI, median [q1;q3] (hours)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.38 [2.00;16.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.50 [2.94;27.50]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.99;1.04]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.24\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTime between initial and follow-up MRI, median [q1;q3] (days)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e184.00 [181.00;187.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e184.00 [180.00;189.00]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[-0.06;0.02]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.25\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIVT treatment\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7 (11%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4 (8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.14;3.24]\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAcute stroke etiology (TOAST) vs. CE etiology\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e0.23\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLAA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2 (3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e9 (18%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.95\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.82;43.44]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOther identified\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5 (8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3 (6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.19;6.22]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUndetermined\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e33 (53%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20 (41%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e[0.26;2.26]\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eCMI, cortical microinfarction; OR, odds ratio: CI, confidence interval; IVT, intravenous thrombolysis; CE, cardioembolic; LAA, large artery atherosclerosis; * when p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eUnivariate analysis using a logistic mixed model for FLAIR detection of chronic CMI was associated with larger acute CMI size on DWI (p\\u0026thinsp;=\\u0026thinsp;0.016), initial FLAIR positivity of acute CMI (p\\u0026thinsp;=\\u0026thinsp;0.0065), and stroke etiology (p\\u0026thinsp;=\\u0026thinsp;0.0036), Detection rate of chronic CMI on FLAIR was similar between both CMI locations (p\\u0026thinsp;=\\u0026thinsp;0.63).\\u003c/p\\u003e\\u003cp\\u003eUnivariate analysis using a logistic mixed model for T2 detection of chronic CMI was associated with cerebellar CMI location (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001, OR\\u0026thinsp;=\\u0026thinsp;8.88, 95% CI\\u0026thinsp;=\\u0026thinsp;3.46\\u0026ndash;22.79) and history of hypertension (p\\u0026thinsp;=\\u0026thinsp;0.008).\\u003c/p\\u003e\\u003cp\\u003eMultivariate analysis confirmed the strong association between cerebellar location and T2 detection of chronic CMI (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001, AOR\\u0026thinsp;=\\u0026thinsp;7.25, 95% CI\\u0026thinsp;=\\u0026thinsp;2.83\\u0026ndash;18.56) adjusted for history of hypertension (representing the only variable with a p value\\u0026thinsp;\\u0026lt;\\u0026thinsp;10% on univariate analysis). The association remained consistent (AOR\\u0026thinsp;=\\u0026thinsp;8.58, 95% CI\\u0026thinsp;=\\u0026thinsp;3.39\\u0026ndash;21.67) after adjustment for acute CMI DWI size (representing the only parameter approaching p value\\u0026thinsp;\\u0026lt;\\u0026thinsp;10% on univariate analysis).\\u003c/p\\u003e\\u003cp\\u003eChronic CMI detection blinded to initial MRI was very low for both FLAIR (5%) and T2-weighted imaging (8%), with similar detection rates for cerebellar and cerebral lesions on FLAIR (6% and 3%, respectively, p\\u0026thinsp;=\\u0026thinsp;0.65), whereas T2 imaging detected more cerebellar than cerebral lesions (15% and 3%, respectively, p\\u0026thinsp;=\\u0026thinsp;0.035).\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn our 1.5T MRI study, T2-weighted appeared to be better suited than FLAIR for chronic cerebellar CMI detection. On multivariate mixed logistic regression analysis cerebellar CMI location was the only parameter among the assessed clinical and radiological characteristics associated with chronic CMI detection on T2-weighted imaging.\\u003c/p\\u003e\\u003cp\\u003eDetection rates for chronic CMI in our study were higher than previously reported for FLAIR and T2-weighted imaging.(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) In these earlier studies, a 3T magnet was used for both acute and follow-up MRI, probably influencing the detection (and inclusion in the study) of both acute and chronic CMI.(\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e) Higher field strength MRI has been shown to have slightly lower accuracy acute in stroke diagnosis on DWI.(\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e) In a Dutch study assessing acute CMI in 7 patients, only very small (\\u0026lt;\\u0026thinsp;5 mm) acute DWI lesions were included, probably explaining the absence of chronic CMI detection on all sequences.(\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) The Japanese study reporting on chronic CMI detection in 25 patients had a very long (i.e. 33 months) and variable median time between acute and follow-up MRI, potentially leading to a low detection rate of chronic CMI.(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e) A major reason for higher detection rates for chronic CMI in our study, especially on T2, was likely the fact that cerebellar CMI were also included, in contrast with the earlier studies. Indeed, the highest chronic CMI detection rates on T2 imaging in our study were observed for cerebellar locations of CMI.\\u003c/p\\u003e\\u003cp\\u003eStroke of cardioembolic origin is associated with the presence of CMI.(\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e) Our group earlier reported that the presence of chronic, relatively small cerebellar cortical infarctions was associated with acute stroke of cardioembolic origin.(\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e) However, large studies have shown that acute cardioembolic infarctions were similarly distributed between the anterior and posterior brain circulation.(\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e) Our current study showed that chronic CMI seemed to be better detected when located in the cerebellum as compared to the neocortex. Thus, the association between presence of relatively small chronic cerebellar cortical infarctions and cardioembolic stroke is probably related to earlier chronic asymptomatic cardioembolic infarctions, more easily detected in the cerebellum than in the neocortex on follow-up MRI. Since cardioembolic infarcts similarly involve the anterior and the posterior circulation, these visible chronic cerebellar CMI probably are proof of more widespread chronic CMI, also involving the cerebral neocortex, less or not visible on MRI.\\u003c/p\\u003e\\u003cp\\u003eThe cerebellar and cerebral cortex show opposing characteristics, both in vivo and ex vivo. The cerebellum represents approximately one-eighth of the volume of the cerebral cortex, while the surfaces of both brain structures are similar, explaining the higher degree of folding of the cerebellar cortex.(\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e) In addition, the cortical thickness of the cerebellum is thinner (varying between 0.5 and 1.0 mm) than the cerebral neocortex (varying between 1.5 and 4.5 mm), and the internal architecture of the cortex varies substantially between both brain structures.(\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e) We hypothesize that these acute cerebellar, so-called cortical, microinfarctions of \\u0026lt;\\u0026thinsp;10 mm involve cortical and subcortical areas of several cerebellar folia, making detection in the chronic phase on T2 imaging more feasible. Indeed, these cerebellar chronic CMI show an ovoid or streak-like form, obliquely orientated to the cerebellar folia, each divided by several CSF spaces in case of involvement of multiple cerebellar folia. In contrast, cerebral CMI are more likely to involve mainly the cortex itself, without CSF space within the ischemic lesion.\\u003c/p\\u003e\\u003cp\\u003e Limitations of our study were the relatively low patient numbers, and the modest inter-rater reliability of chronic CMI detection, especially on FLAIR. In our study, chronic CMI detection was performed after six months. Detection rates of chronic CMI may differ between different time intervals between initial and follow-up MRI. We used a 1.5T magnet for both acute and chronic CMI assessment. Higher field magnets should be tested to assess possible superiority for both acute and chronic CMI detection compared with 1.5T MRI.\\u003c/p\\u003e\\u003cp\\u003eOur study showed that detection of acute CMI may not be restricted to high field-strength magnets, although sensitivity and specificity for acute CMI detection on 3T and 7T MRI might be higher compared with 1.5T MRI.\\u003c/p\\u003e\\u003cp\\u003eCompared to FLAIR, T2-weighted imaging seemed to be the optimal MRI sequence for chronic CMI detection, showing the best inter-rater reliability and the highest detection rates, especially for cerebellar CMI.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to thank Dr. Sarah Kabani (Clinical Research Support Unit (USMR), CHU N\\u0026icirc;mes, Univ. Montpellier, N\\u0026icirc;mes, France) for editing the manuscript, Dr. Thibault Mura (Clinical Research Support Unit (USMR), CHU N\\u0026icirc;mes, Univ. Montpellier, N\\u0026icirc;mes, France) for the supervision of methodological and statistical analyses, and Brigitte Lafont for regulatory assistance.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u0026nbsp;\\u003c/strong\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInformed Consent:\\u003c/strong\\u003e Informed consent was obtained from all individual participants included in the study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability Statement:\\u0026nbsp;\\u003c/strong\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of Interest:\\u003c/strong\\u003e The authors declare that they have no conflict of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical Trial Registration-URL:\\u003c/strong\\u003e http://www.clinicaltrials.gov. Unique identifier: NCT06218576\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eBrundel M, de Bresser J, van Dillen JJ, Kappelle LJ, Biessels GJ (2012) Cerebral Microinfarcts: A Systematic Review of Neuropathological Studies. J Cereb Blood Flow Metab 32(3):425\\u0026ndash;436\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSmith EE, Schneider JA, Wardlaw JM, Greenberg SM (2012) Cerebral microinfarcts: the invisible lesions. Lancet Neurol 11(3):272\\u0026ndash;282\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBrundel M, Reijmer YD, van Veluw SJ, Kuijf HJ, Luijten PR, Kappelle LJ et al (2014) Cerebral microvascular lesions on high-resolution 7-Tesla MRI in patients with type 2 diabetes. Diabetes 63(10):3523\\u0026ndash;3529\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eK\\u0026ouml;vari E, Gold G, Herrmann FR, Canuto A, Hof PR, Bouras C et al (2007) Cortical microinfarcts and demyelination affect cognition in cases at high risk for dementia. Neurology 68(12):927\\u0026ndash;931\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eK\\u0026ouml;vari E, Gold G, Herrmann FR, Canuto A, Hof PR, Michel JP et al (2004) Cortical microinfarcts and demyelination significantly affect cognition in brain aging. Stroke 35(2):410\\u0026ndash;414\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003evan Dalen JW, Scuric EEM, van Veluw SJ, Caan MWA, Nederveen AJ, Biessels GJ et al (2015) Cortical microinfarcts detected in vivo on 3 Tesla MRI: clinical and radiological correlates. Stroke 46(1):255\\u0026ndash;257\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAuriel E, Westover MB, Bianchi MT, Reijmer Y, Martinez-Ramirez S, Ni J et al (2015) Estimating Total Cerebral Microinfarct Burden From Diffusion-Weighted Imaging. Stroke 46(8):2129\\u0026ndash;2135\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003evan Veluw SJ, Zwanenburg JJM, Engelen-Lee J, Spliet WGM, Hendrikse J, Luijten PR et al (2013) In vivo detection of cerebral cortical microinfarcts with high-resolution 7T MRI. J Cereb Blood Flow Metab 33(3):322\\u0026ndash;329\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVinters HV, Ellis WG, Zarow C, Zaias BW, Jagust WJ, Mack WJ et al (2000) Neuropathologic Substrates of Ischemic Vascular Dementia. J Neuropathol Exp Neurol 59(11):931\\u0026ndash;945\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSonnen JA, Larson EB, Crane PK, Haneuse S, Li G, Schellenberg GD et al (2007) Pathological correlates of dementia in a longitudinal, population-based sample of aging. Ann Neurol 62(4):406\\u0026ndash;413\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGold G, Kovari E, Hof PR, Bouras C, Giannakopoulos P (2007) Sorting out the clinical consequences of ischemic lesions in brain aging: a clinicopathological approach. J Neurol Sci 257(1\\u0026ndash;2):17\\u0026ndash;22\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMiyata M, Kakeda S, Yoneda T, Ide S, Watanabe K, Moriya J et al (2018) Signal Change of Acute Cortical and Juxtacortical Microinfarction on Follow-Up MRI. AJNR Am J Neuroradiol 39(5):834\\u0026ndash;840\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHilal S, Doolabi A, Vrooman H, Ikram MK, Ikram MA, Vernooij MW (2021) Clinical Relevance of Cortical Cerebral Microinfarcts on 1.5T Magnetic Resonance Imaging in the Late-Adult Population. Stroke 52(3):922\\u0026ndash;930\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTer Telgte A, Wiegertjes K, Gesierich B, Baskaran BS, Marques JP, Kuijf HJ et al (2020) Temporal Dynamics of Cortical Microinfarcts in Cerebral Small Vessel Disease. JAMA Neurol 77(5):643\\u0026ndash;647\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTer Schiphorst A, Tatu L, Thijs V, Demattei C, Thouvenot E, Renard D (2019) Small obliquely oriented cortical cerebellar infarctions are associated with cardioembolic stroke. BMC Neurol 19(1):100\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRenard D, Ion I, Ricci JE, Mura T, Thouvenot E, Wacongne A (2020) Chronic Small Cortical Cerebellar Infarctions on MRI are Associated with Patent Foramen Ovale in Young Cryptogenic Stroke. Cerebrovasc Dis 49(1):105\\u0026ndash;109\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePierik R, Algra A, van Dijk E, Erasmus ME, van Gelder IC, Koudstaal PJ et al (2020) Distribution of Cardioembolic Stroke: A Cohort Study. Cerebrovasc Dis 49(1):97\\u0026ndash;104\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSharobeam A, Churilov L, Parsons M, Donnan GA, Davis SM, Yan B (2020) Patterns of Infarction on MRI in Patients With Acute Ischemic Stroke and Cardio-Embolism: A Systematic Review and Meta-Analysis. Front Neurol 11:606521\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAdams HP, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL et al (1993) Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 24(1):35\\u0026ndash;41\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLandis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159\\u0026ndash;174\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRosso C, Drier A, Lacroix D, Mutlu G, Pires C, Leh\\u0026eacute;ricy S et al (2010) Diffusion-weighted MRI in acute stroke within the first 6 hours: 1.5 or 3.0 Tesla? Neurology 74(24):1946\\u0026ndash;1953\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWang Z, Van Veluw SJ, Wong A, Liu W, Shi L, Yang J et al (2016) Risk Factors and Cognitive Relevance of Cortical Cerebral Microinfarcts in Patients With Ischemic Stroke or Transient Ischemic Attack. Stroke 47(10):2450\\u0026ndash;2455\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVan Veluw SJ, Hilal S, Kuijf HJ, Ikram MK, Xin X, Yeow TB et al (2015) Cortical microinfarcts on 3T MRI: Clinical correlates in memory-clinic patients. Alzheimer\\u0026rsquo;s Dement 11(12):1500\\u0026ndash;1509\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHilal S, Chai YL, Van Veluw S, Shaik MA, Ikram MK, Venketasubramanian N et al (2017) Association Between Subclinical Cardiac Biomarkers and Clinically Manifest Cardiac Diseases With Cortical Cerebral Microinfarcts. JAMA Neurol 74(4):403\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSereno MI, Diedrichsen J, Tachrount M, Testa-Silva G, d\\u0026rsquo;Arceuil H, De Zeeuw C (2020) The human cerebellum has almost 80% of the surface area of the neocortex. Proc Natl Acad Sci USA 117(32):19538\\u0026ndash;19543\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePriovoulos N, Andersen M, Dumoulin SO, Boer VO, Van Der Zwaag W (2023) High-Resolution Motion-corrected 7.0-T MRI to Derive Morphologic Measures from the Human Cerebellum in Vivo. Radiology 307(2):e220989\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 97(20):11050\\u0026ndash;11055\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWagstyl K, Larocque S, Cucurull G, Lepage C, Cohen JP, Bludau S et al (2020) BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices. Kennedy H, editor. PLoS Biol. ;18(4):e3000678\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"cerebellar, cerebral, cortical microinfarction, T2, FLAIR, 1.5T, MRI\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7091069/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7091069/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eBackground:\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e \\u003c/em\\u003eDetection rate of chronic cerebral cortical microinfarctions (CMI) is low. Few data are available on chronic cerebellar CMI. Our aim was to study the ability of 1.5T FLAIR and T2-weighted imaging to detect chronic CMI and compare cerebral and cerebellar CMI location.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e \\u003c/em\\u003eWe prospectively included stroke patients with ≥1 acute cerebral or cerebellar CMI ≤10mm on DWI. Follow-up FLAIR and T2-weighted imaging with the same 1.5T-magnet was performed after six months. Images were evaluated unblinded to initial MRI to detect signal changes at the same location as initial acute CMI. Variables influencing chronic CMI detection were analyzed in uni- and multivariate analyses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eResults: \\u003c/strong\\u003e\\u003c/em\\u003eWe analyzed 57 patients (median age 65, 44% women) with 111 acute CMI on initial MRI. These constituted 47 cerebellar and 64 cerebral acute CMI, with median DWI size of 5.9mm and initial FLAIR positivity of 70%, without difference between cerebellar and cerebral location. On 6-month follow-up MRI, chronic CMI detection on FLAIR and T2 was similar for cerebral lesions (p=0.81), whereas for cerebellar lesions T2 showed a 2.4-fold higher detection rate than FLAIR (p\\u0026lt;0.0001).\\u003c/p\\u003e\\n\\u003cp\\u003eUnivariate analysis using a logistic mixed model for T2 detection of chronic CMI was associated with cerebellar CMI location (p\\u0026lt;0.0001) and hypertension history (p=0.008). Multivariate analysis confirmed the strong association between cerebellar location and T2 detection of chronic CMI (p\\u0026lt;0.0001, AOR=7.25, 95% CI=2.83-18.56) adjusted for hypertension.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eConclusion:\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e \\u003c/strong\\u003eCompared to FLAIR, T2-weighted imaging shows highest detection rates for cerebellar CMI, with cerebellar CMI location as strongest predictor of chronic CMI detection.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Detection of chronic cerebellar and cerebral cortical microinfarctions on T2 and FLAIR 1.5T MRI\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-08-08 16:21:06\",\"doi\":\"10.21203/rs.3.rs-7091069/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"71bbff58-133a-489d-8a53-b8183365c1a2\",\"owner\":[],\"postedDate\":\"August 8th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-02-16T16:08:28+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7091069\",\"link\":\"https://doi.org/10.1007/s13760-026-03003-1\",\"journal\":{\"identity\":\"acta-neurologica-belgica\",\"isVorOnly\":false,\"title\":\"Acta Neurologica Belgica\"},\"publishedOn\":\"2026-02-14 15:58:18\",\"publishedOnDateReadable\":\"February 14th, 2026\"},\"versionCreatedAt\":\"2025-08-08 16:21:06\",\"video\":\"\",\"vorDoi\":\"10.1007/s13760-026-03003-1\",\"vorDoiUrl\":\"https://doi.org/10.1007/s13760-026-03003-1\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7091069\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7091069\",\"identity\":\"rs-7091069\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}