Comparison of the correlation between cerebral [18F]FDG metabolism as assessed by two asymmetry indices and clinical neurological score in patients with ischemic cerebrovascular disease

Comparison of the correlation between cerebral [18F]FDG metabolism as assessed by two asymmetry indices and clinical neurological score in patients with ischemic cerebrovascular disease | 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 Comparison of the correlation between cerebral [18F]FDG metabolism as assessed by two asymmetry indices and clinical neurological score in patients with ischemic cerebrovascular disease Yuxin Liang, Bixiao Cui, Linlin Ye, Bin Yang, Yi Shan, Hongwei Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5319717/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Molecular Imaging and Biology → Version 1 posted 4 You are reading this latest preprint version Abstract Purpose To identify a method of assessing cerebral 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) metabolism with an asymmetry index (AI) that reflects clinical neurological function in patients with ischemic cerebrovascular disease (ICVD), and to explore its applications and potential value to the ICVD in clinical settings. Procedures: Seventy patients diagnosed with subacute and chronic ischemic stroke were retrospectively analyzed. All patients underwent [ 18 F]FDG PET/MR scans and were assessed using the National Institutes of Health Stroke Scale (NIHSS) and the Modified Rankin Scale (mRS). Following a year of rehabilitation training, nineteen patients underwent a repeat [ 18 F]FDG PET/MR scan. The decreased cerebral [ 18 F]FDG metabolism region was defined as the AI value greater than 10%. Two voxel-wise AIs, designated as AI 1 and AI 2 , were calculated based on the standardized uptake value ratio (SUVR). The decreased metabolism on affected side accessed by different AI calculation methods were compared. The correlations between the decreased metabolism and the clinical scores were analyzed. Results The volume and percentage of decreased [ 18 F]FDG metabolism assessed by AI 2 was larger than that obtained from AI 1 (all p < 0.0001). The correlation coefficients between the clinical scores and the decreased metabolism in temporal and parietal lobes assessed by AI 1 method were all higher than those from AI 2 . In addition, the improved follow-up patients showed more pronounced metabolic improvement as assessed by AI 1 . Conclusions The assessment of cerebral [ 18 F]FDG metabolism in patients with unilateral internal carotid/middle cerebral artery steno-occlusion to reflect clinical neurological function using the AI 1 method demonstrated superior performance in comparison to the AI 2 method. Ischemic cerebrovascular disease Asymmetry index Positron emission tomography Brain glucose Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ischemic cerebrovascular disease (ICVD) is predominantly responsible for the majority of strokes, characterized by progressive steno-occlusive changes at the terminal portion of the internal carotid artery (ICA) or middle cerebral artery (MCA)[ 1 ]. As one of the leading causes of death and disability worldwide, ICVD has posed a huge burden in public health issue of growing importance[ 2 ]. The risk of stroke recurrence among these patients remains high, even with aggressive medical management[ 3 ], which has prompted us to contemplate effective long-term monitoring to control the risk of reinfarction in patients with cerebral infarction. The brain is an organ with high metabolic activity that primarily utilizes glucose as its source of energy. The 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) PET imaging offers valuable insights into cerebral metabolism[ 4 ], which holds great significance in understanding the functional condition[ 5 ]. In contrast to MRI, the hybrid [ 18 F]FDG PET/MR allows for acquisition both of the precise morphological data and the metabolic information[ 6 ]. Quantifying the pathophysiological changes and regional metabolism in the course of ICVD patients is critical for monitoring their clinical outcomes and recovery effects[ 7 , 8 ]. The asymmetry index (AI) is commonly employed for assessing the distribution asymmetry of cerebral metabolism or cerebral blood flow[ 9 ]. Previous studies on functional brain changes in ICVD patients have shown that the AI values for glucose metabolism were significantly lower after effective treatment [ 10 ]. Van Niftrik CHB et al.[ 11 ]using an AI method demonstrated a robust association between the degeneration after stroke and the ipsilateral thalamic diaschisis, while Zhu Y et al.[ 12 ]found the metabolic assessment of another AI method was correlated with the predisposing factors in ICVD. However, the results of different AI methods may yield disparate insights into the clinical neurologic status of a given subject. It remains a question worthy of further exploration as to which AI computational approach is better suited to assessing decreased [ 18 F]FDG metabolism and facilitating comprehension of alterations in disease state among ICVD patients. The objective was to identify the AI method for more superior performance of capturing the decreased [ 18 F]FDG metabolism state to reflect the clinical neurological function and the disease state alteration in the patients with ICVD. Materials and Methods Subjects Seventy patients with subacute and chronic ischemic stroke in Xuanwu Hospital, Capital Medical University from March 2018 to August 2023 were retrospectively screened and included according to the following criteria: (1) a confirmed diagnosis of ICVD due to ICA or MCA steno-occlusive; (2) a history of a clinically confirmed stroke of the relevant ICA or MCA territory; and (3) consecutive PET/MR, DWI, computed tomography angiography and magnetic resonance angiography scans. The exclusion criteria were: (1) presence of multiple infarcts on both sides of the brain; (2) other neurological disorders that can cause abnormal brain metabolism; and (3) the contraindication for MRI or artefacts on MRI. Following a year of rehabilitation training (non-operative treatment), nineteen patients underwent a repeat follow-up [ 18 F]FDG PET/MR scan. The neurological function was assessed using the National Institutes of Health Stroke Scale (NIHSS) and the Modified Rankin Scale (mRS) scores on admission to hospital. This study was approved by the ethical approval institutional review board of Xuanwu Hospital, Capital Medical University and conducted in accordance with the Declaration of Helsinki. The written informed consents were obtained from all participating patients. Figure 1 shown the flowchart of the study design. PET/MR Image Acquisition All scans were collected on a hybrid time of flight (TOF) PET/MR system (Signa, GE Healthcare). Before examination, all subjects fasted for minimum of 6 hours and blood glucose levels were checked to ensure a glycemic level below 8 mmol/L. [ 18 F]FDG (3.7 MBq/kg) were injected manually in the median cubital vein, the PET/MR scan started at 50min post-injection. Each patient was instructed to stay calm and was positioned in the PET/MR scanner in a supine position using a 19-channel head and neck union coil. The [ 18 F]FDG PET images were obtained over a period of 10 minutes. The PET data were corrected for attenuation, scatter, random, decay, and dead time. The default attenuation correction sequences (Dixon MR sequences) and MR scans were simultaneously obtained. The Dixon MR sequences was automatically prescribed and acquired as follows: LAVA-Flex (GE Healthcare) axial acquisition, repetition time (TR) = 4 ms, echo time (TE) = 1.7 ms, slice thickness = 5.2 mm, 120 slices, pixel size = 1.95 × 2.93 mm 2 , and acquisition time = 18 s. The corrected PET data were reconstructed using a time-of-flight, point spread function, ordered subset expectation maximization (time of flight - point spread function - office of systems engineering and management, TOF-PSF-OSEM) algorithm with 8 iterations and 32 subsets, and a 3 mm cut-off filter. The resulting pixel size was 1.82 × 1.82 × 2.78 mm 3 . PET and MR imaging data were simultaneously acquired. The main MRI sequences included the T2 fluid-attenuated inversion recovery (T2 FLAIR) sequence (voxel size = 0.94 × 0.94 × 4.00 mm 3 , TR = 11000 ms, TE = 141 ms, and slices = 32), the diffusion-weighted image (DWI) (b = 0/1000) sequence (voxel size = 1.88 × 1.88 × 4.00 mm 3 , TR = 6189 ms, TE = 74.7 ms, and slices = 32). PET/MR Image Preprocessing The PET/MR image preprocessing using 3D Slicer Tool (version 5.5.0, https://www.slicer.org ). Firstly, the skull-stripping was performed on all MRI sequences (T2 Flair, DWI, ADC) as well as [ 18 F]FDG PET by using HD-BET brain extraction toolkit algorithm[ 13 ]. Subsequently, each subject’s [ 18 F]FDG PET image was performed partial volume correction (PVC) by using the Van-Cittert algorithm, and the standardized uptake value ratio (SUVR) was calculated[ 14 , 15 ]. Then, the DWI (b = 1000), ADC, and PET images were all co-registered to the individual T2 FLAIR image using the General Registration BRAINS algorithm. Segmentation Process Two experienced neuroradiologists independently and manually delineated the cerebral infarction regions based on PET/MRI images. For cases of disagreement, a consensus was reached in a separate session. The individual brain PET image was subdivided into 83 regions by using an automated labeling system, neuroparc ( https://github.com/neurodata/neuroparc )[ 16 ]. And the bilateral frontal, temporal, parietal, and occipital regions were left behind in preparation for the next step of analysis. Asymmetry Index (AI) Measurements AI is the method used to measure the asymmetry of a distribution. The [ 18 F]FDG PET images were converted into maps that represented the standardized uptake value ratio (SUVR) for each voxel. The SUVR was calculated by dividing the tissue concentration of radioactivity (kBq/mL) in the region of interest by the mean activity concentration in a reference region. The AI was calculated in the individual brain map after removing all cerebral infarctions on both ipsilateral and contralateral sides to assess the left-right asymmetry on SUVR value. Two formulas for evaluating asymmetry, named AI 1 and AI 2 , were computed based on the following two equations ( 1 ) (2) respectively[ 17 ]: $$A{I_1}=\frac{{SUV{R_{contralateral}} - SUV{R_{ipsilateral}}}}{{SUV{R_{contralateral}}}} \times 100\%$$ 1 $$A{I_2}=\frac{{{\text{2}} \times (SUV{R_{contralateral}} - SUV{R_{ipsilateral}})}}{{SUV{R_{contralateral}}+SUV{R_{ipsilateral}}}} \times 100\%$$ 2 where contralateral (ipsilateral) represents the affected side (unaffected side). Subsequently, the volume of decreased metabolism on affected side was defined as those voxel-wise AI value higher than 10%[ 18 ]. And we calculated the percentage change in frontal, temporal, parietal, occipital regions and cerebral hemisphere on affected side compared to the unaffected side. Statistical Analysis All statistical analyses were conducted using IBM SPSS Statistics for Windows, version 27.0 (IBM). Categorical variables were expressed as percentages. Normally distributed metric variables were expressed as mean ± standard deviation (SD). Non-normally distributed variables were expressed as median (range). Metabolic differences across the AI calculation methods were assessed using Paired T-tests. The statistical significance was determined at p value < 0.05. The correlation between the AI assessments and NIHSS/mRS score were analyzed using Spearman’s rank correlation. The data before and after the follow-up were all analyzed. Results Patient characteristic A total of 70 patients, comprising 51 males (72.86%) with a mean age of 52 ± 11 years, presenting with unilateral internal carotid artery and middle cerebral artery steno-occlusive disease, underwent a [ 18 F]FDG PET/MR scan. The NIHSS and mRS scores were recorded for each patient. Additionally, nineteen of these patients (including 10 males (52.63%) with a mean age of 51 ± 14 years at pre-follow-up and 53 ± 13 years at post-follow-up) underwent a repeated follow-up [ 18 F]FDG PET/MR scan and were reassessed using the NIHSS and mRS after a year of rehabilitation training. Figure 2 illustrates the exemplary imaging of one participant both before and after the follow-up period. Table 1 showed the demographic characteristics of all patients and the follow-up participants. Table 1 Demographic characteristics of participants Unilateral internal carotid artery/middle cerebral artery steno-occlusive disease patients All (N = 70) Pre-follow-up (N = 19) Post-follow-up (N = 19) p value Gender (M/F) 51/19 10/9 Age (years) 52.36 ± 10.57 51.32 ± 13.64 52.95 ± 13.43 0.2783 NIHSS 3 (0, 14) 5 (1, 10) 2 (0, 9) 0.0047 mRS 2 (0, 5) 4 (1, 4) 2 (0, 4) < 0.0001 Blood glucose (mmol/L) 5.92 ± 1.00 5.85 ± 1.16 5.92 ± 1.24 0.1163 Injection (MBq) 306.18 ± 52.16 309.25 ± 61.04 294.12 ± 50.47 0.1056 Gender was expressed as percentage. Age, blood glucose, and injection were expressed as mean ± standard deviation. The NIHSS and mRS scores were expressed as median (range). The p value was shown for the statistically significant difference between pre-follow-up and post-follow-up. NIHSS, National Institutes of Health Stroke Scale; mRS, Modified Rankin Scale. The hypometabolic state on affected side as assessed by AI 1 and AI 2 We found the regional decreased metabolism volume and its respective percentage in the individual lobes and the entire hemisphere on the affected side, not including the infarction area, obtained from AI 2 method were all greater than that of AI 1 . The volume of hypometabolism was calculated as 176.66 ± 36.09 vs. 178.68 ± 36.11 ( p < 0.0001) for AI 1 and AI 2 on the affected cerebral hemisphere, resulting in a calculated percentage of hypometabolism of 43.52 ± 8.72 vs. 44.02 ± 8.69 ( p < 0.0001). In addition, the volume and percentage of hypometabolism observed in frontal, temporal, parietal, and occipital lobes yielded comparable outcomes (all p < 0.0001) (shown in Table 2 ). Table 2 The hypometabolic volume and percentage in participants (N = 70) Volume (ml) Percentage (%) Region AI 1 AI 2 p value AI 1 AI 2 p value FL 50.50 ± 12.45 51.01 ± 12.46 < 0.0001 45.25 ± 10.72 45.71 ± 10.69 < 0.0001 TL 41.31 ± 10.98 41.84 ± 11.02 < 0.0001 42.36 ± 11.13 42.90 ± 11.11 < 0.0001 PL 64.43 ± 15.69 65.14 ± 15.74 < 0.0001 44.25 ± 9.29 44.74 ± 9.26 < 0.0001 OL 20.43 ± 7.77 20.69 ± 7.86 < 0.0001 40.22 ± 9.65 40.73 ± 9.66 < 0.0001 Affected hemisphere 176.66 ± 36.09 178.68 ± 36.11 < 0.0001 43.52 ± 8.72 44.02 ± 8.69 < 0.0001 Mean ± standard deviation was shown for the regional hypometabolic volume and percentage evaluated by AI (AI 1 , AI 2 ). The p value was shown for the statistically significant difference between AI 1 and AI 2 . FL, Frontal lobe; TL, Temporal lobe; PL, Parietal lobe; OL, Occipital lobe. Correlation between the decreased metabolism volume as evaluated by AI (AI 1 , AI 2 ) and the NIHSS/mRS score A significant correlation between the decreased metabolism volume (excluding the infarction area) as evaluated by AI (AI 1 , AI 2 ) and the NIHSS score can be observed. The correlation coefficients obtained from the AI 1 method were all higher than those from the AI 2 . The correlation coefficients for the temporal lobe were 0.3403 ( p = 0.0039, Suppl. Figure 1a) and 0.3393 ( p = 0.0041, Suppl. Figure 1b) for AI 1 and AI 2 , respectively. The correlation coefficients for the parietal lobe were 0.3076 ( p = 0.0096, Suppl. Figure 1e) and 0.3052 ( p = 0.0102, Suppl. Figure 1f) for AI 1 and AI 2 , respectively. And the correlation coefficients for the whole affected hemisphere were 0.3010 ( p = 0.0113, Suppl. Figure 3a) and 0.2942 ( p = 0.0134, Suppl. Figure 3b) for AI 1 and AI 2 , respectively. Similar results demonstrated that the volume and percentage (without the infarction area) of hypometabolism obtained by the AI 1 exhibited a stronger correlation with mRS than the correlation between AI 2 and mRS. The correlation coefficients for the temporal lobe were 0.2751 ( p = 0.0212, Suppl. Figure 1c) and 0.2745 ( p = 0.0215, Suppl. Figure 1d) for AI 1 and AI 2 , respectively. The correlation coefficients for the parietal lobe were 0.2885 ( p = 0.0154, Suppl. Figure 1g) and 0.2825 ( p = 0.0178, Suppl. Figure 1h) for AI 1 and AI 2 , respectively. And the correlation coefficients for the whole affected hemisphere were 0.3010 ( p = 0.0113, Suppl. Figure 3c) and 0.2942 ( p = 0.0134, Suppl. Figure 3d) for AI 1 and AI 2 , respectively. Correlation between the regional percentage of the decreased metabolism volume as evaluated by AI (AI 1 , AI 2 ) and the NIHSS/mRS score The significant correlation between the regional decreased metabolic percentage (without the infarction area) evaluated by AIs (AI 1 , AI 2 ) and the NIHSS score can be observed. The correlation coefficients for the temporal lobe were 0.3550 ( p = 0.0026, Suppl. Figure 2a) and 0.3516 ( p = 0.0028, Suppl. Figure 2b) for AI 1 and AI 2 , respectively. And the correlation coefficients for the parietal lobe were 0.4887 ( p < 0.0001, Suppl. Figure 2e) and 0.4866 ( p < 0.0001, Suppl. Figure 3f) for AI 1 and AI 2 , respectively. The higher correlation in AI 1 could also be found between the regional percentage of decreased metabolism volume and the mRS score. The correlation coefficients for the temporal lobe were 0.2917 ( p = 0.0143, Suppl. Figure 2c) and 0.2874 ( p = 0.0159, Suppl. Figure 2d) for AI 1 and AI 2 , respectively. And the correlation coefficients for the parietal lobe were 0.4504 ( p < 0.0001, Suppl. Figure 2g) and 0.4485 ( p < 0.0001, Suppl. Figure 2h) for AI 1 and AI 2 , respectively. The decreased metabolic percentage in the whole affected hemisphere as determined by the AI 2 , demonstrated a slightly stronger correlation with NIHSS/mRS scores than the results obtained by the corresponding AI 1 . The correlation coefficients for the NIHSS score were 0.4076 ( p = 0.0005; Fig. 3 e) and 0.4094 ( p = 0.0004; Fig. 3 f) for AI 1 and AI 2 , respectively. And the correlation coefficients for the mRS score were 0.3649 ( p = 0.0019; Fig. 3 g) and 0.3660 ( p = 0.0018; Fig. 3 h), respectively. Difference of hypometabolic state assessed by AI 1 and AI 2 in pre-follow-up and post-follow-up patients In the follow-up group of fourteen patients, the volume of decreased hypometabolism (excluding the infarction area) was 18.07 ± 12.53 and 17.98 ± 12.46, respectively, as assessed by the AI 1 and AI 2 methods (both p < 0.0001; Fig. 4 a). Moreover, the percentage of decreased hypometabolism was 4.67 ± 3.25 and 4.64 ± 3.23, respectively (both p < 0.0001; Fig. 4 b). The reduction in hypometabolic volume, as assessed by the AI 1 method, and its hemispheric percentage on the affected side, was both greater than that observed by the AI 2 (both p < 0.0001). Table 3 provide a detailed comparison of the before and after follow-up evaluations conducted using the AI 1 and AI 2 . In addition, there were significant differences between the pre- and post-follow-up patients in terms of NIHSS ( p < 0.0047) and mRS ( p < 0.0001) scores (show in Table 4 ). Moreover, the decreased metabolism observed in the remaining five patients in the follow-up group exhibited an increase, accompanied by a notable improvement in their NIHSS/mRS scores. Table 3 The hypometabolic volume and percentage in the affected hemisphere of the pre- and post-follow-up (N = 14) AI 1 AI 2 Pre-follow-up Post-follow-up Change p value Pre-follow-up Post-follow-up Change p value Volume (ml) 187.35 ± 41.76 169.28 ± 39.64 18.07 ± 12.53 0.0001 189.17 ± 41.63 171.18 ± 39.59 17.98 ± 12.46 0.0001 Percentage (%) 47.84 ± 10.77 43.17 ± 9.84 4.67 ± 3.25 0.0001 48.30 ± 10.71 43.66 ± 9.80 4.64 ± 3.23 0.0001 Mean ± standard deviation was shown for the hypometabolic volume and percentage in the affected hemisphere evaluated by the AI (AI 1 , AI 2 ). The p value was shown for the statistically significant difference between the results of pre-follow-up and post-follow-up patients. Table 4 The NIHSS and mRS scores of pre-follow-up and post-follow-up patients (N = 14) Clinical score Pre-follow-up Post-follow-up Change p value NIHSS 4.5 (1, 9) 1.5 (0, 4) 2 (0, 7) 0.0047 mRS 4 (1, 4) 2 (0, 3) 2 (0, 3) < 0.0001 The NIHSS and mRS scores were shown as median value (range). The p value was shown for the statistically significant difference between the clinical scores (NIHSS/mRS) from pre-follow-up and post-follow-up patients respectively. NIHSS, National Institutes of Health Stroke Scale; mRS, Modified Rankin Scale. Discussion This study aims to compare two AI methods in assessing decreased [ 18 F]FDG metabolism for reflecting clinical neurological function in patients with ICVD, and to explore its value in research and clinical settings. The results demonstrated that the volume and percentage of decreased metabolism in the lobes on affected side, excluding the infarct area, attained through AI 2 calculation consistently greater than those achieved through AI 1 . The correlation between the volume of decreased metabolism in the affected cerebral hemisphere, as calculated by AI 1 and AI 2 , and the clinical score can be observed. The improved follow-up patients showed more pronounced metabolic improvement as assessed by AI 1 . The brain is a highly metabolically active organ that primarily utilizes glucose as its source of energy. [ 18 F]FDG has been widely employed in PET systems as a glucose analogue, facilitating valuable insights into cerebral glucose metabolism and functional status[ 19 ]. Yuan H et al.[ 5 ] reported a consistently elevated metabolism at the peri-ischemic zone during acute cerebral ischemia, suggesting the presence of viable tissues that can be salvaged with reperfusion. Nagasawa H et al.[ 20 ]observed significant decreases in cerebral glucose metabolic rate (CMRGlu) in areas without lesions using [ 18 F]FDG PET in chronic ischemic cerebrovascular disease. In our study, we employed [ 18 F]FDG PET/MR to simultaneously acquire precise structural data with excellent tissue contrast, as well as metabolic and functional information. The AI 1 and AI 2 was both employed to assess cerebral metabolism in patients presenting with unilateral stenosis or occlusion of the internal carotid or middle cerebral arteries, and the results demonstrated a notable decrease in the metabolic activity of the brain tissue (without the infarct lesion) when compared to the contralateral side. The AI 1 and AI 2 computations have been employed in many prior investigations that have focused on the research of ICVD, primarily for investigations evaluating asymmetry in the structure of brain regions, cerebral blood flow, and cerebral metabolism[ 21 , 22 ]. The present study focuses on the comparative assessment of the two AIs in evaluating cerebral metabolic reduction on the affected side. We found that the AI 2 method consistently yielded larger estimates for both the volume and the regional percentage of decreased brain metabolism on the affected side, in comparison to the AI 1 method. It possible to interpret this in terms of the meaning expressed by the structure of the two formulas. It can be observed that the numerator of the two formulas, AI 1 and AI 2 , is identical, while the denominator of both is constituted by the SUVR value of the contralateral cerebral hemisphere and the average SUVR value of the bilateral cerebral hemispheres, respectively. Furthermore, the average SUVR value of the two cerebral hemispheres is less than that of the contralateral cerebral hemisphere due to the presence of brain tissue around the infarct foci in the affected hemisphere, which undergoes less metabolism than that of the contralateral cerebral hemisphere. It can therefore be surmised that for a given patient with unilateral internal carotid or middle cerebral artery stenosis and occlusion, the asymmetry calculated by AI 2 will be greater than that obtained by the AI 1 method. This will consequently result in a larger volume and percentage of metabolic decompensation being assessed by AI 2 than by AI 1 [ 23 ]. Prior research has identified a notable correlation between the magnitude of the AI value or its evaluation of diminished cerebral metabolism and clinical neurological function and prognosis in patients with ischemic cerebrovascular disease across both the subacute and chronic phases[ 24 ]. Cui et al.[ 18 ]identified a significant correlation between the preoperative NIHSS score and the AI 1 value in patients undergoing [ 18 F]FDG PET imaging. Yu et al.[ 25 ]performed [ 18 F]FDG PET imaging for investigating changes in metabolism before and after bypass surgery in patients with chronic ischemia. And they demonstrated that the AI 2 method can be used to assess the difference in metabolism between the bilateral hemispheres whose declination correlated significantly with the surgical therapy, and suggested the uptake of [ 18 F]FDG in the hypoperfusion region surrounding the ischemic core is a sensitive indicator for predicting ischemia. Our study also revealed a significant correlation between decreased cerebral metabolism, as evaluated through both AI 1 and AI 2 , and NIHSS/mRS scores. In light of the fact that the NIHSS score is of great value for the monitoring of patients' progress and the planning of rehabilitation or long-term care, and that the mRS score is highly relevant to clinicians and patients contemplating intervention[ 26 ], we are of the opinion that the assessment of cerebral metabolism using both AI 1 and AI 2 methods has the potential to achieve clinical status assessment. Moreover, although it was mentioned above that the AI 2 method generally yielded larger measurements of decreased metabolism compared to the AI 1 method, which might lead us to speculate that AI 2 method might more comprehensively assess the hypometabolism of the brain, our results showed that, in the temporal and parietal lobes, the correlation coefficients of the decreased metabolism volume or its percentage and NIHSS/mRS score assessed by AI 1 method were all higher than that of AI 2 method. Conversely, the AI 2 method showed higher correlation coefficients in other regions. According to the cerebral arterial territories research, this distribution characteristic may be related to our subject selection, with cerebral ischemic attributed to the unilateral steno-occlusion attributed to the internal carotid artery or middle cerebral artery[ 27 , 28 ]. In addition, it also probably due to the fact that when assessing metabolic decompensation in the region supplied by the responsible vascular innervation on the affected side, the AI 2 method takes into account the contralateral metabolism, whereas the AI 1 method somewhat relatively disregards this influence. These suggest that the AI 1 method may be more suitable for evaluating the metabolic changes around the infarct area and the clinical status of the unilateral internal carotid artery/middle cerebral artery steno-occlusive disease patients. Al-Ajlan et al.[ 29 ]showed that the biological underpinning of the success of endovascular therapy is a reduction in infarct volume. Sobesky et al.[ 30 ]found that the cerebral hypoperfusion volume, as assessed by the AI, was significantly correlated with infarct volume and with NIHSS score. Another study observed the significant correlation between the AI of cerebral blood flow and SUVR values in the affected cerebral hemisphere of ICVD patients[ 31 ]. Yoon et al.[ 32 ]reported the significant correlation between the pretreatment of percentage of infarct core area in each brain region and the mRS score in the acute stroke patients. In our study, we found that in the affected cerebral hemispheres, both volume and percentage of metabolic reduction assessed by AI 1 and AI 2 were significantly correlated with NIHSS/mRS scores, although the volumetric correlation assessed by AI 1 was slightly stronger than the correlation for AI 2 , and the percentage of metabolic reduction assessed by AI 2 in relation to NIHSS/mRS was slightly stronger than the corresponding correlation with AI 1 . Sebök M et al.[ 33 ]observed that the patients with ICVD who exhibited significantly elevated NIHSS/mRS scores demonstrated elevated asymmetry indices based on PET imaging. Zhang H et al.[ 19 ]demonstrated that both the significant improvements in neurological function and improvements in brain metabolism shown by [ 18 F]FDG PET imaging could be found after treatment. Similarly, our study indicated both the improvement of cerebral metabolism assessed by AIs and the decreased NIHSS/mRS score can be found in the fourteen follow-up patients. Furthermore, the metabolic improvement of patients after follow-up as assessed by AI 1 was found to be more remarkable than that assessed by AI 2 , so we proposed that the AI 1 method is more sensitive to the improvement in clinical condition before and after follow-up. Additionally, the hypometabolic area in each region, as assessed by AIs in five other follow-up patients with decreased NIHSS/mRS scores, did not consistently shrink. Considering that the level of glucose utilization correlates with neuronal activity, as showed by previous research, it is important to pay more attention to these follow-up patients or consider adjusting treatment regimens[ 34 , 35 ]. The quantification of the extent of decreased metabolism around infarct foci due to unilateral internal carotid artery or middle cerebral artery stenosis-occlusion based on AI the intuitive and objective assessment of the decreased metabolic state of the patient's brain tissue by neuroimagers and clinicians alike. Furthermore, it reflects the metabolic changes of the patient's brain tissue before and after follow-up, which may assist clinicians in making an appropriate treatment decision for the patient's condition. In cases where there is a discrepancy between the patient's clinical scores and the observed changes in brain metabolism, this may indicate the necessity for further evaluation of the patient's disease status or appropriate adjustments to the patient's treatment plan. In this study, we found that the degree of decreased [ 18 F]FDG metabolism obtained by both the AI 1 and AI 2 methods correlated with NIHSS and mRS scores. However, the correlation obtained by the AI 1 method was stronger than that obtained by the AI 2 method in the areas of the internal carotid artery and middle cerebral artery, which are the main blood supply regions. Furthermore, we were able to detect the changes in the patients' cerebral metabolism before and after follow-up more sensitively using the AI 1 method. It is therefore recommended that the use of AI 1 for more superior assessment, when evaluating disease severity and prognosis in patients with ICVD, as well as realize the changes in disease progression, be employed. Our study has a few limitation. First, the clinical samples were small and consisted of a uniform participant population (unilateral steno-occlusive disease). Second, our follow-up time was relatively short. The future study will improve the follow-up time in both number and duration. Additionally, this study used a semi-quantitative approach to compare the AI 1 and AI 2 methods. Future research could further explore the absolute quantitative aspects. Conclusions In summary, the correlation between the clinical scores and the decreased [ 18 F]FDG metabolism assessed by AI 1 method appears more significantly reflecting the improvement of these patient’s clinical condition before and after follow-up. The assessment of cerebral [ 18 F]FDG metabolism in patients with unilateral internal carotid/middle cerebral artery steno-occlusion to reflect clinical neurological function using the AI 1 demonstrated superior performance in comparison to the AI 2 . Declarations Acknowledgements: We thank Hui Liao for her help in comments of statistical analysis, Hui Liao and Yifei Zhang for their help in the comments of revision. Author Contributions: MZ,JL, YL and BC designed the study; LY, BY, HY, and LM performed the image acquisitions; YL, BC, and YS performed the image analysis; YL and BC interpreted the results and wrote the initial manuscript; MZ and JL reviewed and revised the initial manuscript; all authors contributed to and approved the final manuscript. Funding : This study was supported by the National Natural Science Foundation of China (Grant No. 82130058), Project of Beijing Municipal Administration of Hospitals Incubating Program (Grant No. PX2024033), R&D Program of Beijing Municipal Education Commission (KM202410025016), Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (Grant No. CCMU2024ZKYXZ008), and the Natural Science Foundation of Capital Medical University (Grant No. PYZ22048). Conflicts of Interest : The authors declare that they have no conflict of interest with regard to this study. Ethical Approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed Consent: Informed consent was obtained from all individual participants included in the study. 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J Nuclear Medicine: Official Publication Soc Nuclear Med 38:14–19 Meerwaldt AE, Straathof M, Oosterveld W et al (2023) In vivo imaging of cerebral glucose metabolism informs on subacute to chronic post-stroke tissue status - A pilot study combining PET and deuterium metabolic imaging. J Cereb Blood Flow Metabolism: Official J Int Soc Cereb Blood Flow Metabolism 43:778–790 Takasawa M, Watanabe M, Yamamoto S et al (2002) Prognostic value of subacute crossed cerebellar diaschisis: single-photon emission CT study in patients with middle cerebral artery territory infarct. AJNR Am J Neuroradiol 23:189–193 Yu Z, Shi X, Zhou Z, Yang Y, Li P, Zhang Y (2020) Cerebral glucose metabolism changes in chronic ischemia patients following subcranial-intracranial bypass. Neurosurg Rev 43:1383–1389 Kasner SE (2006) Clinical interpretation and use of stroke scales. 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Eur J Nucl Med Mol Imaging 47:1668–1677 Yoon W, Baek BH, Lee YY, Kim SK, Kim J-T, Park MS (2021) Association of pretreatment pontine infarction with extremely poor outcome after endovascular thrombectomy in acute basilar artery occlusion. J Neurointerventional Surg 13:136–140 Sebök M, van Niftrik CHB, Piccirelli M et al (2018) BOLD cerebrovascular reactivity as a novel marker for crossed cerebellar diaschisis. Neurology 91:e1328–e1337 Lou M, Zhang H, Wang J et al (2007) Hyperbaric oxygen treatment attenuated the decrease in regional glucose metabolism of rats subjected to focal cerebral ischemia: a high resolution positron emission tomography study. Neuroscience 146:555–561 Sobrado M, Delgado M, Fernández-Valle E et al (2011) Longitudinal studies of ischemic penumbra by using 18F-FDG PET and MRI techniques in permanent and transient focal cerebral ischemia in rats. NeuroImage 57:45–54 Supplementary Files COIformBinYang.pdf COIformBixiaoCui.pdf COIformHongweiYang.pdf COIformJieLu.pdf COIformLeiMa.pdf COIformLinlinYe.pdf COIformMiaoZhang.pdf COIformYiShan.pdf COIformYuxinLiang.pdf ElectronicSupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Molecular Imaging and Biology → Version 1 posted Reviewers agreed at journal 18 Dec, 2024 Reviewers invited by journal 18 Dec, 2024 Editor assigned by journal 23 Oct, 2024 First submitted to journal 23 Oct, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5319717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":391825944,"identity":"77563e52-35be-441e-83b7-7a86d31bd72e","order_by":0,"name":"Yuxin Liang","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Liang","suffix":""},{"id":391825945,"identity":"ec7200e5-df72-4a12-9c73-58138f36d8f6","order_by":1,"name":"Bixiao Cui","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bixiao","middleName":"","lastName":"Cui","suffix":""},{"id":391825946,"identity":"e4a3cbbd-81df-4053-9d4d-7c79c6001195","order_by":2,"name":"Linlin Ye","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Ye","suffix":""},{"id":391825947,"identity":"30cce67b-d677-4755-a487-7c1472d8be9f","order_by":3,"name":"Bin Yang","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yang","suffix":""},{"id":391825948,"identity":"b9b43b48-2adc-4a7d-a004-7c0908477da1","order_by":4,"name":"Yi Shan","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Shan","suffix":""},{"id":391825949,"identity":"dbdc5a2d-6084-409b-afcb-7613768b531f","order_by":5,"name":"Hongwei Yang","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Yang","suffix":""},{"id":391825950,"identity":"2bc735ac-f410-43eb-8d56-3422426b6102","order_by":6,"name":"Lei Ma","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ma","suffix":""},{"id":391825951,"identity":"2fad61a7-4e2d-41c3-a0c0-88d85dda5aae","order_by":7,"name":"Miao Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYDACCSSSIaGAQY6Nvf0AKVoMGIz5eM4kEKMFBgwYEudJOBjg1SE/u/nZw685FvL8DOxPNzwwsElvk2BIYPhRsQ2nFsY5x8yNZbdJGM5s4DG7kWCQltsm3XiAsefMbZxamCUSzKQlt0kwbjjAwwbUcji3TeZAAjNjG24tbBLp30Ba7DccYH8G1PI/nU0iwQCvFh6JHDPJj9skEjccYAA57EACQS0SEjll0ozbJJKhfkk2bAMG8kF8fpGfkb5N8ue2Ott+BvZnN39U2MnLt7cffPCjArcWcBDwgDU/QIgcwKseCBh/EFIxCkbBKBgFIxsAAOqXUz5XPmYTAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4814-6159","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Miao","middleName":"","lastName":"Zhang","suffix":""},{"id":391825952,"identity":"dbb38580-5d23-4f52-9d9e-00a2c92779d4","order_by":8,"name":"Jie Lu","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-10-23 14:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5319717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5319717/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11307-025-02002-7","type":"published","date":"2025-04-15T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72284592,"identity":"f6ba91e2-822d-4625-a475-0b9ce839c9b5","added_by":"auto","created_at":"2024-12-24 16:47:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":822466,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study design. AI, asymmetry index. ROIs, regions of interest.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5319717/v1/8a7d612a455efbd758923878.png"},{"id":72285963,"identity":"57d1db44-c467-43d2-be21-42c55eeccaa5","added_by":"auto","created_at":"2024-12-24 16:55:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1695402,"visible":true,"origin":"","legend":"\u003cp\u003eExample of a participate with ischemic cerebrovascular disease. A 24-year-old woman with ischemia in the right hemisphere. The NIHSS and mRS scores improved from 5 and 4 before follow-up to 1 and 2, respectively. On the T1 \u003cstrong\u003e(a)\u003c/strong\u003e, T2 \u003cstrong\u003e(b)\u003c/strong\u003e, T2-FLAIR \u003cstrong\u003e(c)\u003c/strong\u003e, and DWI \u003cstrong\u003e(d)\u003c/strong\u003e, the infarction can be seen in the right hemisphere. On the [\u003csup\u003e18\u003c/sup\u003eF]FDG PET-MR in the pre-follow-up period \u003cstrong\u003e(e1)\u003c/strong\u003e, the metabolic impairment can be seen in the peri-infarct area. On the [\u003csup\u003e18\u003c/sup\u003eF]FDG PET-MR in the post-follow-up period \u003cstrong\u003e(e2)\u003c/strong\u003e, the hypometabolic state of the infarct surrounding areas in the right cerebral hemisphere exhibited an improvement from the previous state.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5319717/v1/436a0adb12b352ae78ddd8d4.png"},{"id":72285959,"identity":"aa2382df-f81d-409e-af1c-e43582b36eb1","added_by":"auto","created_at":"2024-12-24 16:55:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":472683,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between the hypometabolic volume and percentage in AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e with the clinical scores(NIHSS/mRS) (N=70). Percentage was presented as the ratio of the hypometabolic volume in affected hemisphere. NIHSS, National Institutes of Health Stroke Scale. mRS, Modified Rankin Scale. \u003cstrong\u003e(a)\u003c/strong\u003e Correlation between the volume in AI\u003csub\u003e1\u003c/sub\u003e and NIHSS score. \u003cstrong\u003e(b)\u003c/strong\u003e Correlation between the volume in AI\u003csub\u003e2\u003c/sub\u003e and NIHSS score. \u003cstrong\u003e(c)\u003c/strong\u003e Correlation between the volume in AI\u003csub\u003e1\u003c/sub\u003e and mRS score. \u003cstrong\u003e(d)\u003c/strong\u003e Correlation between the volume in AI\u003csub\u003e2\u003c/sub\u003e and mRS score. \u003cstrong\u003e(e)\u003c/strong\u003e Correlation between the percentage in AI\u003csub\u003e1\u003c/sub\u003e and NIHSS score. \u003cstrong\u003e(f)\u003c/strong\u003e Correlation between the percentage in AI\u003csub\u003e2\u003c/sub\u003e and NIHSS score. \u003cstrong\u003e(g)\u003c/strong\u003e Correlation between the percentage in AI\u003csub\u003e1\u003c/sub\u003e and mRS score. \u003cstrong\u003e(h)\u003c/strong\u003e Correlation between the percentage in AI\u003csub\u003e2\u003c/sub\u003e and mRS score.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5319717/v1/80a2749cc89340f46706f052.png"},{"id":72286381,"identity":"9b3bb642-aa25-45ba-b9ff-5030b1ee9d6b","added_by":"auto","created_at":"2024-12-24 17:03:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78819,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the hypometabolic volume \u003cstrong\u003e(a)\u003c/strong\u003e/percentage \u003cstrong\u003e(b)\u003c/strong\u003e change of pre-follow-up and post-follow-up patients in AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e (N=14). Percentage was presented as the ratio of the hypometabolic volume in affected hemisphere. **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5319717/v1/7c53c5320e78d82789f0d673.png"},{"id":81050849,"identity":"827f6035-c57e-4b59-b6bc-259f512c886a","added_by":"auto","created_at":"2025-04-21 16:05:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3963507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5319717/v1/a52b2531-6472-4572-9b01-3c35718354a2.pdf"},{"id":72284615,"identity":"675b0dd0-afca-436b-8684-9d4faf6d4c03","added_by":"auto","created_at":"2024-12-24 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16:47:56","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":1247955,"visible":true,"origin":"","legend":"","description":"","filename":"COIformYuxinLiang.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5319717/v1/5562961bbdb30569c401636b.pdf"},{"id":72284600,"identity":"fc5c8c89-f647-4423-9031-ffea40ecf83b","added_by":"auto","created_at":"2024-12-24 16:47:55","extension":"docx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":33181896,"visible":true,"origin":"","legend":"","description":"","filename":"ElectronicSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5319717/v1/1cf471bf760b795492aee076.docx"}],"financialInterests":"","formattedTitle":"Comparison of the correlation between cerebral [18F]FDG metabolism as assessed by two asymmetry indices and clinical neurological score in patients with ischemic cerebrovascular disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic cerebrovascular disease (ICVD) is predominantly responsible for the majority of strokes, characterized by progressive steno-occlusive changes at the terminal portion of the internal carotid artery (ICA) or middle cerebral artery (MCA)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As one of the leading causes of death and disability worldwide, ICVD has posed a huge burden in public health issue of growing importance[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The risk of stroke recurrence among these patients remains high, even with aggressive medical management[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which has prompted us to contemplate effective long-term monitoring to control the risk of reinfarction in patients with cerebral infarction.\u003c/p\u003e \u003cp\u003eThe brain is an organ with high metabolic activity that primarily utilizes glucose as its source of energy. The 2-deoxy-2-[\u003csup\u003e18\u003c/sup\u003eF]fluoro-D-glucose ([\u003csup\u003e18\u003c/sup\u003eF]FDG) PET imaging offers valuable insights into cerebral metabolism[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which holds great significance in understanding the functional condition[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In contrast to MRI, the hybrid [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/MR allows for acquisition both of the precise morphological data and the metabolic information[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Quantifying the pathophysiological changes and regional metabolism in the course of ICVD patients is critical for monitoring their clinical outcomes and recovery effects[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe asymmetry index (AI) is commonly employed for assessing the distribution asymmetry of cerebral metabolism or cerebral blood flow[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous studies on functional brain changes in ICVD patients have shown that the AI values for glucose metabolism were significantly lower after effective treatment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Van Niftrik CHB et al.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]using an AI method demonstrated a robust association between the degeneration after stroke and the ipsilateral thalamic diaschisis, while Zhu Y et al.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]found the metabolic assessment of another AI method was correlated with the predisposing factors in ICVD. However, the results of different AI methods may yield disparate insights into the clinical neurologic status of a given subject. It remains a question worthy of further exploration as to which AI computational approach is better suited to assessing decreased [\u003csup\u003e18\u003c/sup\u003eF]FDG metabolism and facilitating comprehension of alterations in disease state among ICVD patients.\u003c/p\u003e \u003cp\u003eThe objective was to identify the AI method for more superior performance of capturing the decreased [\u003csup\u003e18\u003c/sup\u003eF]FDG metabolism state to reflect the clinical neurological function and the disease state alteration in the patients with ICVD.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eSeventy patients with subacute and chronic ischemic stroke in Xuanwu Hospital, Capital Medical University from March 2018 to August 2023 were retrospectively screened and included according to the following criteria: (1) a confirmed diagnosis of ICVD due to ICA or MCA steno-occlusive; (2) a history of a clinically confirmed stroke of the relevant ICA or MCA territory; and (3) consecutive PET/MR, DWI, computed tomography angiography and magnetic resonance angiography scans. The exclusion criteria were: (1) presence of multiple infarcts on both sides of the brain; (2) other neurological disorders that can cause abnormal brain metabolism; and (3) the contraindication for MRI or artefacts on MRI. Following a year of rehabilitation training (non-operative treatment), nineteen patients underwent a repeat follow-up [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/MR scan. The neurological function was assessed using the National Institutes of Health Stroke Scale (NIHSS) and the Modified Rankin Scale (mRS) scores on admission to hospital. This study was approved by the ethical approval institutional review board of Xuanwu Hospital, Capital Medical University and conducted in accordance with the Declaration of Helsinki. The written informed consents were obtained from all participating patients. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shown the flowchart of the study design.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePET/MR Image Acquisition\u003c/h3\u003e\n\u003cp\u003eAll scans were collected on a hybrid time of flight (TOF) PET/MR system (Signa, GE Healthcare). Before examination, all subjects fasted for minimum of 6 hours and blood glucose levels were checked to ensure a glycemic level below 8 mmol/L. [\u003csup\u003e18\u003c/sup\u003eF]FDG (3.7 MBq/kg) were injected manually in the median cubital vein, the PET/MR scan started at 50min post-injection. Each patient was instructed to stay calm and was positioned in the PET/MR scanner in a supine position using a 19-channel head and neck union coil.\u003c/p\u003e \u003cp\u003eThe [\u003csup\u003e18\u003c/sup\u003eF]FDG PET images were obtained over a period of 10 minutes. The PET data were corrected for attenuation, scatter, random, decay, and dead time. The default attenuation correction sequences (Dixon MR sequences) and MR scans were simultaneously obtained. The Dixon MR sequences was automatically prescribed and acquired as follows: LAVA-Flex (GE Healthcare) axial acquisition, repetition time (TR)\u0026thinsp;=\u0026thinsp;4 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;1.7 ms, slice thickness\u0026thinsp;=\u0026thinsp;5.2 mm, 120 slices, pixel size\u0026thinsp;=\u0026thinsp;1.95 \u0026times; 2.93 mm\u003csup\u003e2\u003c/sup\u003e, and acquisition time\u0026thinsp;=\u0026thinsp;18 s. The corrected PET data were reconstructed using a time-of-flight, point spread function, ordered subset expectation maximization (time of flight - point spread function - office of systems engineering and management, TOF-PSF-OSEM) algorithm with 8 iterations and 32 subsets, and a 3 mm cut-off filter. The resulting pixel size was 1.82 \u0026times; 1.82 \u0026times; 2.78 mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePET and MR imaging data were simultaneously acquired. The main MRI sequences included the T2 fluid-attenuated inversion recovery (T2 FLAIR) sequence (voxel size\u0026thinsp;=\u0026thinsp;0.94 \u0026times; 0.94 \u0026times; 4.00 mm\u003csup\u003e3\u003c/sup\u003e, TR\u0026thinsp;=\u0026thinsp;11000 ms, TE\u0026thinsp;=\u0026thinsp;141 ms, and slices\u0026thinsp;=\u0026thinsp;32), the diffusion-weighted image (DWI) (b\u0026thinsp;=\u0026thinsp;0/1000) sequence (voxel size\u0026thinsp;=\u0026thinsp;1.88 \u0026times; 1.88 \u0026times; 4.00 mm\u003csup\u003e3\u003c/sup\u003e, TR\u0026thinsp;=\u0026thinsp;6189 ms, TE\u0026thinsp;=\u0026thinsp;74.7 ms, and slices\u0026thinsp;=\u0026thinsp;32).\u003c/p\u003e\n\u003ch3\u003ePET/MR Image Preprocessing\u003c/h3\u003e\n\u003cp\u003eThe PET/MR image preprocessing using 3D Slicer Tool (version 5.5.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.slicer.org\u003c/span\u003e\u003cspan address=\"https://www.slicer.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Firstly, the skull-stripping was performed on all MRI sequences (T2 Flair, DWI, ADC) as well as [\u003csup\u003e18\u003c/sup\u003eF]FDG PET by using HD-BET brain extraction toolkit algorithm[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Subsequently, each subject\u0026rsquo;s [\u003csup\u003e18\u003c/sup\u003eF]FDG PET image was performed partial volume correction (PVC) by using the Van-Cittert algorithm, and the standardized uptake value ratio (SUVR) was calculated[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Then, the DWI (b\u0026thinsp;=\u0026thinsp;1000), ADC, and PET images were all co-registered to the individual T2 FLAIR image using the General Registration BRAINS algorithm.\u003c/p\u003e\n\u003ch3\u003eSegmentation Process\u003c/h3\u003e\n\u003cp\u003eTwo experienced neuroradiologists independently and manually delineated the cerebral infarction regions based on PET/MRI images. For cases of disagreement, a consensus was reached in a separate session. The individual brain PET image was subdivided into 83 regions by using an automated labeling system, neuroparc (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/neurodata/neuroparc\u003c/span\u003e\u003cspan address=\"https://github.com/neurodata/neuroparc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. And the bilateral frontal, temporal, parietal, and occipital regions were left behind in preparation for the next step of analysis.\u003c/p\u003e\n\u003ch3\u003eAsymmetry Index (AI) Measurements\u003c/h3\u003e\n\u003cp\u003eAI is the method used to measure the asymmetry of a distribution. The [\u003csup\u003e18\u003c/sup\u003eF]FDG PET images were converted into maps that represented the standardized uptake value ratio (SUVR) for each voxel. The SUVR was calculated by dividing the tissue concentration of radioactivity (kBq/mL) in the region of interest by the mean activity concentration in a reference region. The AI was calculated in the individual brain map after removing all cerebral infarctions on both ipsilateral and contralateral sides to assess the left-right asymmetry on SUVR value. Two formulas for evaluating asymmetry, named AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, were computed based on the following two equations (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (2) respectively[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$A{I_1}=\\frac{{SUV{R_{contralateral}} - SUV{R_{ipsilateral}}}}{{SUV{R_{contralateral}}}} \\times 100\\%$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$A{I_2}=\\frac{{{\\text{2}} \\times (SUV{R_{contralateral}} - SUV{R_{ipsilateral}})}}{{SUV{R_{contralateral}}+SUV{R_{ipsilateral}}}} \\times 100\\%$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere contralateral (ipsilateral) represents the affected side (unaffected side). Subsequently, the volume of decreased metabolism on affected side was defined as those voxel-wise AI value higher than 10%[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. And we calculated the percentage change in frontal, temporal, parietal, occipital regions and cerebral hemisphere on affected side compared to the unaffected side.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics for Windows, version 27.0 (IBM). Categorical variables were expressed as percentages. Normally distributed metric variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Non-normally distributed variables were expressed as median (range). Metabolic differences across the AI calculation methods were assessed using Paired T-tests. The statistical significance was determined at \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The correlation between the AI assessments and NIHSS/mRS score were analyzed using Spearman\u0026rsquo;s rank correlation. The data before and after the follow-up were all analyzed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristic\u003c/h2\u003e \u003cp\u003eA total of 70 patients, comprising 51 males (72.86%) with a mean age of 52\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years, presenting with unilateral internal carotid artery and middle cerebral artery steno-occlusive disease, underwent a [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/MR scan. The NIHSS and mRS scores were recorded for each patient. Additionally, nineteen of these patients (including 10 males (52.63%) with a mean age of 51\u0026thinsp;\u0026plusmn;\u0026thinsp;14 years at pre-follow-up and 53\u0026thinsp;\u0026plusmn;\u0026thinsp;13 years at post-follow-up) underwent a repeated follow-up [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/MR scan and were reassessed using the NIHSS and mRS after a year of rehabilitation training. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the exemplary imaging of one participant both before and after the follow-up period. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the demographic characteristics of all patients and the follow-up participants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eDemographic characteristics of participants\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnilateral internal carotid artery/middle cerebral artery steno-occlusive disease patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (N\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-follow-up (N\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost-follow-up (N\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10/9\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.36\u0026thinsp;\u0026plusmn;\u0026thinsp;10.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.32\u0026thinsp;\u0026plusmn;\u0026thinsp;13.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.95\u0026thinsp;\u0026plusmn;\u0026thinsp;13.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0, 14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eBlood glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjection (MBq)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306.18\u0026thinsp;\u0026plusmn;\u0026thinsp;52.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e309.25\u0026thinsp;\u0026plusmn;\u0026thinsp;61.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e294.12\u0026thinsp;\u0026plusmn;\u0026thinsp;50.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eGender was expressed as percentage. Age, blood glucose, and injection were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. The NIHSS and mRS scores were expressed as median (range). The p value was shown for the statistically significant difference between pre-follow-up and post-follow-up. NIHSS, National Institutes of Health Stroke Scale; mRS, Modified Rankin Scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe hypometabolic state on affected side as assessed by AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eWe found the regional decreased metabolism volume and its respective percentage in the individual lobes and the entire hemisphere on the affected side, not including the infarction area, obtained from AI\u003csub\u003e2\u003c/sub\u003e method were all greater than that of AI\u003csub\u003e1\u003c/sub\u003e. The volume of hypometabolism was calculated as 176.66\u0026thinsp;\u0026plusmn;\u0026thinsp;36.09 vs. 178.68\u0026thinsp;\u0026plusmn;\u0026thinsp;36.11 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e on the affected cerebral hemisphere, resulting in a calculated percentage of hypometabolism of 43.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.72 vs. 44.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.69 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In addition, the volume and percentage of hypometabolism observed in frontal, temporal, parietal, and occipital lobes yielded comparable outcomes (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eThe hypometabolic volume and percentage in participants (N\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eVolume (ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAI\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAI\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.50\u0026thinsp;\u0026plusmn;\u0026thinsp;12.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.01\u0026thinsp;\u0026plusmn;\u0026thinsp;12.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.25\u0026thinsp;\u0026plusmn;\u0026thinsp;10.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.71\u0026thinsp;\u0026plusmn;\u0026thinsp;10.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.84\u0026thinsp;\u0026plusmn;\u0026thinsp;11.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.90\u0026thinsp;\u0026plusmn;\u0026thinsp;11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.43\u0026thinsp;\u0026plusmn;\u0026thinsp;15.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.14\u0026thinsp;\u0026plusmn;\u0026thinsp;15.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.74\u0026thinsp;\u0026plusmn;\u0026thinsp;9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.69\u0026thinsp;\u0026plusmn;\u0026thinsp;7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.22\u0026thinsp;\u0026plusmn;\u0026thinsp;9.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.73\u0026thinsp;\u0026plusmn;\u0026thinsp;9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eAffected hemisphere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176.66\u0026thinsp;\u0026plusmn;\u0026thinsp;36.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178.68\u0026thinsp;\u0026plusmn;\u0026thinsp;36.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation was shown for the regional hypometabolic volume and percentage evaluated by AI (AI\u003csub\u003e1\u003c/sub\u003e, AI\u003csub\u003e2\u003c/sub\u003e). The p value was shown for the statistically significant difference between AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e. FL, Frontal lobe; TL, Temporal lobe; PL, Parietal lobe; OL, Occipital lobe.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCorrelation between the decreased metabolism volume as evaluated by AI (AI\u003c/em\u003e \u003csub\u003e \u003cem\u003e1\u003c/em\u003e \u003c/sub\u003e, \u003cem\u003eAI\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) and the NIHSS/mRS score\u003c/em\u003e\u003c/p\u003e \u003cp\u003eA significant correlation between the decreased metabolism volume (excluding the infarction area) as evaluated by AI (AI\u003csub\u003e1\u003c/sub\u003e, AI\u003csub\u003e2\u003c/sub\u003e) and the NIHSS score can be observed. The correlation coefficients obtained from the AI\u003csub\u003e1\u003c/sub\u003e method were all higher than those from the AI\u003csub\u003e2\u003c/sub\u003e. The correlation coefficients for the temporal lobe were 0.3403 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0039, Suppl. Figure\u0026nbsp;1a) and 0.3393 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0041, Suppl. Figure\u0026nbsp;1b) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively. The correlation coefficients for the parietal lobe were 0.3076 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0096, Suppl. Figure\u0026nbsp;1e) and 0.3052 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0102, Suppl. Figure\u0026nbsp;1f) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively. And the correlation coefficients for the whole affected hemisphere were 0.3010 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0113, Suppl. Figure\u0026nbsp;3a) and 0.2942 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0134, Suppl. Figure\u0026nbsp;3b) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively.\u003c/p\u003e \u003cp\u003eSimilar results demonstrated that the volume and percentage (without the infarction area) of hypometabolism obtained by the AI\u003csub\u003e1\u003c/sub\u003e exhibited a stronger correlation with mRS than the correlation between AI\u003csub\u003e2\u003c/sub\u003e and mRS. The correlation coefficients for the temporal lobe were 0.2751 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0212, Suppl. Figure\u0026nbsp;1c) and 0.2745 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0215, Suppl. Figure\u0026nbsp;1d) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively. The correlation coefficients for the parietal lobe were 0.2885 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0154, Suppl. Figure\u0026nbsp;1g) and 0.2825 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0178, Suppl. Figure\u0026nbsp;1h) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively. And the correlation coefficients for the whole affected hemisphere were 0.3010 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0113, Suppl. Figure\u0026nbsp;3c) and 0.2942 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0134, Suppl. Figure\u0026nbsp;3d) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCorrelation between the regional percentage of the decreased metabolism volume as evaluated by AI (AI\u003c/em\u003e \u003csub\u003e \u003cem\u003e1\u003c/em\u003e \u003c/sub\u003e, \u003cem\u003eAI\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) and the NIHSS/mRS score\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe significant correlation between the regional decreased metabolic percentage (without the infarction area) evaluated by AIs (AI\u003csub\u003e1\u003c/sub\u003e, AI\u003csub\u003e2\u003c/sub\u003e) and the NIHSS score can be observed. The correlation coefficients for the temporal lobe were 0.3550 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0026, Suppl. Figure\u0026nbsp;2a) and 0.3516 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0028, Suppl. Figure\u0026nbsp;2b) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively. And the correlation coefficients for the parietal lobe were 0.4887 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Suppl. Figure\u0026nbsp;2e) and 0.4866 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Suppl. Figure\u0026nbsp;3f) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively.\u003c/p\u003e \u003cp\u003eThe higher correlation in AI\u003csub\u003e1\u003c/sub\u003e could also be found between the regional percentage of decreased metabolism volume and the mRS score. The correlation coefficients for the temporal lobe were 0.2917 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0143, Suppl. Figure\u0026nbsp;2c) and 0.2874 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0159, Suppl. Figure\u0026nbsp;2d) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively. And the correlation coefficients for the parietal lobe were 0.4504 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Suppl. Figure\u0026nbsp;2g) and 0.4485 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Suppl. Figure\u0026nbsp;2h) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively.\u003c/p\u003e \u003cp\u003eThe decreased metabolic percentage in the whole affected hemisphere as determined by the AI\u003csub\u003e2\u003c/sub\u003e, demonstrated a slightly stronger correlation with NIHSS/mRS scores than the results obtained by the corresponding AI\u003csub\u003e1\u003c/sub\u003e. The correlation coefficients for the NIHSS score were 0.4076 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0005; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee) and 0.4094 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0004; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef) for AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, respectively. And the correlation coefficients for the mRS score were 0.3649 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0019; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg) and 0.3660 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0018; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh), respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifference of hypometabolic state assessed by AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e in pre-follow-up and post-follow-up patients\u003c/h2\u003e \u003cp\u003eIn the follow-up group of fourteen patients, the volume of decreased hypometabolism (excluding the infarction area) was 18.07\u0026thinsp;\u0026plusmn;\u0026thinsp;12.53 and 17.98\u0026thinsp;\u0026plusmn;\u0026thinsp;12.46, respectively, as assessed by the AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e methods (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Moreover, the percentage of decreased hypometabolism was 4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25 and 4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23, respectively (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The reduction in hypometabolic volume, as assessed by the AI\u003csub\u003e1\u003c/sub\u003e method, and its hemispheric percentage on the affected side, was both greater than that observed by the AI\u003csub\u003e2\u003c/sub\u003e (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provide a detailed comparison of the before and after follow-up evaluations conducted using the AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e. In addition, there were significant differences between the pre- and post-follow-up patients in terms of NIHSS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0047) and mRS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) scores (show in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Moreover, the decreased metabolism observed in the remaining five patients in the follow-up group exhibited an increase, accompanied by a notable improvement in their NIHSS/mRS scores.\u003c/p\u003e \u003cp\u003e \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\u003e\u003cb\u003eThe hypometabolic volume and percentage in the affected hemisphere of the pre- and post-follow-up (N\u0026thinsp;=\u0026thinsp;14)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAI\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAI\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-follow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-follow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePre-follow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePost-follow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e187.35\u0026thinsp;\u0026plusmn;\u0026thinsp;41.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e169.28\u0026thinsp;\u0026plusmn;\u0026thinsp;39.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e18.07\u0026thinsp;\u0026plusmn;\u0026thinsp;12.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e189.17\u0026thinsp;\u0026plusmn;\u0026thinsp;41.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e171.18\u0026thinsp;\u0026plusmn;\u0026thinsp;39.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e17.98\u0026thinsp;\u0026plusmn;\u0026thinsp;12.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e47.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e43.17\u0026thinsp;\u0026plusmn;\u0026thinsp;9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e48.30\u0026thinsp;\u0026plusmn;\u0026thinsp;10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e43.66\u0026thinsp;\u0026plusmn;\u0026thinsp;9.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation was shown for the hypometabolic volume and percentage in the affected hemisphere evaluated by the AI (AI\u003csub\u003e1\u003c/sub\u003e, AI\u003csub\u003e2\u003c/sub\u003e). The p value was shown for the statistically significant difference between the results of pre-follow-up and post-follow-up patients.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003e\u003cb\u003eThe NIHSS and mRS scores of pre-follow-up and post-follow-up patients (N\u0026thinsp;=\u0026thinsp;14)\u003c/b\u003e\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 \u003cp\u003eClinical score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-follow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-follow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5 (1, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (0, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe NIHSS and mRS scores were shown as median value (range). The p value was shown for the statistically significant difference between the clinical scores (NIHSS/mRS) from pre-follow-up and post-follow-up patients respectively. NIHSS, National Institutes of Health Stroke Scale; mRS, Modified Rankin Scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aims to compare two AI methods in assessing decreased [\u003csup\u003e18\u003c/sup\u003eF]FDG metabolism for reflecting clinical neurological function in patients with ICVD, and to explore its value in research and clinical settings. The results demonstrated that the volume and percentage of decreased metabolism in the lobes on affected side, excluding the infarct area, attained through AI\u003csub\u003e2\u003c/sub\u003e calculation consistently greater than those achieved through AI\u003csub\u003e1\u003c/sub\u003e. The correlation between the volume of decreased metabolism in the affected cerebral hemisphere, as calculated by AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, and the clinical score can be observed. The improved follow-up patients showed more pronounced metabolic improvement as assessed by AI\u003csub\u003e1\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eThe brain is a highly metabolically active organ that primarily utilizes glucose as its source of energy. [\u003csup\u003e18\u003c/sup\u003eF]FDG has been widely employed in PET systems as a glucose analogue, facilitating valuable insights into cerebral glucose metabolism and functional status[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Yuan H et al.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] reported a consistently elevated metabolism at the peri-ischemic zone during acute cerebral ischemia, suggesting the presence of viable tissues that can be salvaged with reperfusion. Nagasawa H et al.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]observed significant decreases in cerebral glucose metabolic rate (CMRGlu) in areas without lesions using [\u003csup\u003e18\u003c/sup\u003eF]FDG PET in chronic ischemic cerebrovascular disease. In our study, we employed [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/MR to simultaneously acquire precise structural data with excellent tissue contrast, as well as metabolic and functional information. The AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e was both employed to assess cerebral metabolism in patients presenting with unilateral stenosis or occlusion of the internal carotid or middle cerebral arteries, and the results demonstrated a notable decrease in the metabolic activity of the brain tissue (without the infarct lesion) when compared to the contralateral side.\u003c/p\u003e \u003cp\u003eThe AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e computations have been employed in many prior investigations that have focused on the research of ICVD, primarily for investigations evaluating asymmetry in the structure of brain regions, cerebral blood flow, and cerebral metabolism[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The present study focuses on the comparative assessment of the two AIs in evaluating cerebral metabolic reduction on the affected side. We found that the AI\u003csub\u003e2\u003c/sub\u003e method consistently yielded larger estimates for both the volume and the regional percentage of decreased brain metabolism on the affected side, in comparison to the AI\u003csub\u003e1\u003c/sub\u003e method. It possible to interpret this in terms of the meaning expressed by the structure of the two formulas. It can be observed that the numerator of the two formulas, AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, is identical, while the denominator of both is constituted by the SUVR value of the contralateral cerebral hemisphere and the average SUVR value of the bilateral cerebral hemispheres, respectively. Furthermore, the average SUVR value of the two cerebral hemispheres is less than that of the contralateral cerebral hemisphere due to the presence of brain tissue around the infarct foci in the affected hemisphere, which undergoes less metabolism than that of the contralateral cerebral hemisphere. It can therefore be surmised that for a given patient with unilateral internal carotid or middle cerebral artery stenosis and occlusion, the asymmetry calculated by AI\u003csub\u003e2\u003c/sub\u003e will be greater than that obtained by the AI\u003csub\u003e1\u003c/sub\u003e method. This will consequently result in a larger volume and percentage of metabolic decompensation being assessed by AI\u003csub\u003e2\u003c/sub\u003e than by AI\u003csub\u003e1\u003c/sub\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrior research has identified a notable correlation between the magnitude of the AI value or its evaluation of diminished cerebral metabolism and clinical neurological function and prognosis in patients with ischemic cerebrovascular disease across both the subacute and chronic phases[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Cui et al.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]identified a significant correlation between the preoperative NIHSS score and the AI\u003csub\u003e1\u003c/sub\u003e value in patients undergoing [\u003csup\u003e18\u003c/sup\u003eF]FDG PET imaging. Yu et al.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]performed [\u003csup\u003e18\u003c/sup\u003eF]FDG PET imaging for investigating changes in metabolism before and after bypass surgery in patients with chronic ischemia. And they demonstrated that the AI\u003csub\u003e2\u003c/sub\u003e method can be used to assess the difference in metabolism between the bilateral hemispheres whose declination correlated significantly with the surgical therapy, and suggested the uptake of [\u003csup\u003e18\u003c/sup\u003eF]FDG in the hypoperfusion region surrounding the ischemic core is a sensitive indicator for predicting ischemia. Our study also revealed a significant correlation between decreased cerebral metabolism, as evaluated through both AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, and NIHSS/mRS scores. In light of the fact that the NIHSS score is of great value for the monitoring of patients' progress and the planning of rehabilitation or long-term care, and that the mRS score is highly relevant to clinicians and patients contemplating intervention[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], we are of the opinion that the assessment of cerebral metabolism using both AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e methods has the potential to achieve clinical status assessment.\u003c/p\u003e \u003cp\u003eMoreover, although it was mentioned above that the AI\u003csub\u003e2\u003c/sub\u003e method generally yielded larger measurements of decreased metabolism compared to the AI\u003csub\u003e1\u003c/sub\u003e method, which might lead us to speculate that AI\u003csub\u003e2\u003c/sub\u003e method might more comprehensively assess the hypometabolism of the brain, our results showed that, in the temporal and parietal lobes, the correlation coefficients of the decreased metabolism volume or its percentage and NIHSS/mRS score assessed by AI\u003csub\u003e1\u003c/sub\u003e method were all higher than that of AI\u003csub\u003e2\u003c/sub\u003e method. Conversely, the AI\u003csub\u003e2\u003c/sub\u003e method showed higher correlation coefficients in other regions. According to the cerebral arterial territories research, this distribution characteristic may be related to our subject selection, with cerebral ischemic attributed to the unilateral steno-occlusion attributed to the internal carotid artery or middle cerebral artery[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, it also probably due to the fact that when assessing metabolic decompensation in the region supplied by the responsible vascular innervation on the affected side, the AI\u003csub\u003e2\u003c/sub\u003e method takes into account the contralateral metabolism, whereas the AI\u003csub\u003e1\u003c/sub\u003e method somewhat relatively disregards this influence. These suggest that the AI\u003csub\u003e1\u003c/sub\u003e method may be more suitable for evaluating the metabolic changes around the infarct area and the clinical status of the unilateral internal carotid artery/middle cerebral artery steno-occlusive disease patients.\u003c/p\u003e \u003cp\u003eAl-Ajlan et al.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]showed that the biological underpinning of the success of endovascular therapy is a reduction in infarct volume. Sobesky et al.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]found that the cerebral hypoperfusion volume, as assessed by the AI, was significantly correlated with infarct volume and with NIHSS score. Another study observed the significant correlation between the AI of cerebral blood flow and SUVR values in the affected cerebral hemisphere of ICVD patients[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Yoon et al.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]reported the significant correlation between the pretreatment of percentage of infarct core area in each brain region and the mRS score in the acute stroke patients. In our study, we found that in the affected cerebral hemispheres, both volume and percentage of metabolic reduction assessed by AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e were significantly correlated with NIHSS/mRS scores, although the volumetric correlation assessed by AI\u003csub\u003e1\u003c/sub\u003e was slightly stronger than the correlation for AI\u003csub\u003e2\u003c/sub\u003e, and the percentage of metabolic reduction assessed by AI\u003csub\u003e2\u003c/sub\u003e in relation to NIHSS/mRS was slightly stronger than the corresponding correlation with AI\u003csub\u003e1\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eSeb\u0026ouml;k M et al.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]observed that the patients with ICVD who exhibited significantly elevated NIHSS/mRS scores demonstrated elevated asymmetry indices based on PET imaging. Zhang H et al.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]demonstrated that both the significant improvements in neurological function and improvements in brain metabolism shown by [\u003csup\u003e18\u003c/sup\u003eF]FDG PET imaging could be found after treatment. Similarly, our study indicated both the improvement of cerebral metabolism assessed by AIs and the decreased NIHSS/mRS score can be found in the fourteen follow-up patients. Furthermore, the metabolic improvement of patients after follow-up as assessed by AI\u003csub\u003e1\u003c/sub\u003e was found to be more remarkable than that assessed by AI\u003csub\u003e2\u003c/sub\u003e, so we proposed that the AI\u003csub\u003e1\u003c/sub\u003e method is more sensitive to the improvement in clinical condition before and after follow-up. Additionally, the hypometabolic area in each region, as assessed by AIs in five other follow-up patients with decreased NIHSS/mRS scores, did not consistently shrink. Considering that the level of glucose utilization correlates with neuronal activity, as showed by previous research, it is important to pay more attention to these follow-up patients or consider adjusting treatment regimens[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe quantification of the extent of decreased metabolism around infarct foci due to unilateral internal carotid artery or middle cerebral artery stenosis-occlusion based on AI the intuitive and objective assessment of the decreased metabolic state of the patient's brain tissue by neuroimagers and clinicians alike. Furthermore, it reflects the metabolic changes of the patient's brain tissue before and after follow-up, which may assist clinicians in making an appropriate treatment decision for the patient's condition. In cases where there is a discrepancy between the patient's clinical scores and the observed changes in brain metabolism, this may indicate the necessity for further evaluation of the patient's disease status or appropriate adjustments to the patient's treatment plan. In this study, we found that the degree of decreased [\u003csup\u003e18\u003c/sup\u003eF]FDG metabolism obtained by both the AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e methods correlated with NIHSS and mRS scores. However, the correlation obtained by the AI\u003csub\u003e1\u003c/sub\u003e method was stronger than that obtained by the AI\u003csub\u003e2\u003c/sub\u003e method in the areas of the internal carotid artery and middle cerebral artery, which are the main blood supply regions. Furthermore, we were able to detect the changes in the patients' cerebral metabolism before and after follow-up more sensitively using the AI\u003csub\u003e1\u003c/sub\u003e method. It is therefore recommended that the use of AI\u003csub\u003e1\u003c/sub\u003e for more superior assessment, when evaluating disease severity and prognosis in patients with ICVD, as well as realize the changes in disease progression, be employed.\u003c/p\u003e \u003cp\u003eOur study has a few limitation. First, the clinical samples were small and consisted of a uniform participant population (unilateral steno-occlusive disease). Second, our follow-up time was relatively short. The future study will improve the follow-up time in both number and duration. Additionally, this study used a semi-quantitative approach to compare the AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e methods. Future research could further explore the absolute quantitative aspects.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the correlation between the clinical scores and the decreased [\u003csup\u003e18\u003c/sup\u003eF]FDG metabolism assessed by AI\u003csub\u003e1\u003c/sub\u003e method appears more significantly reflecting the improvement of these patient\u0026rsquo;s clinical condition before and after follow-up. The assessment of cerebral [\u003csup\u003e18\u003c/sup\u003eF]FDG metabolism in patients with unilateral internal carotid/middle cerebral artery steno-occlusion to reflect clinical neurological function using the AI\u003csub\u003e1\u003c/sub\u003e demonstrated superior performance in comparison to the AI\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We thank Hui Liao for her help in comments of statistical analysis, Hui Liao and Yifei Zhang for their help in the comments of revision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eMZ,JL, YL and BC designed the study; LY, BY, HY, and LM performed the image acquisitions; YL, BC, and YS performed the image analysis; YL and BC interpreted the results and wrote the initial manuscript; MZ\u0026nbsp;and JL reviewed and revised the\u0026nbsp;initial\u0026nbsp;manuscript;\u0026nbsp;all authors contributed to and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study was supported by the National Natural Science Foundation of China (Grant No. 82130058), Project of Beijing Municipal Administration of Hospitals Incubating Program (Grant No. PX2024033), R\u0026amp;D Program of Beijing Municipal Education Commission (KM202410025016), Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (Grant No. CCMU2024ZKYXZ008), and the Natural Science Foundation of Capital Medical University (Grant No. PYZ22048).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e: The authors declare that they have no conflict of interest with regard to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e All research data and computer codes are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCampbell BCV, De Silva DA, Macleod MR et al (2019) Ischaemic stroke. 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J Nuclear Medicine: Official Publication Soc Nuclear Med 56:1774\u0026ndash;1779\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagasawa H, Kogure K, Itoh M, Ido T (1994) Multi-focal metabolic disturbances in human brain after cerebral infarction studied with 18FDG and positron emission tomography. NeuroReport 5:961\u0026ndash;964\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen AR, Friberg HH, Schmidt JF, Hasselbalch SG (1988) Quantitative measurements of cerebral blood flow using SPECT and [99mTc]-d,l-HM-PAO compared to xenon-133. J Cereb Blood Flow Metabolism: Official J Int Soc Cereb Blood Flow Metabolism 8:S69\u0026ndash;S81\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SE, Choi CW, Yoon BW et al (1997) Crossed-cerebellar diaschisis in cerebral infarction: technetium-99m-HMPAO SPECT and MRI. 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Stroke 47:777\u0026ndash;781\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSobesky J, Thiel A, Ghaemi M et al (2005) Crossed cerebellar diaschisis in acute human stroke: a PET study of serial changes and response to supratentorial reperfusion. J Cereb Blood Flow Metabolism: Official J Int Soc Cereb Blood Flow Metabolism 25:1685\u0026ndash;1691\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui B, Zhang T, Ma Y et al (2020) Simultaneous PET-MRI imaging of cerebral blood flow and glucose metabolism in the symptomatic unilateral internal carotid artery/middle cerebral artery steno-occlusive disease. Eur J Nucl Med Mol Imaging 47:1668\u0026ndash;1677\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon W, Baek BH, Lee YY, Kim SK, Kim J-T, Park MS (2021) Association of pretreatment pontine infarction with extremely poor outcome after endovascular thrombectomy in acute basilar artery occlusion. 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NeuroImage 57:45\u0026ndash;54\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-imaging-and-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mibi","sideBox":"Learn more about [Molecular Imaging and Biology](http://link.springer.com/journal/11307)","snPcode":"11307","submissionUrl":"https://www.editorialmanager.com/mibi/default2.aspx","title":"Molecular Imaging and Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ischemic cerebrovascular disease, Asymmetry index, Positron emission tomography, Brain glucose","lastPublishedDoi":"10.21203/rs.3.rs-5319717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5319717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo identify a method of assessing cerebral 2-deoxy-2-[\u003csup\u003e18\u003c/sup\u003eF]fluoro-D-glucose ([\u003csup\u003e18\u003c/sup\u003eF]FDG) metabolism with an asymmetry index (AI) that reflects clinical neurological function in patients with ischemic cerebrovascular disease (ICVD), and to explore its applications and potential value to the ICVD in clinical settings.\u003c/p\u003e\u003ch2\u003eProcedures:\u003c/h2\u003e \u003cp\u003eSeventy patients diagnosed with subacute and chronic ischemic stroke were retrospectively analyzed. All patients underwent [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/MR scans and were assessed using the National Institutes of Health Stroke Scale (NIHSS) and the Modified Rankin Scale (mRS). Following a year of rehabilitation training, nineteen patients underwent a repeat [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/MR scan. The decreased cerebral [\u003csup\u003e18\u003c/sup\u003eF]FDG metabolism region was defined as the AI value greater than 10%. Two voxel-wise AIs, designated as AI\u003csub\u003e1\u003c/sub\u003e and AI\u003csub\u003e2\u003c/sub\u003e, were calculated based on the standardized uptake value ratio (SUVR). The decreased metabolism on affected side accessed by different AI calculation methods were compared. The correlations between the decreased metabolism and the clinical scores were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe volume and percentage of decreased [\u003csup\u003e18\u003c/sup\u003eF]FDG metabolism assessed by AI\u003csub\u003e2\u003c/sub\u003e was larger than that obtained from AI\u003csub\u003e1\u003c/sub\u003e (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The correlation coefficients between the clinical scores and the decreased metabolism in temporal and parietal lobes assessed by AI\u003csub\u003e1\u003c/sub\u003e method were all higher than those from AI\u003csub\u003e2\u003c/sub\u003e. In addition, the improved follow-up patients showed more pronounced metabolic improvement as assessed by AI\u003csub\u003e1\u003c/sub\u003e.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe assessment of cerebral [\u003csup\u003e18\u003c/sup\u003eF]FDG metabolism in patients with unilateral internal carotid/middle cerebral artery steno-occlusion to reflect clinical neurological function using the AI\u003csub\u003e1\u003c/sub\u003e method demonstrated superior performance in comparison to the AI\u003csub\u003e2\u003c/sub\u003e method.\u003c/p\u003e","manuscriptTitle":"Comparison of the correlation between cerebral [18F]FDG metabolism as assessed by two asymmetry indices and clinical neurological score in patients with ischemic cerebrovascular disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 16:47:49","doi":"10.21203/rs.3.rs-5319717/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-12-18T10:22:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-18T10:07:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-24T00:56:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Imaging and Biology","date":"2024-10-23T10:20:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-imaging-and-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mibi","sideBox":"Learn more about [Molecular Imaging and Biology](http://link.springer.com/journal/11307)","snPcode":"11307","submissionUrl":"https://www.editorialmanager.com/mibi/default2.aspx","title":"Molecular Imaging and Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"15f3e588-8bf7-467b-b2b2-afc3ff829690","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T16:00:34+00:00","versionOfRecord":{"articleIdentity":"rs-5319717","link":"https://doi.org/10.1007/s11307-025-02002-7","journal":{"identity":"molecular-imaging-and-biology","isVorOnly":false,"title":"Molecular Imaging and Biology"},"publishedOn":"2025-04-15 15:57:29","publishedOnDateReadable":"April 15th, 2025"},"versionCreatedAt":"2024-12-24 16:47:49","video":"","vorDoi":"10.1007/s11307-025-02002-7","vorDoiUrl":"https://doi.org/10.1007/s11307-025-02002-7","workflowStages":[]},"version":"v1","identity":"rs-5319717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5319717","identity":"rs-5319717","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}