Combined Hyperperfusion with Prominent Veins for the Prediction of Hemorrhagic Transformation in Stroke Patients

Combined Hyperperfusion with Prominent Veins for the Prediction of Hemorrhagic Transformation in Stroke Patients | 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 Combined Hyperperfusion with Prominent Veins for the Prediction of Hemorrhagic Transformation in Stroke Patients Lijun Pan, Juan Wang, Jinyan Zu, Yang Li, Rui Zhang, Yixu Zhao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9279263/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study was aimed to investigated whether hyperperfusion and prominent veins via magnetic resonance using arterial spin-labeling (ASL) and susceptibility weighted imaging (SWI) could help predict hemorrhagic transformation (HT) after reperfusion therapy in subacute ischemic stroke. Methods We retrospectively reviewed 44 patients with unilateral supratentorial infraction who were admitted between 24h and 2 weeks after stroke onset. All patients underwent magnetic resonance imaging (ASL, SWI) after admission. Hyperperfusion areas were defined as regions with a relative cerebral blood flow (rCBF) of ≥ 1.4. Imaging assessments revealed that prominent veins refer to the hypointense signals surrounding the infarct area on SWI, while hypointense hemorrhagic foci in the infarct area can also be visualized by this modality. All clinical parameters were enrolled by two senior neurologists. Results Among the final cohort of 44 patients with subacute ischemic stroke, comparison between the HT group and the no-HT group showed that HT was strongly associated with hyperperfusion (p < 0.001), prominent veins (p = 0.028) and CBF penumbra (OR 0.937, 95% CI 0.903 to 0.973, p < 0.001). Based on ASL and SWI findings, patients were stratified into four distinct subgroups: those with ASL-detected hyperperfusion or no hyperperfusion, combined with the presence or absence of prominent veins on SWI. 6 patients presented with both prominent veins and hyperperfusion, and all of these 6 patients (100%) developed HT. In contrast, only 5% of patients without prominent veins nor hyperperfusion developed HT. Furthermore, regardless of hyperperfusion status, the Initial National Institute of Health Stroke Scale (NIHSS) score was significantly correlated with prominent veins. Conclusions The presence of hyperperfusion and/or prominent veins increases the likelihood of HT in ischemic stroke. Combined SWI and ASL imaging serves as a valuable tool for predicting HT in patients with supratentorial ischemic stroke. Arterial Spin-labeling Hemorrhagic Transformation Hyperperfusion Prominent veins Susceptibility-weighted Imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Hemorrhagic transformation (HT) is a serious and potentially life-threatening complication following reperfusion therapy in ischemic stroke patients, with currently limited reliable predictive markers. Accumulating evidence reveals that loss of cerebral blood flow (CBF) autoregulation and severe blood-brain barrier (BBB) disruption may lead to HT in ischemic stroke patients treated with intravenous thrombolysis or those who have undergone endovascular intervention reperfusion therapy[ 1 ]. Identifying patients at high risk for HT is crucial for adjusting reperfusion strategies, preventing HT and progressive infarction, and reducing disability and mortality[ 2 ]. Well-established imaging techniques, contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI), have been widely utilized for identifying BBB as a means to predict HT. However, the use of contrast agents and exposure to CT radiation have limited its clinical application. Arterial spin-labeling (ASL), a noninvasive perfusion-weighted MRI technique, has emerged as a valuable tool for assessing cerebral hemodynamics [ 3 ]. Our previous study demonstrated that crossed cerebellar diaschisis detected by ASL manifested as decreased CBF in the contralateral hemisphere compared to the ipsilateral region in supratentorial infarct patients[ 4 ]. Previous ASL investigations observed a significant correlation between hyperperfusion and HT, suggesting that ASL hyperperfusion maps may offer predictive insights for HT in acute ischemic stroke[ 1 , 5 ]. These results suggested that ASL can be used as a sensitive tool for early diagnosis of hyperperfusion. Susceptibility-weighted imaging (SWI), a modern MR technique, is much more sensitive for the detection of HT than either non-contrast CT scan or T2 gradient echo sequences. This greater sensitivity is important in the acute phase of ischemic stroke[ 6 ]. In addition to visualizing HT, SWI can also provide detailed visualization of prominent veins, microbleeds and the susceptibility vessel sign - findings that prove valuable for assessing stroke severity and predicting prognosis in ischemic stroke[ 7 ]. By virtue of its capacity to visualize the intricate venous architecture, SWI imaging modality complements the cerebral perfusion data derived from ASL, thereby facilitating a more comprehensive understanding of the brain’s vascular system in its entirety [ 8 ]. Prominent asymmetrical cortical veins in the infarct region, which possibly represent increased oxygen extraction fraction (OEF) and collateral circulation in the penumbra of the infarct[ 9 – 11 ]. A preclinical study in nonhuman primates also observed prominent vein in collateral-rich group but not in collateral-poor group by using SWI, meanwhile, the absence of prominent vein is indicative of larger infarct volumes and poorer prognosis[ 12 ]. Although these studies have established that the presence of prominent veins is linked to adequate collateral circulation in AIS patients, the direct associated between prominent veins and HT not been definitively established, and further investigations are warranted. Interestingly, previous research suggest that subacute pathophysiological processes contribute more significantly to HT development than acute events in rat models of occlusion [ 13 ]. Further studies indicate that the progression of HT may depend on the timing of hyperperfusion; late postischemic HP is often associated with tissue necrosis, whereas early postischemic HP might be non-detrimental or even beneficial[ 14 ]. We hypothesized that patients with hyperperfusion and prominent veins exhibit higher rates of HT compared with those without hyperperfusion and prominent veins in subacute stroke. Therefore, the aim of this study was to develop a technique for quantitative assessment of hyperperfusion using pre-treatment ASL, prominent veins using SWI and to investigate its value in the prediction of subsequent HT in AIS patients. Methods Patients This study was approved by Local Institution Review Board of Renji Hospital, Shanghai Jiaotong University School of Medicine. We analyzed Images from patients presenting with subacute ischemic stroke (defined as 24 hours to 2 weeks post-onset) between August 2022 and July 2025(n = 154). All patients underwent a similar imaging protocol (including ASL and SWI) upon admission. Exclusion criteria were: (1) infarcts in the brainstem, cerebellum, or bilateral supratentorial regions; (2) history of intracranial tumor, head trauma, subarachnoid hemorrhage, arteriovenous malformation, or brain surgery; (3) abnormalities in the posterior fossa on T1, T2, and diffusion-weighted MR images; and (4) MR angiography showing angiopathy of vertebral basilar artery and the major branches. After screening, 44 patients were included. The selection flowchart is shown in Fig. 1 . Data on demographic and clinical parameters were collected from the electronic medical records; these included age, sex, prior antiplatelet agent use, time interval between stroke onset and the MRI. Baseline National Institutes of Health Stroke Scale (NIHSS) scores at admission were assessed by two experienced neurologists independently. All patients included in our study present were not eligible for thrombolysis or endovascular treatments due to the extended treatment window and presence of contraindications. Reperfusion therapies primarily included antiplatelet agents, medications to promote circulation, and neuroprotective strategies. MR imaging All MRI scans were performed on 3-T platform (HDxt; General Electric Medical Systems, Waukesha, WI, USA) using an eight-channel phased-array head coil. The standard stroke imaging protocol included T1-weighted, T2-weighted, MR angiography, SWI and ASL sequences. Pseudo-continuous ASL perfusion images were acquired using 3D fast spin-echo acquisition with background suppression and a post-labeling delay of 1500 ms. Other parameters were: repetition time/echo time = 4601/10.5 ms, field of view = 240×240 mm 2 , matrix size = 128×128, 38 slices with a slice thickness of 4 mm, and number of averages = 3. SWI parameters were: TR/TE = 42.6/3.2ms, flip angle = 12°, slice thickness = 4 mm with a 2-mm slice gap, 60 sections per slab, matrix = 256 × 256, FOV = 220 mm × 220 mm 2 . Imaging data processing Two independent, experienced readers manually drew circular regions of interest (ROI) on ASL maps. Mean CBF values were obtained from the ipsilateral ROI and its mirror region in the contralateral hemisphere. Relative CBF (rCBF) was calculated as the ratio of ipsilateral to contralateral CBF(rCBF = CBF ipsilateral /CBF contralateral ). A region exhibiting a relative cerebral blood flow (rCBF) value of on pre-treatment arterial spin labeling (ASL) perfusion maps was identified as a hyperperfusion area. HP was defined as rCBF ≥ 1.4[ 15 ]. HT was defined on SWI as multiple punctate hypointensities with no continuity within the infarct area. Prominent veins was assessed as prominent vessels and hypointense signals around the infarct area by comparing to contralateral region[ 16 ]. Statistical analysis Statistical analyses were conducted using SPSS 27.0 (IBM Corp., Armonk, NY, USA). Continuous normally distributed variables are expressed as mean ± standard deviation (SD). The independent sample t-test or Mann–Whitney U test (when data were not normally distributed) was used to compare continuous variables); Categorical variables were compared by the chi-square test or fisher’s exact text was used for small sample sizes (when the expected call frequency was < 5). A two-tailed p-value < 0.05 was considered statistically significant. Results A total of 44 patients with subacute stroke were included in the final data analysis, of these patients, 15(34%) developed HT, as confirmed by SWI. The median lengths of time interval between stroke onset and the MRI were 3.57 ± 1.59d. Hyperperfusion was detected in 19 (43%) patients, whereas prominent veins were identified in 11 patients (25%). Patients were divided into two groups based on the presence or absence of HT: the HT group and the non-HT group. Univariate analysis showed that hyperperfusion (p < 0.001) was detected in 13 patients (87%) of the HT group, which was significantly higher than the 6 patients (21%) in the non-HT group, the Phi Coefficient (φ) was 0.631 (p < 0.001); a similar group difference was also observed in prominent veins indices (47% in the HT group,14% in the non-HT group, p = 0.028), the Phi Coefficient (φ) was 0.36 (p = 0.017). ASL-derived cerebral blood flow (CBF) analysis revealed the similar CBF in the core-ipsilateral (CBF core ) between the two groups. However, CBF in the penumbral-ipsilatera CBF (CBF penumbra ) was significantly higher in the HT group compared to non-HT group (p < 0.001), On multivariate logistic analysis, CBF penumbra (OR 0.937, 95% CI 0.903 to 0.973, p < 0.001) was found to be independent risk factors for the status of HT, suggesting that CBF penumbra , rather than CBF core , may be a more significant contributor to HT. Furthermore, no significant difference in NIHSS scores was noted between the two subgroups. Detailed data of patients are shown in Table 1 . Table 1 Patient characteristics with and without hemorrhagic transformation Characteristics HT (n = 15) No HT (n = 29) p- value Phi Coefficient (φ) p- value Age(years) 63.5 ± 15.1 61.6 ± 12.2 0.654 Male sex,n(%) 10(67%) 20(69%) 0.877 Onset to MRI(days) 3.53 ± 1.58 3.59 ± 1.75 0.900 Infarct volumne(mm 3 ) 18933 ± 19350 26674 ± 46697 0.528 NIHSS admission 5.6 ± 3.7 5.8 ± 3.6 0.854 NIHSS discharge 4.2 ± 2.3 4.7 ± 3.3 0.150 NIHSS change 1.4 ± 1.7 1.1 ± 2.0 0.540 Hypertension,n(%) 12(80%) 3(9.68%) 0.613 Diabetes,n(%) 8(53%) 1(3.23%) 0.880 Hyperlipidemia(%) 0(0%) 0(0%) 0.601 Stroke,n(%) 1(7%) 2(6.45%) 0.385 Atrial fibrillation,n(%) 2(13%) 4(12.9%) 0.813 Smoking,n(%) 6(40%) 16(53.33%) 0.950 Drinking,n(%) 3(20%) 5(16.13%) 0.589 Prominent veins,n(%) 7(47%) 4(14%) 0.028* 0.36 0.017* Hyperperfusion,n(%) 13(87%) 6(21%) <0.001* 0.631 <0.001* CBF core (ml/100g/min) 25.8 ± 16.3 23.2 ± 24.8 0.707 CBF penumbra (ml/100g/min) 66 ± 33.2 30.7 ± 15.9 <0.001* HT: Hemorrhagic Transformation; NIHSS: Initial National Institute of Health Stroke Scale; CBF: cerebral blood flow. CBF core : CBF in the core- ipsilateral; CBF penumbra : CBF in the penumbra-ipsilateral. Values are presented as mean ± standard deviation or n (%). p value by χ 2 text or Mann-Whitney U test. * Results indicate a significance difference at p < 0.05. According to findings from ASL and SWI: patients were stratified into four distinct subgroups in Table 2 , specifically: non-hyperperfusion combined with non-prominent veins (Fig. 2 . n = 20, 45%), hyperperfusion combined with non-prominent veins (Fig. 3 .n = 13, 30%), non-hyperperfusion combined with prominent veins (Fig. 4 . n = 5, 11%), hyperperfusion combined with prominent veins (Fig. 5 . n = 6, 14%) (Table 2 ). HT rates (p < 0.001) and baseline NIHSS scores (p = 0.017) were found to be significantly associated with the status of prominent veins and hyperperfusion. Other demographic and clinical parameters showed no significant differences among the groups. Table 2 Patient characteristics stratified by prominent veins and hyperperfusion status characteristics No hyperperfusion (n = 25) Hyperperfusion(n = 19) p value No prominent veins (n = 20) prominent veins (n = 5) No prominent veins (n = 13) Prominent veins (n = 6) Age(year) 62.8 ± 13 58.6 ± 9.21 64.3 ± 13.7 59.2 ± 12 0.877 Male sex-(%) 14(67%) 4(67%) 7(67%) 3(69%) 0.573 Infarct volumne(mm3) 24901 ± 43560 25226 ± 22692 23431 ± 45868 21465 ± 26329 0.894 NIHSS admission 5.3 ± 3.5 9.8 ± 7.8 3.9 ± 1.5 8 ± 4.1 0.017* NIHSS discharge 4.4 ± 3.2 7.8 ± 3.76 3 ± 1.58 5.5 ± 2.34 0.061 NIHSS change 0.85 ± 1.98 2 ± 2.4 0.92 ± 1.18 2.5 ± 2.16 0.282 Hypertension(%) 15(75%) 4(80%) 9(69%) 5(83%) 0.613 Diabetes(%) 5(25%) 1(20%) 8(61%) 3(50%) 0.135 Hyperlipemia(%) 1(5%) 0(0%) 1(8%) 0(0%) 0.734 stroke(%) 2(10%) 1(20%) 2(15%) 1(17%) 0.924 Atrial fibrillation(%) 2(10%) 0(0%) 0(0%) 2(33%) 0.104 Smoking(%) 11(55%) 2(40%) 4(30%) 3(50%) 0.570 Drinking(%) 8(40%) 2(40%) 3(23%) 1(17%) 0.585 HT(%) 1(5%) 1(20%) 7(54%) 6(100%) <0.0001* HT: Hemorrhagic Transformation; NIHSS: Initial National Institute of Health Stroke Scale. Values are mean ± standard deviation or n (%). p value by χ 2 text or Kruskal-Wallis test. *Results indicate a significance difference at p < 0.05. Subsequent analyses revealed that the HT rate reached 100% in the hyperperfusion combined with prominent veins subgroup, in contrast to a mere 5% in the non-hyperperfusion combined with non-prominent veins subgroup(p < 0.001) (Fig. 6 ). Meanwhile the HT rate was 54% in the hyperperfusion combined with non-prominent veins subgroup and 20% in the non-hyperperfusion combined with prominent veins subgroup. Among both prominent veins and non-prominent veins patients, hyperperfusion was associated with a significantly higher HT rate (p = 0.028 and p = 0.015, respectively). Additionally, within both hyperperfusion and non-hyperperfusion subgroups, patients with prominent veins had significantly higher baseline NIHSS scores than non- prominent veins subgroup (p = 0.022 and p = 0.033, respectively). Discussion The main finding of our study was that the presence of prominent veins, hyperperfusion and CBF penumbra had strong association with HT. Multivariate logistic analysis indicated that the CBF penumbra of hyperperfusion was an independent risk factor for predicting HT. We assessed hyperperfusion on ASL maps either within or around the hypoperfused region compared with the homologous contralateral hemisphere. significantly higher CBF was observed in the ipsilateral penumbra of HT group, contrasting with non-HT group. Prior studies reported hyperemic lesions, identified as hyperperfusion on ASL, in ischemic stroke patients[ 17 ]. Our findings support the notion that ischemic brain tissue is more susceptible to BBB disruption and CBF dysregulation, increasing HP risk after reperfusion [ 18 ]. In addition, unlike previous studies that focused on the presence of hyperperfusion, a noteworthy study observed that higher CBFmax values are more likely to induce HT, whereas mean CBF was not an independent risk factor for HT. The underlying mechanism may be that patients with hyperperfusion who subsequently developed HT sustained more severe cortical injury and BBB disruption, thereby facilitating the leakage of labeled spins from the arterial lumen into the extracellular space. Consequently, hyperperfused patients who went on to develop HT exhibited higher CBF compared with their hyperperfused counterparts who did not progress to HT. Ping et al reported that cortical and medullary prominent veins were associated with poor functional outcomes, particularly in patients who received thrombolytic therapy. However, prominent veins were not linked to poor functional outcomes in those treated conservatively, nor were they associated with an increased risk of HT. Notably, this conclusion is inconsistent with our findings[ 19 ]. Our study identified prominent veins in 11 patients (25%), with a significant association with HT (p = 0.028, φ Coefficient = 0.36), which suggested that prominent veins adjacent to the infarcted regions may be indicative of an increased susceptibility to HT. Another study identified the Asymmetric Prominent Veins-based Alberta Stroke Program Early Computed Tomography Score (APVs-ASPECTS) as an independent predictor of both parenchymal hemorrhage (PH) after mechanical thrombectomy, but its predictive power is only moderate [ 20 ]. A low APVs-ASPECTS score may thus indicate a large volume of ischemic brain tissue, the presence of cerebral infarction and inadequate collateral circulation, all of which are potential contributors to an elevated risk of hemorrhage after mechanical thrombectomy. Subsequent studies have additionally demonstrated that all patients with both hyperperfusion and prominent veins suffered HT after reperfusion therapy, a rate significantly higher than in those without both prominent veins and hyperperfusion. Interestingly, in negative hyperperfusion patients, prominent veins presence did not significantly affect HT rates, aligning previous reports questioning the independent predictive value for HT or clinical progression [ 6 ]. Thus, while prominent veins alone may not reliably predict HT, its co-concurrence with hyperperfusion demonstrates high predictive value. In our cohort, HT manifested in 15 patients, which was associated with higher mortality and disability. However, when we investigated other potential risk factors for HT, we found no significant associations with demographic or clinical parameters, including changes in NIHSS scores. In our study, the over-windowing and the presence of contraindications are probably the main reasons for patients not performing revascularization therapies. This population had more underlying diseases and a poorer overall prognosis. Meanwhile, the administered treatments included antiplatelet agents, circulation-promoting drugs, and neuroprotective therapy, which may explain the observed stability of NIHSS scores[ 19 ]. Consistent with prior research [ 21 ],we observed hypoperfusion in the infract core.HT may result from severe ischemic damage to vessel walls, leading to BBB disruption and leakage[ 22 ],potentially explaining the lower CBF in the infarct core of the HT+ group. This study, however, has some limitations. First, the small sample size, which may limit generalizability; Second, lack of data on patients receiving revascularization therapies and absence of acute phase comparison; Third, the following clinical effect and imaging review of patients for 1–3 month after discharge should be completed. Conclusions Patients exhibiting hyperperfusion are at an elevated risk of HT regardless of prominent veins status. Hyperperfusion appears to be an independent risk factor for HT, but not prominent veins. SWI and ASL are valuable noninvasive tools for predicting HT in supratentorial stroke patients following reperfusion therapy and further external verification is required in the future. Declarations Funding Sources This work was supported by the Foundation of Zhejiang Provincial Medical and Health Science and Technology Plan Project(2022RC070); Interdisciplinary Project of Shanghai Jiaotong University (YG2021QN40); and Clinical Research Projects of Renji Hospital Campus, Shanghai Jiaotong University School of Medicine (LY2024-186-B). Ethical approval This study was approved from the shanghai jiaotong university school of medicine, renji hospital ethics Committee (RA2020-543). This study respects the life, health, dignity, self-determination, privacy and confidentiality of the subjects’ personal information of all participants. All methods were carried out in accordance with the guidelines and provisions of the Declaration of Helsinki. Written informed consent was obtained from all participants. Conflict of Interest Statement The authors declare no conflict of interest. Author Contributions YL,TYW and YXZ performed the MRI experiments. JW, LJP and JYZ collected imaging data. RZ collected clinical NIHSS score. PLJ and JW did the statistical analysis and wrote the manuscript. ZAC did a critical revision of manuscript. All authors read and approved the final manuscript. Data Availability Statement The data that support the findings of this study are not publicly available due to the privacy of the research participants but are available from the corresponding author (ZAC) upon reasonable request after signing a data transfer agreement. References Yu S, Liebeskind DS, Dua S, Wilhalme H, Elashoff D, Qiao XJ, et al. Postischemic hyperperfusion on arterial spin labeled perfusion MRI is linked to hemorrhagic transformation in stroke. J Cereb Blood Flow Metab. 2015;35:630–7. https://doi.org/10.1038/jcbfm.2014.238 . Kulmatytskyi AV, Bilobryn MS, Makarovska MB. Hemorrhagic transformation of cerebral infarction: risk factors, diagnosis, and new approaches to treatment. INJ. 2024;20:28–33. https://doi.org/10.22141/2224-0713.20.1.2024.1043 . Kobata T, Yamasaki T, Omori K, Ogawa K. Influence of the Imaging Method on Regional Cerebral Blood Flow Value in Arterial Spin Labeling (ASL): Comparison of Pulsed-ASL with Two-dimensional Acquisition and Pseudo-continuous-ASL with 3D Spiral Acquisition. Nippon Hoshasen Gijutsu Gakkai Zasshi. 2022;78:969–77. https://doi.org/10.6009/jjrt.2022-1265 . Wang J, Pan L-J, Zhou B, Zu J-Y, Zhao Y-X, Li Y, et al. Crossed cerebellar diaschisis after stroke detected noninvasively by arterial spin-labeling MR imaging. BMC Neurosci. 2020;21:46. https://doi.org/10.1186/s12868-020-00595-z . Niibo T, Ohta H, Miyata S, Ikushima I, Yonenaga K, Takeshima H. Prediction of Blood-Brain Barrier Disruption and Intracerebral Hemorrhagic Infarction Using Arterial Spin-Labeling Magnetic Resonance Imaging. Stroke. 2017;48:117–22. https://doi.org/10.1161/STROKEAHA.116.013923 . Verclytte S, Fisch O, Colas L, Vanaerde O, Toledano M, Budzik J-F. ASL and susceptibility-weighted imaging contribution to the management of acute ischaemic stroke. Insights Imaging. 2017;8:91–100. https://doi.org/10.1007/s13244-016-0529-y . Tan R, Spring KJ, Killingsworth M, Bhaskar S. Comparative Diagnostic and Prognostic Performance of SWI and T2-Weighted MRI in Cerebral Microbleed Detection Following Acute Ischemic Stroke: A Meta-Analysis and SPOT-CMB Study. Medicina. 2025;61:1566. https://doi.org/10.3390/medicina61091566 . Kalchev E. Generalized Venous Prominence on Susceptibility-Weighted Imaging Correlates With Global Cerebral Blood Flow Decline. Cureus. 2024. https://doi.org/10.7759/cureus.56272 . Huang P, Chen C-H, Lin W-C, Lin R-T, Khor G-T, Liu C-K. Clinical applications of susceptibility weighted imaging in patients with major stroke. J Neurol. 2012;259:1426–32. https://doi.org/10.1007/s00415-011-6369-2 . Huang K, Liu J, Yun W, Cao Y, Zhang M. The role of asymmetrical prominent veins sign in early neurological deterioration of acute ischemic stroke patients. Front Neurol. 2022;13:860824. https://doi.org/10.3389/fneur.2022.860824 . Sheikhbabaei M, Gharekhanloo F, Khazaei M, Pirdehghan A. Comparison of Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging in the Estimation of the Amount of Reversible Ischemic Tissue (Penumbra). SN Compr Clin Med. 2022;5:36. https://doi.org/10.1007/s42399-022-01274-2 . Parthasarathy R, Kate M, Rempel JL, Liebeskind DS, Jeerakathil T, Butcher KS, et al. Prognostic Evaluation Based on Cortical Vein Score Difference in Stroke. Stroke. 2013;44:2748–54. https://doi.org/10.1161/STROKEAHA.113.001231 . Neumann-Haefelin T, Kastrup A, de Crespigny A, Ringer TM, Sun GH, Yenari MA, et al. MRI of subacute hemorrhagic transformation in the rat suture occlusion model. NeuroReport. 2001;12:309–11. https://doi.org/10.1097/00001756-200102120-00025 . Marchal G, Young AR, Baron JC. Early postischemic hyperperfusion: pathophysiologic insights from positron emission tomography. J Cereb Blood Flow Metab. 1999;19:467–82. https://doi.org/10.1097/00004647-199905000-00001 . Huang J, Hao P, Chen Z, Deng K, Liu B, Xu Y. Quantitative assessment of hyperperfusion using arterial spin labeling to predict hemorrhagic transformation in acute ischemic stroke patients with mechanical endovascular therapy. Eur Radiol. 2023;34:579–87. https://doi.org/10.1007/s00330-023-10007-5 . Huang P, Chen C-H, Lin W-C, Lin R-T, Khor G-T, Liu C-K. Clinical applications of susceptibility weighted imaging in patients with major stroke. J Neurol. 2012;259:1426–32. https://doi.org/10.1007/s00415-011-6369-2 . Huang J, Hao P, Chen Z, Deng K, Liu B, Xu Y. Quantitative assessment of hyperperfusion using arterial spin labeling to predict hemorrhagic transformation in acute ischemic stroke patients with mechanical endovascular therapy. Eur Radiol. 2023;34:579–87. https://doi.org/10.1007/s00330-023-10007-5 . Shiraishi K, Wang Z, Kokuryo D, Aoki I, Yokoyama M. A polymeric micelle magnetic resonance imaging (MRI) contrast agent reveals blood-brain barrier (BBB) permeability for macromolecules in cerebral ischemia-reperfusion injury. J Control Release. 2017;253:165–71. https://doi.org/10.1016/j.jconrel.2017.03.020 . Lu P, Cui L, Zhao X. The Prognostic Impact of Susceptibility-Weighted Imaging Prominent Veins in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis. NDT. 2021;17:3069–79. https://doi.org/10.2147/NDT.S331874 . Li Z, Jiang H, Yan R, Li H, Li Z, Bai Y, et al. Prediction of parenchymal hematoma after mechanical thrombectomy by asymmetrical prominent veins: a retrospective cohort study based on susceptibility-weighted imaging. Quant Imaging Med Surg. 2025;15:7224–34. https://doi.org/10.21037/qims-2025-262 . Suh CH, Jung SC, Cho SJ, Kim D, Lee JB, Woo D-C, et al. Perfusion CT for prediction of hemorrhagic transformation in acute ischemic stroke: a systematic review and meta-analysis. Eur Radiol. 2019;29:4077–87. https://doi.org/10.1007/s00330-018-5936-7 . Spronk E, Sykes G, Falcione S, Munsterman D, Joy T, Kamtchum-Tatuene J, et al. Hemorrhagic Transformation in Ischemic Stroke and the Role of Inflammation. Front Neurol. 2021;12:661955. https://doi.org/10.3389/fneur.2021.661955 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9279263","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624811819,"identity":"5b2dcf6c-2a7a-43f5-bffb-82fa435c4068","order_by":0,"name":"Lijun Pan","email":"","orcid":"","institution":"Renji Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Pan","suffix":""},{"id":624811820,"identity":"da032d4e-5b65-45e4-9e7d-c7cdb36c1e71","order_by":1,"name":"Juan Wang","email":"","orcid":"","institution":"Renji Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Wang","suffix":""},{"id":624811821,"identity":"1ac5b3ee-b7ec-433f-a670-86d7a1a05c1c","order_by":2,"name":"Jinyan Zu","email":"","orcid":"","institution":"Renji Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Jinyan","middleName":"","lastName":"Zu","suffix":""},{"id":624811822,"identity":"f26e1b84-5fb2-48dd-9883-762476103ab4","order_by":3,"name":"Yang Li","email":"","orcid":"","institution":"Renji Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":624811823,"identity":"e9cf8b23-826a-446c-8a03-d951e37ce651","order_by":4,"name":"Rui Zhang","email":"","orcid":"","institution":"Renji Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhang","suffix":""},{"id":624811824,"identity":"c8c66e90-e936-4805-9cbd-8cd9b7154724","order_by":5,"name":"Yixu Zhao","email":"","orcid":"","institution":"Renji Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Yixu","middleName":"","lastName":"Zhao","suffix":""},{"id":624811825,"identity":"bdc930f5-058c-482a-a283-41f66dc0c303","order_by":6,"name":"Tianyao Wang","email":"","orcid":"","institution":"Renji Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Tianyao","middleName":"","lastName":"Wang","suffix":""},{"id":624811826,"identity":"afebf12e-ff7a-4ced-a2fc-55001ff6482f","order_by":7,"name":"Zengai Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACCRiDmfnAgQ8/SNPClnhwZg9JWhh4jA9zsBGhg39287GHPyru2PW383w4zMDDIM8vdoCAJXeOpRtInHmWPOMw74bDBRYMhjNnJ+DXYiCRYyZh2HY4mQGkZQYPQ4LBbYJa8r9JJP47nCx/mOfBYR42orTksEkcbDhsZ3CYh4E4LRI30swkG44dTjA8zGYADGQJwn7hn5H8TPJHzWF7ufOHH3/48MNGnl+agBYYSGyA2kqcchCwJ17pKBgFo2AUjDgAAOt4Rh5NI3eEAAAAAElFTkSuQmCC","orcid":"","institution":"Renji Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Zengai","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-03-31 11:41:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9279263/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9279263/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107500710,"identity":"2343d890-e029-408f-b1f6-b0057c6d6489","added_by":"auto","created_at":"2026-04-22 05:49:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289857,"visible":true,"origin":"","legend":"\u003cp\u003ePatients flow-chart of the cohort.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9279263/v1/e63e4ca2343ebc18d1af1cb3.png"},{"id":107705633,"identity":"e2611ca4-5bf7-4141-97d6-36955d09fa62","added_by":"auto","created_at":"2026-04-24 09:14:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":478505,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative AIS cases with positive prominent vessels and positive hyperintense. 1. (a-d) A 61-year-old man and (e-f) a 34-year-old man with supratentorial stroke in the right basal ganglia for 3 and 5days respectively. DWI (a,e) showed the hyperintense acute infarct in the right basal ganglia. ASL scanned (b,f) showed hyperperfusion (red arrows) and SWI scanned (c,g) showed prominent vessels (black arrows) around corresponding DWI lesions. The SWI scanned (d,h) showed hemorrhagic transformation within the infarct (white arrows).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9279263/v1/5e3c6948fb70db3702b7f8b6.png"},{"id":107705624,"identity":"3ee18145-8913-4c7c-9544-bcd01cf3292c","added_by":"auto","created_at":"2026-04-24 09:13:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":413280,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative AIS cases with negative prominent vessels and positive hyperintense. (a-d) A 65-year-old man with supratentorial stroke in the left front and parietal lobe for 2 days. CT (a) and DWI (b) showed acute infarct in the left front and parietal lobe. SWI scanned (c) showed normal veins around the infarction area. But ASL scanned (d) showed hyperperfusion around corresponding DWI lesions (red arrows). (e-f) A 56-year-old man with supratentorial stroke in the left parietal lobe for 3 days. CT (e) and DWI (f) showed acute infarct in the same region. SWI scanned (g) showed less veins around the infarct and hemorrhagic transformation within the infarct (white arrows). ASL scanned (h) showed hyperperfusion around the infarction region (red arrows).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9279263/v1/fd1a95e91d294e28d47a8a47.png"},{"id":107705727,"identity":"399367bf-9d62-4df1-91ee-47d5e569962d","added_by":"auto","created_at":"2026-04-24 09:14:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":261640,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative AIS case with positive prominent vessels and negative hyperintense. A 58 year-old women with supratentorial stroke in the right front and parietal lobe for 2 days. DWI (a) showed the hyperintense acute infarct in the right front and parietal lobe. SWI scanned (b,d) showed prominent veins over the infract (black arrows) and hemorrhagic transformation within the infract (white arrows). ASL scanned (c) showed hypoperfusion around the infarction area.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9279263/v1/5100d35f363c8c20cbadb251.png"},{"id":107706015,"identity":"c36e7f8f-c095-4cb2-beef-8ebcc52ab426","added_by":"auto","created_at":"2026-04-24 09:17:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":279666,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative AIS cases with negative prominent vessels and negative hyperintense. \u0026nbsp;A 65-year-old men with supratentorial stroke in the right frontotemporal lobe for 2 days. DWI (a) showed the hyperintense acute infarct in the right frontotemporal lobe. SWI imaging (b) showed normal veins around the infarct. ASL imaging (c) showed hypoperfusion around the core infarction.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9279263/v1/2e970f13e5c05daf0b537060.png"},{"id":107868827,"identity":"7b58b0c2-74ca-449e-8fcc-d965276157e0","added_by":"auto","created_at":"2026-04-27 07:34:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":249974,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of HT incidence rates and NIHSS scores by PV and HP status. (\u003cstrong\u003ea\u003c/strong\u003e) the numbers of HT+ and HT- in four different groups; (\u003cstrong\u003eb\u003c/strong\u003e) Admission NIHSS scores in four different groups.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9279263/v1/6318d2a25ac0a5faeba770c6.png"},{"id":108804200,"identity":"9d825f5b-6375-4f7d-af17-211d032ace76","added_by":"auto","created_at":"2026-05-08 15:17:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2315367,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9279263/v1/ea371b11-c936-46e6-a7d4-89c9aecb1304.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCombined Hyperperfusion with Prominent Veins for the Prediction of Hemorrhagic Transformation in Stroke Patients \u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eHemorrhagic transformation (HT) is a serious and potentially life-threatening complication following reperfusion therapy in ischemic stroke patients, with currently limited reliable predictive markers. Accumulating evidence reveals that loss of cerebral blood flow (CBF) autoregulation and severe blood-brain barrier (BBB) disruption may lead to HT in ischemic stroke patients treated with intravenous thrombolysis or those who have undergone endovascular intervention reperfusion therapy[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Identifying patients at high risk for HT is crucial for adjusting reperfusion strategies, preventing HT and progressive infarction, and reducing disability and mortality[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWell-established imaging techniques, contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI), have been widely utilized for identifying BBB as a means to predict HT. However, the use of contrast agents and exposure to CT radiation have limited its clinical application. Arterial spin-labeling (ASL), a noninvasive perfusion-weighted MRI technique, has emerged as a valuable tool for assessing cerebral hemodynamics [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Our previous study demonstrated that crossed cerebellar diaschisis detected by ASL manifested as decreased CBF in the contralateral hemisphere compared to the ipsilateral region in supratentorial infarct patients[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous ASL investigations observed a significant correlation between hyperperfusion and HT, suggesting that ASL hyperperfusion maps may offer predictive insights for HT in acute ischemic stroke[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These results suggested that ASL can be used as a sensitive tool for early diagnosis of hyperperfusion.\u003c/p\u003e \u003cp\u003eSusceptibility-weighted imaging (SWI), a modern MR technique, is much more sensitive for the detection of HT than either non-contrast CT scan or T2 gradient echo sequences. This greater sensitivity is important in the acute phase of ischemic stroke[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition to visualizing HT, SWI can also provide detailed visualization of prominent veins, microbleeds and the susceptibility vessel sign - findings that prove valuable for assessing stroke severity and predicting prognosis in ischemic stroke[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. By virtue of its capacity to visualize the intricate venous architecture, SWI imaging modality complements the cerebral perfusion data derived from ASL, thereby facilitating a more comprehensive understanding of the brain\u0026rsquo;s vascular system in its entirety [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Prominent asymmetrical cortical veins in the infarct region, which possibly represent increased oxygen extraction fraction (OEF) and collateral circulation in the penumbra of the infarct[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A preclinical study in nonhuman primates also observed prominent vein in collateral-rich group but not in collateral-poor group by using SWI, meanwhile, the absence of prominent vein is indicative of larger infarct volumes and poorer prognosis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although these studies have established that the presence of prominent veins is linked to adequate collateral circulation in AIS patients, the direct associated between prominent veins and HT not been definitively established, and further investigations are warranted.\u003c/p\u003e \u003cp\u003eInterestingly, previous research suggest that subacute pathophysiological processes contribute more significantly to HT development than acute events in rat models of occlusion [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Further studies indicate that the progression of HT may depend on the timing of hyperperfusion; late postischemic HP is often associated with tissue necrosis, whereas early postischemic HP might be non-detrimental or even beneficial[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We hypothesized that patients with hyperperfusion and prominent veins exhibit higher rates of HT compared with those without hyperperfusion and prominent veins in subacute stroke. Therefore, the aim of this study was to develop a technique for quantitative assessment of hyperperfusion using pre-treatment ASL, prominent veins using SWI and to investigate its value in the prediction of subsequent HT in AIS patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e This study was approved by Local Institution Review Board of Renji Hospital, Shanghai Jiaotong University School of Medicine. We analyzed Images from patients presenting with subacute ischemic stroke (defined as 24 hours to 2 weeks post-onset) between August 2022 and July 2025(n\u0026thinsp;=\u0026thinsp;154). All patients underwent a similar imaging protocol (including ASL and SWI) upon admission. Exclusion criteria were: (1) infarcts in the brainstem, cerebellum, or bilateral supratentorial regions; (2) history of intracranial tumor, head trauma, subarachnoid hemorrhage, arteriovenous malformation, or brain surgery; (3) abnormalities in the posterior fossa on T1, T2, and diffusion-weighted MR images; and (4) MR angiography showing angiopathy of vertebral basilar artery and the major branches. After screening, 44 patients were included. The selection flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData on demographic and clinical parameters were collected from the electronic medical records; these included age, sex, prior antiplatelet agent use, time interval between stroke onset and the MRI. Baseline National Institutes of Health Stroke Scale (NIHSS) scores at admission were assessed by two experienced neurologists independently. All patients included in our study present were not eligible for thrombolysis or endovascular treatments due to the extended treatment window and presence of contraindications. Reperfusion therapies primarily included antiplatelet agents, medications to promote circulation, and neuroprotective strategies.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMR imaging\u003c/h3\u003e\n\u003cp\u003eAll MRI scans were performed on 3-T platform (HDxt; General Electric Medical Systems, Waukesha, WI, USA) using an eight-channel phased-array head coil. The standard stroke imaging protocol included T1-weighted, T2-weighted, MR angiography, SWI and ASL sequences. Pseudo-continuous ASL perfusion images were acquired using 3D fast spin-echo acquisition with background suppression and a post-labeling delay of 1500 ms. Other parameters were: repetition time/echo time\u0026thinsp;=\u0026thinsp;4601/10.5 ms, field of view\u0026thinsp;=\u0026thinsp;240\u0026times;240 mm\u003csup\u003e2\u003c/sup\u003e, matrix size\u0026thinsp;=\u0026thinsp;128\u0026times;128, 38 slices with a slice thickness of 4 mm, and number of averages\u0026thinsp;=\u0026thinsp;3. SWI parameters were: TR/TE\u0026thinsp;=\u0026thinsp;42.6/3.2ms, flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;4 mm with a 2-mm slice gap, 60 sections per slab, matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256, FOV\u0026thinsp;=\u0026thinsp;220 mm \u0026times; 220 mm\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eImaging data processing\u003c/h3\u003e\n\u003cp\u003eTwo independent, experienced readers manually drew circular regions of interest (ROI) on ASL maps. Mean CBF values were obtained from the ipsilateral ROI and its mirror region in the contralateral hemisphere. Relative CBF (rCBF) was calculated as the ratio of ipsilateral to contralateral CBF(rCBF\u0026thinsp;=\u0026thinsp;CBF\u003csub\u003e\u003cem\u003eipsilateral\u003c/em\u003e\u003c/sub\u003e/CBF\u003csub\u003e\u003cem\u003econtralateral\u003c/em\u003e\u003c/sub\u003e). A region exhibiting a relative cerebral blood flow (rCBF) value of on pre-treatment arterial spin labeling (ASL) perfusion maps was identified as a hyperperfusion area. HP was defined as rCBF\u0026thinsp;\u0026ge;\u0026thinsp;1.4[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHT was defined on SWI as multiple punctate hypointensities with no continuity within the infarct area. Prominent veins was assessed as prominent vessels and hypointense signals around the infarct area by comparing to contralateral region[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using SPSS 27.0 (IBM Corp., Armonk, NY, USA). Continuous normally distributed variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). The independent sample t-test or Mann\u0026ndash;Whitney U test (when data were not normally distributed) was used to compare continuous variables); Categorical variables were compared by the chi-square test or fisher\u0026rsquo;s exact text was used for small sample sizes (when the expected call frequency was \u0026lt;\u0026thinsp;5). A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 44 patients with subacute stroke were included in the final data analysis, of these patients, 15(34%) developed HT, as confirmed by SWI. The median lengths of time interval between stroke onset and the MRI were 3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59d. Hyperperfusion was detected in 19 (43%) patients, whereas prominent veins were identified in 11 patients (25%).\u003c/p\u003e \u003cp\u003ePatients were divided into two groups based on the presence or absence of HT: the HT group and the non-HT group. Univariate analysis showed that hyperperfusion (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was detected in 13 patients (87%) of the HT group, which was significantly higher than the 6 patients (21%) in the non-HT group, the Phi Coefficient (φ) was 0.631 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); a similar group difference was also observed in prominent veins indices (47% in the HT group,14% in the non-HT group, p\u0026thinsp;=\u0026thinsp;0.028), the Phi Coefficient (φ) was 0.36 (p\u0026thinsp;=\u0026thinsp;0.017). ASL-derived cerebral blood flow (CBF) analysis revealed the similar CBF in the core-ipsilateral (CBF\u003cem\u003ecore\u003c/em\u003e) between the two groups. However, CBF in the penumbral-ipsilatera CBF (CBF\u003cem\u003epenumbra\u003c/em\u003e) was significantly higher in the HT group compared to non-HT group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), On multivariate logistic analysis, CBF\u003cem\u003epenumbra\u003c/em\u003e (OR 0.937, 95% CI 0.903 to 0.973, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was found to be independent risk factors for the status of HT, suggesting that CBF\u003cem\u003epenumbra\u003c/em\u003e, rather than CBF\u003cem\u003ecore\u003c/em\u003e, may be a more significant contributor to HT. Furthermore, no significant difference in NIHSS scores was noted between the two subgroups. Detailed data of patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003ePatient characteristics with and without hemorrhagic transformation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHT (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo HT (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePhi Coefficient (φ)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnset to MRI(days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfarct volumne(mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18933\u0026thinsp;\u0026plusmn;\u0026thinsp;19350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26674\u0026thinsp;\u0026plusmn;\u0026thinsp;46697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS discharge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(9.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(3.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(6.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(53.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(16.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProminent veins,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperperfusion,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCBF\u003cem\u003ecore\u003c/em\u003e (ml/100g/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCBF\u003cem\u003epenumbra\u003c/em\u003e (ml/100g/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u0026thinsp;\u0026plusmn;\u0026thinsp;33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHT: Hemorrhagic Transformation; NIHSS: Initial National Institute of Health Stroke Scale; CBF: cerebral blood flow. CBF\u003cem\u003ecore\u003c/em\u003e: CBF in the core- ipsilateral; CBF\u003cem\u003epenumbra\u003c/em\u003e: CBF in the penumbra-ipsilateral.\u003c/p\u003e \u003cp\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or n (%). \u003cem\u003ep\u003c/em\u003e value by χ\u003csup\u003e2\u003c/sup\u003e text or Mann-Whitney U test. * Results indicate a significance difference at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eAccording to findings from ASL and SWI: patients were stratified into four distinct subgroups in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, specifically: non-hyperperfusion combined with non-prominent veins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. n\u0026thinsp;=\u0026thinsp;20, 45%), hyperperfusion combined with non-prominent veins (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.n\u0026thinsp;=\u0026thinsp;13, 30%), non-hyperperfusion combined with prominent veins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. n\u0026thinsp;=\u0026thinsp;5, 11%), hyperperfusion combined with prominent veins (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. n\u0026thinsp;=\u0026thinsp;6, 14%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). HT rates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and baseline NIHSS scores (p\u0026thinsp;=\u0026thinsp;0.017) were found to be significantly associated with the status of prominent veins and hyperperfusion. Other demographic and clinical parameters showed no significant differences among the groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics stratified by prominent veins and hyperperfusion status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo hyperperfusion (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHyperperfusion(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo prominent veins (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eprominent veins\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo prominent veins\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProminent veins\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex-(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfarct volumne(mm3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24901\u0026thinsp;\u0026plusmn;\u0026thinsp;43560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25226\u0026thinsp;\u0026plusmn;\u0026thinsp;22692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23431\u0026thinsp;\u0026plusmn;\u0026thinsp;45868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21465\u0026thinsp;\u0026plusmn;\u0026thinsp;26329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS discharge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipemia(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estroke(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHT: Hemorrhagic Transformation; NIHSS: Initial National Institute of Health Stroke Scale.\u003c/p\u003e \u003cp\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or n (%). \u003cem\u003ep\u003c/em\u003e value by χ\u003csup\u003e2\u003c/sup\u003e text or Kruskal-Wallis test.\u003c/p\u003e \u003cp\u003e*Results indicate a significance difference at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/p\u003e \u003cp\u003eSubsequent analyses revealed that the HT rate reached 100% in the hyperperfusion combined with prominent veins subgroup, in contrast to a mere 5% in the non-hyperperfusion combined with non-prominent veins subgroup(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Meanwhile the HT rate was 54% in the hyperperfusion combined with non-prominent veins subgroup and 20% in the non-hyperperfusion combined with prominent veins subgroup. Among both prominent veins and non-prominent veins patients, hyperperfusion was associated with a significantly higher HT rate (p\u0026thinsp;=\u0026thinsp;0.028 and p\u0026thinsp;=\u0026thinsp;0.015, respectively). Additionally, within both hyperperfusion and non-hyperperfusion subgroups, patients with prominent veins had significantly higher baseline NIHSS scores than non- prominent veins subgroup (p\u0026thinsp;=\u0026thinsp;0.022 and p\u0026thinsp;=\u0026thinsp;0.033, respectively).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThe main finding of our study was that the presence of prominent veins, hyperperfusion and CBF\u003cem\u003epenumbra\u003c/em\u003e had strong association with HT. Multivariate logistic analysis indicated that the CBF\u003cem\u003epenumbra\u003c/em\u003e of hyperperfusion was an independent risk factor for predicting HT.\u003c/p\u003e \u003cp\u003eWe assessed hyperperfusion on ASL maps either within or around the hypoperfused region compared with the homologous contralateral hemisphere. significantly higher CBF was observed in the ipsilateral penumbra of HT group, contrasting with non-HT group. Prior studies reported hyperemic lesions, identified as hyperperfusion on ASL, in ischemic stroke patients[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our findings support the notion that ischemic brain tissue is more susceptible to BBB disruption and CBF dysregulation, increasing HP risk after reperfusion [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, unlike previous studies that focused on the presence of hyperperfusion, a noteworthy study observed that higher CBFmax values are more likely to induce HT, whereas mean CBF was not an independent risk factor for HT. The underlying mechanism may be that patients with hyperperfusion who subsequently developed HT sustained more severe cortical injury and BBB disruption, thereby facilitating the leakage of labeled spins from the arterial lumen into the extracellular space. Consequently, hyperperfused patients who went on to develop HT exhibited higher CBF compared with their hyperperfused counterparts who did not progress to HT.\u003c/p\u003e \u003cp\u003ePing et al reported that cortical and medullary prominent veins were associated with poor functional outcomes, particularly in patients who received thrombolytic therapy. However, prominent veins were not linked to poor functional outcomes in those treated conservatively, nor were they associated with an increased risk of HT. Notably, this conclusion is inconsistent with our findings[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our study identified prominent veins in 11 patients (25%), with a significant association with HT (p\u0026thinsp;=\u0026thinsp;0.028, φ Coefficient\u0026thinsp;=\u0026thinsp;0.36), which suggested that prominent veins adjacent to the infarcted regions may be indicative of an increased susceptibility to HT. Another study identified the Asymmetric Prominent Veins-based Alberta Stroke Program Early Computed Tomography Score (APVs-ASPECTS) as an independent predictor of both parenchymal hemorrhage (PH) after mechanical thrombectomy, but its predictive power is only moderate [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A low APVs-ASPECTS score may thus indicate a large volume of ischemic brain tissue, the presence of cerebral infarction and inadequate collateral circulation, all of which are potential contributors to an elevated risk of hemorrhage after mechanical thrombectomy.\u003c/p\u003e \u003cp\u003eSubsequent studies have additionally demonstrated that all patients with both hyperperfusion and prominent veins suffered HT after reperfusion therapy, a rate significantly higher than in those without both prominent veins and hyperperfusion. Interestingly, in negative hyperperfusion patients, prominent veins presence did not significantly affect HT rates, aligning previous reports questioning the independent predictive value for HT or clinical progression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Thus, while prominent veins alone may not reliably predict HT, its co-concurrence with hyperperfusion demonstrates high predictive value.\u003c/p\u003e \u003cp\u003eIn our cohort, HT manifested in 15 patients, which was associated with higher mortality and disability. However, when we investigated other potential risk factors for HT, we found no significant associations with demographic or clinical parameters, including changes in NIHSS scores. In our study, the over-windowing and the presence of contraindications are probably the main reasons for patients not performing revascularization therapies. This population had more underlying diseases and a poorer overall prognosis. Meanwhile, the administered treatments included antiplatelet agents, circulation-promoting drugs, and neuroprotective therapy, which may explain the observed stability of NIHSS scores[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsistent with prior research [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e],we observed hypoperfusion in the infract core.HT may result from severe ischemic damage to vessel walls, leading to BBB disruption and leakage[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e],potentially explaining the lower CBF in the infarct core of the HT+ group.\u003c/p\u003e \u003cp\u003eThis study, however, has some limitations. First, the small sample size, which may limit generalizability; Second, lack of data on patients receiving revascularization therapies and absence of acute phase comparison; Third, the following clinical effect and imaging review of patients for 1\u0026ndash;3 month after discharge should be completed.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePatients exhibiting hyperperfusion are at an elevated risk of HT regardless of prominent veins status. Hyperperfusion appears to be an independent risk factor for HT, but not prominent veins. SWI and ASL are valuable noninvasive tools for predicting HT in supratentorial stroke patients following reperfusion therapy and further external verification is required in the future.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Foundation of Zhejiang Provincial Medical and Health Science and Technology Plan Project(2022RC070); Interdisciplinary Project of Shanghai Jiaotong University (YG2021QN40); and Clinical Research Projects of Renji Hospital Campus, Shanghai Jiaotong University School of Medicine (LY2024-186-B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved from the shanghai jiaotong university school of medicine, renji hospital ethics Committee (RA2020-543). This study respects the life, health, dignity, self-determination, privacy and confidentiality of the subjects\u0026rsquo; personal information of all participants. All methods were carried out in accordance with the guidelines and provisions of the Declaration of Helsinki. Written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL,TYW and YXZ performed the MRI experiments. JW, LJP and JYZ collected imaging data. RZ collected clinical NIHSS score. PLJ and JW did the statistical analysis and wrote the manuscript. ZAC did a critical revision of manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not publicly available due to the privacy of the research participants but are available from the corresponding author (ZAC) upon reasonable request after signing a data transfer agreement.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYu S, Liebeskind DS, Dua S, Wilhalme H, Elashoff D, Qiao XJ, et al. Postischemic hyperperfusion on arterial spin labeled perfusion MRI is linked to hemorrhagic transformation in stroke. J Cereb Blood Flow Metab. 2015;35:630\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/jcbfm.2014.238\u003c/span\u003e\u003cspan address=\"10.1038/jcbfm.2014.238\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulmatytskyi AV, Bilobryn MS, Makarovska MB. Hemorrhagic transformation of cerebral infarction: risk factors, diagnosis, and new approaches to treatment. INJ. 2024;20:28\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22141/2224-0713.20.1.2024.1043\u003c/span\u003e\u003cspan address=\"10.22141/2224-0713.20.1.2024.1043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobata T, Yamasaki T, Omori K, Ogawa K. Influence of the Imaging Method on Regional Cerebral Blood Flow Value in Arterial Spin Labeling (ASL): Comparison of Pulsed-ASL with Two-dimensional Acquisition and Pseudo-continuous-ASL with 3D Spiral Acquisition. Nippon Hoshasen Gijutsu Gakkai Zasshi. 2022;78:969\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6009/jjrt.2022-1265\u003c/span\u003e\u003cspan address=\"10.6009/jjrt.2022-1265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Pan L-J, Zhou B, Zu J-Y, Zhao Y-X, Li Y, et al. Crossed cerebellar diaschisis after stroke detected noninvasively by arterial spin-labeling MR imaging. BMC Neurosci. 2020;21:46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12868-020-00595-z\u003c/span\u003e\u003cspan address=\"10.1186/s12868-020-00595-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiibo T, Ohta H, Miyata S, Ikushima I, Yonenaga K, Takeshima H. Prediction of Blood-Brain Barrier Disruption and Intracerebral Hemorrhagic Infarction Using Arterial Spin-Labeling Magnetic Resonance Imaging. Stroke. 2017;48:117\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/STROKEAHA.116.013923\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.116.013923\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerclytte S, Fisch O, Colas L, Vanaerde O, Toledano M, Budzik J-F. ASL and susceptibility-weighted imaging contribution to the management of acute ischaemic stroke. Insights Imaging. 2017;8:91\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13244-016-0529-y\u003c/span\u003e\u003cspan address=\"10.1007/s13244-016-0529-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan R, Spring KJ, Killingsworth M, Bhaskar S. Comparative Diagnostic and Prognostic Performance of SWI and T2-Weighted MRI in Cerebral Microbleed Detection Following Acute Ischemic Stroke: A Meta-Analysis and SPOT-CMB Study. Medicina. 2025;61:1566. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/medicina61091566\u003c/span\u003e\u003cspan address=\"10.3390/medicina61091566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalchev E. Generalized Venous Prominence on Susceptibility-Weighted Imaging Correlates With Global Cerebral Blood Flow Decline. Cureus. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.56272\u003c/span\u003e\u003cspan address=\"10.7759/cureus.56272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang P, Chen C-H, Lin W-C, Lin R-T, Khor G-T, Liu C-K. Clinical applications of susceptibility weighted imaging in patients with major stroke. J Neurol. 2012;259:1426\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00415-011-6369-2\u003c/span\u003e\u003cspan address=\"10.1007/s00415-011-6369-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang K, Liu J, Yun W, Cao Y, Zhang M. The role of asymmetrical prominent veins sign in early neurological deterioration of acute ischemic stroke patients. Front Neurol. 2022;13:860824. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fneur.2022.860824\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2022.860824\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheikhbabaei M, Gharekhanloo F, Khazaei M, Pirdehghan A. Comparison of Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging in the Estimation of the Amount of Reversible Ischemic Tissue (Penumbra). SN Compr Clin Med. 2022;5:36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s42399-022-01274-2\u003c/span\u003e\u003cspan address=\"10.1007/s42399-022-01274-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParthasarathy R, Kate M, Rempel JL, Liebeskind DS, Jeerakathil T, Butcher KS, et al. Prognostic Evaluation Based on Cortical Vein Score Difference in Stroke. Stroke. 2013;44:2748\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/STROKEAHA.113.001231\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.113.001231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeumann-Haefelin T, Kastrup A, de Crespigny A, Ringer TM, Sun GH, Yenari MA, et al. MRI of subacute hemorrhagic transformation in the rat suture occlusion model. NeuroReport. 2001;12:309\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/00001756-200102120-00025\u003c/span\u003e\u003cspan address=\"10.1097/00001756-200102120-00025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarchal G, Young AR, Baron JC. Early postischemic hyperperfusion: pathophysiologic insights from positron emission tomography. J Cereb Blood Flow Metab. 1999;19:467\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/00004647-199905000-00001\u003c/span\u003e\u003cspan address=\"10.1097/00004647-199905000-00001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang J, Hao P, Chen Z, Deng K, Liu B, Xu Y. Quantitative assessment of hyperperfusion using arterial spin labeling to predict hemorrhagic transformation in acute ischemic stroke patients with mechanical endovascular therapy. Eur Radiol. 2023;34:579\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00330-023-10007-5\u003c/span\u003e\u003cspan address=\"10.1007/s00330-023-10007-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang P, Chen C-H, Lin W-C, Lin R-T, Khor G-T, Liu C-K. Clinical applications of susceptibility weighted imaging in patients with major stroke. J Neurol. 2012;259:1426\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00415-011-6369-2\u003c/span\u003e\u003cspan address=\"10.1007/s00415-011-6369-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang J, Hao P, Chen Z, Deng K, Liu B, Xu Y. Quantitative assessment of hyperperfusion using arterial spin labeling to predict hemorrhagic transformation in acute ischemic stroke patients with mechanical endovascular therapy. Eur Radiol. 2023;34:579\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00330-023-10007-5\u003c/span\u003e\u003cspan address=\"10.1007/s00330-023-10007-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiraishi K, Wang Z, Kokuryo D, Aoki I, Yokoyama M. A polymeric micelle magnetic resonance imaging (MRI) contrast agent reveals blood-brain barrier (BBB) permeability for macromolecules in cerebral ischemia-reperfusion injury. J Control Release. 2017;253:165\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jconrel.2017.03.020\u003c/span\u003e\u003cspan address=\"10.1016/j.jconrel.2017.03.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu P, Cui L, Zhao X. The Prognostic Impact of Susceptibility-Weighted Imaging Prominent Veins in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis. NDT. 2021;17:3069\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/NDT.S331874\u003c/span\u003e\u003cspan address=\"10.2147/NDT.S331874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Jiang H, Yan R, Li H, Li Z, Bai Y, et al. Prediction of parenchymal hematoma after mechanical thrombectomy by asymmetrical prominent veins: a retrospective cohort study based on susceptibility-weighted imaging. Quant Imaging Med Surg. 2025;15:7224\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/qims-2025-262\u003c/span\u003e\u003cspan address=\"10.21037/qims-2025-262\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuh CH, Jung SC, Cho SJ, Kim D, Lee JB, Woo D-C, et al. Perfusion CT for prediction of hemorrhagic transformation in acute ischemic stroke: a systematic review and meta-analysis. Eur Radiol. 2019;29:4077\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00330-018-5936-7\u003c/span\u003e\u003cspan address=\"10.1007/s00330-018-5936-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpronk E, Sykes G, Falcione S, Munsterman D, Joy T, Kamtchum-Tatuene J, et al. Hemorrhagic Transformation in Ischemic Stroke and the Role of Inflammation. Front Neurol. 2021;12:661955. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fneur.2021.661955\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2021.661955\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Arterial Spin-labeling, Hemorrhagic Transformation, Hyperperfusion, Prominent veins, Susceptibility-weighted Imaging","lastPublishedDoi":"10.21203/rs.3.rs-9279263/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9279263/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study was aimed to investigated whether hyperperfusion and prominent veins via magnetic resonance using arterial spin-labeling (ASL) and susceptibility weighted imaging (SWI) could help predict hemorrhagic transformation (HT) after reperfusion therapy in subacute ischemic stroke.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively reviewed 44 patients with unilateral supratentorial infraction who were admitted between 24h and 2 weeks after stroke onset. All patients underwent magnetic resonance imaging (ASL, SWI) after admission. Hyperperfusion areas were defined as regions with a relative cerebral blood flow (rCBF) of \u0026ge;\u0026thinsp;1.4. Imaging assessments revealed that prominent veins refer to the hypointense signals surrounding the infarct area on SWI, while hypointense hemorrhagic foci in the infarct area can also be visualized by this modality. All clinical parameters were enrolled by two senior neurologists.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the final cohort of 44 patients with subacute ischemic stroke, comparison between the HT group and the no-HT group showed that HT was strongly associated with hyperperfusion (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), prominent veins (p\u0026thinsp;=\u0026thinsp;0.028) and CBF\u003cem\u003epenumbra\u003c/em\u003e (OR 0.937, 95% CI 0.903 to 0.973, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Based on ASL and SWI findings, patients were stratified into four distinct subgroups: those with ASL-detected hyperperfusion or no hyperperfusion, combined with the presence or absence of prominent veins on SWI. 6 patients presented with both prominent veins and hyperperfusion, and all of these 6 patients (100%) developed HT. In contrast, only 5% of patients without prominent veins nor hyperperfusion developed HT. Furthermore, regardless of hyperperfusion status, the Initial National Institute of Health Stroke Scale (NIHSS) score was significantly correlated with prominent veins.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe presence of hyperperfusion and/or prominent veins increases the likelihood of HT in ischemic stroke. Combined SWI and ASL imaging serves as a valuable tool for predicting HT in patients with supratentorial ischemic stroke.\u003c/p\u003e","manuscriptTitle":"Combined Hyperperfusion with Prominent Veins for the Prediction of Hemorrhagic Transformation in Stroke Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 05:49:13","doi":"10.21203/rs.3.rs-9279263/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78b122de-069c-4c0e-b4f0-94fde13abb84","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-04T06:27:26+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"217044028834491605395243978917521552982","date":"2026-04-30T07:31:27+00:00","index":66,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T06:41:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 05:49:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9279263","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9279263","identity":"rs-9279263","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}