Journal of Magnetic Resonance Imaging最新文献

{"title":"Editorial for “Voxel-Wise Radiomics Habitat Analysis of Posttreatment Gliomas for Noninvasive Differentiation of True Progression and Pseudoprogression”","authors":"Toshiaki Taoka","doi":"10.1002/jmri.70422","DOIUrl":"10.1002/jmri.70422","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"64 5","pages":"1280-1281"},"PeriodicalIF":4.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148330617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Imaging Compass: Navigating Reperfusion in Acute Ischemic Stroke","authors":"Kai Xu,&nbsp;Jinhao Lyu,&nbsp;Xin Lou","doi":"10.1002/jmri.70517","DOIUrl":"10.1002/jmri.70517","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>The management of acute ischemic stroke has shifted from rigid time-based protocols to imaging-driven, tissue-based reperfusion strategies. Non-contrast CT and CT angiography remain the indispensable frontline for rapid triage, particularly in community and low-resource settings. However, magnetic resonance imaging (MRI) provides unique biological information that complements CT-based assessment: Diffusion-weighted imaging (DWI) detects cellular ischemia within minutes without contrast; DWI-fluid-attenuated inversion recovery (FLAIR) mismatch serves as a tissue clock for unknown-onset stroke; susceptibility-weighted imaging (SWI) aids thrombus characterization; and MRI resolves the posterior fossa without beam-hardening artifact. Low-field portable MRI further extends these capabilities to the bedside where conventional scanners are unavailable. This review examines four convergent trends within this transition: expanding thrombectomy to large-core infarction, extending thrombolysis through tissue-based selection, advancing recanalization in posterior-circulation stroke while defining boundaries for medium- and distal-vessel occlusions, and simplifying imaging workflows through fast MRI protocols, quantitative CT biomarkers, and artificial intelligence (AI). Landmark trials have established that treatment benefit is determined by tissue viability rather than clock time, while recent negative trials define critical boundaries beyond which intervention may cause harm. We critically appraise how CT and MRI are integrated hierarchically across these scenarios, and how emerging fast MRI and quantitative biomarkers may reshape future selection paradigms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Evidence Level</h3>\u0000 \u0000 <p>3–4.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Technical Efficacy</h3>\u0000 \u0000 <p>3.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"64 5","pages":"1197-1214"},"PeriodicalIF":4.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148818582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for “Association of Iron and Myelin Alterations in the Contralesional Dentate Nucleus and Thalamus With Functional Outcome in Acute Ischemic Stroke: A Susceptibility Source Separation Study”","authors":"Yaqi Chen,&nbsp;Deren Wang","doi":"10.1002/jmri.70435","DOIUrl":"10.1002/jmri.70435","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"64 5","pages":"1293-1294"},"PeriodicalIF":4.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for “Association Between Deep Gray Matter Iron Deposition Dysregulation and Cognitive Performance in Type 2 Diabetes Mellitus Patients: A Quantitative Susceptibility Mapping Study”","authors":"Yuto Uchida","doi":"10.1002/jmri.70434","DOIUrl":"10.1002/jmri.70434","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"64 5","pages":"1309-1310"},"PeriodicalIF":4.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probing Ovarian Stiffness in Healthy Volunteers With 3D Magnetic Resonance Elastography","authors":"Huiting Cao,&nbsp;Feng Jin,&nbsp;Qiang Liu,&nbsp;Wen Zhang,&nbsp;Yun Chen,&nbsp;Yu Shi,&nbsp;Wenxu Qi","doi":"10.1002/jmri.70455","DOIUrl":"10.1002/jmri.70455","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The feasibility of multi-frequency magnetic resonance elastography (MRE) for ovarian assessment, normative stiffness values, and related influencing factors in the general population remains unclear.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To evaluate ovarian MRE feasibility, preferred frequency, normative stiffness, and influencing factors in healthy individuals.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Type&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Prospective.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Subjects&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;About 55 healthy female volunteers (median age: 42; median body mass index (BMI): 23).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Field Strength/Sequence&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;3 T; spin-echo echo-planar imaging sequence at 40, 60, and 80 Hz.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Assessment&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Quality-control metrics and ovarian stiffness values (left and right) were measured for all participants. Image quality was evaluated by IQS (1–4; diagnostic images ≥ 3) and confidence percentage.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Statistical Tests&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Intraclass correlation coefficient (ICC); ANOVA; multivariate linear regression; significance level, &lt;i&gt;p&lt;/i&gt; &lt; 0.05.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Quality-control metrics, including fixed-ROI average phase SD [138.63 (106.17–177.74), 127.90 (97.77–186.46), and 133.70 (92.73–185.79)] and average within-ROI phase range [605.25 (468.43–777.50), 604.00 (432.83–821.50), and 607.00 (442.00–804.80)] at 40, 60, and 80 Hz, showed no significant frequency-dependent variation (both &lt;i&gt;p&lt;/i&gt; = 0.947), confirming no substantial vibration-induced phase artifacts. Combined with good-to-excellent reproducibility (ICCs: 0.897–0.999), these data supported the feasibility of ovarian MRE. Among tested frequencies, 60 Hz showed relatively favorable image quality, the highest IQS agreement (inter-reader/intra-reader weighted &lt;i&gt;κ&lt;/i&gt; = 0.828–0.891/0.862–0.877). Ovarian stiffness ranges were 1.56–2.91, 2.91–3.70, and 3.10–3.89 kPa in the premenopausal, perimenopausal, and postmenopausal groups, respectively. Ovarian stiffness remained stable across menstrual-cycle phases in either ovary (left/right: &lt;i&gt;p&lt;/i&gt; = 0.250/0.358, 0.986/0.858, 0.088/0.476, 0.249/0.448, 0.633/0.185, and 0.120/0.706). Regression analysis identified age and BMI as independent predictors of ovarian stiffness; age had a larger","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"64 5","pages":"1215-1226"},"PeriodicalIF":4.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148471193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of MR Cytometry in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparative Study","authors":"Jie Ding,&nbsp;Diwei Shi,&nbsp;Shiyun Sun,&nbsp;Jinlong Zheng,&nbsp;Wentao Yang,&nbsp;Caixia Fu,&nbsp;Dazhi Chen,&nbsp;Junzhong Xu,&nbsp;Hua Guo,&nbsp;Chao You,&nbsp;Yajia Gu","doi":"10.1002/jmri.70484","DOIUrl":"10.1002/jmri.70484","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;MR cytometry imaging is developed to characterize in vivo microstructures. Its clinical value in predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer remains unknown.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To implement MR cytometry to predict pCR for breast cancer and compare the performance of different quantitative models (IMPULSED, JOINT, and EXCHANGE).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Type&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Prospective.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Subject&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;209 female patients from center A (50.32 ± 11.06 years) and B (48.05 ± 11.30 years).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Field Strength/Sequence&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;3 T; pulsed gradient spin-echo diffusion-weighted imaging (DWI) and oscillating gradient spin-echo DWI of 17/33 Hz (center A) or 25/50 Hz (center B).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Assessment&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Tumor regions were manually delineated. Time-dependent apparent diffusion coefficients (ADC) and model-derived microstructural parameters (including cell diameter, intracellular volume fraction, cellularity, extracellular diffusivity, and water exchange rate constant) were calculated before and after NAC. Four multivariate logistic regression models, that is, time-dependent ADCs, IMPULSED, JOINT, and EXCHANGE, were constructed to predict pCR; performance of combining MR cytometry with ADCs, that is, IMPULSED-ADC, JOINT-ADC, and EXCHANGE-ADC, was also evaluated.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Statistical Tests&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Wilcoxon test, Mann–Whitney U test, Logistic regression analysis, DeLong's test, and Pearson correlation analysis, with statistical significance set at &lt;i&gt;p&lt;/i&gt; &lt; 0.05.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Changes in intracellular volume fraction, cellularity, and water exchange rate constant before and after NAC exhibited significant differences between pCR and non-pCR groups. The regression models of time-dependent ADCs, IMPULSED, JOINT, and EXCHANGE yielded AUCs of 0.709, 0.716, 0.747, and 0.745 in predicting pCR for center A and 0.777, 0.809, 0.846, and 0.864 for center B; the combined models of IMPULSED-ADC, JOINT-ADC, and EXCH","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"64 5","pages":"1346-1360"},"PeriodicalIF":4.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmri.70484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148706849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for “Structural MRI Volumetry Index for Differentiation of Progressive Supranuclear Palsy From Parkinson's Disease and Multiple System Atrophy by Automatic Segmentation: A Comparison With Magnetic Resonance Parkinsonism Index”","authors":"Steven Benitez,&nbsp;Seena Dehkharghani","doi":"10.1002/jmri.70486","DOIUrl":"10.1002/jmri.70486","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"64 5","pages":"1264-1265"},"PeriodicalIF":4.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148759836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of Baseline Glymphatic-Related MRI Metrics With Neoadjuvant Chemotherapy-Related Cognitive Impairment in Patients With Breast Cancer","authors":"Tian-Ye Lin,&nbsp;Yizhe Hu,&nbsp;Shao-Shuai Sun,&nbsp;Yang Yang,&nbsp;Chun-Xiao Zheng,&nbsp;Nan Sun,&nbsp;Ying-Shi Sun,&nbsp;Zixuan Lin","doi":"10.1002/jmri.70433","DOIUrl":"10.1002/jmri.70433","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Chemotherapy-related cognitive impairment (CRCI) is a common complication for cancer survivors. The glymphatic system is increasingly recognized as playing a role in cognitive health. However, whether the glymphatic system is associated with CRCI and its dynamic change during chemotherapy remains unclear.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To investigate longitudinal changes of glymphatic-related MRI metrics during chemotherapy and their association with CRCI.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Type&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Prospective longitudinal.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Population&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Sixty two female patients with breast cancer underwent brain MRI and neuropsychological assessments at baseline and after chemotherapy.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Field Strength/Sequence&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Resting-state functional MRI, diffusion tensor imaging (DTI), 3D T1-weighted magnetization-prepared rapid gradient echo sequence at 3 T.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Assessment&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Patients were divided into cognitive decline (&lt;i&gt;n&lt;/i&gt; = 22) and cognitive remain (&lt;i&gt;n&lt;/i&gt; = 40) groups according to the Montreal Cognitive Assessment (MoCA) and the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog) scale. MR metrics, including blood-oxygen-level-dependent cerebrospinal coupling (BOLD-CSF coupling), DTI along the perivascular space (DTI-ALPS) index, fraction of perivascular space (PVS/ICV), and choroid plexus volume to intracranial volume (ChP/ICV), were used to reflect glymphatic function.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Statistical Tests&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Linear mixed-effects models were utilized to assess longitudinal trajectories. Analysis of covariance was used to identify differences in glymphatic-related MRI metrics between groups. Multivariable linear regression was used to determine the association between post-chemotherapy cognitive decline and baseline metrics. Significance was defined as &lt;i&gt;p&lt;/i&gt; &lt; 0.05 or FDR corrected &lt;i&gt;p&lt;/i&gt; &lt; 0.05.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Result&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;After chemotherapy, significant cognitive decline, an increase in PVS/ICV, ChP/ICV, and BOLD-CSF coupling strength were observed. Longitudinal changes in MRI metrics did not differ between the cognitive remain and cognitive decline groups (FDR-corrected &lt;i&gt;p&lt;/i&gt; = 0.234–0.833). The cogniti","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"64 5","pages":"1311-1323"},"PeriodicalIF":4.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148396919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustering of Cerebral Microbleeds in Cerebral Amyloid Angiopathy: A Flow Territory-Based Analysis","authors":"Manon R. Schipper,&nbsp;Reinier G. J. van der Zwet,&nbsp;Jelle J. Goeman,&nbsp;Thijs W. van Harten,&nbsp;Emma A. Koemans,&nbsp;Marieke J. H. Wermer,&nbsp;Marianne A. A. van Walderveen,&nbsp;Matthias J. P. van Osch","doi":"10.1002/jmri.70454","DOIUrl":"10.1002/jmri.70454","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Cerebral microbleeds (CMBs) are a key imaging marker of cerebral amyloid angiopathy (CAA), but their individual spatial distribution and potential clustering remain poorly understood.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study assessed whether CMBs exhibit patient-specific, flow territory-based clustering in sporadic (sCAA) and Dutch-type hereditary CAA (D-CAA). The arterial flow territory subdivision was chosen, considering the predilection of CMB location in the posterior cortex—not limited to the occipital lobe—and the prominent vascular role in CAA.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Type&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Retrospective cross-sectional study.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Population&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Thirty-three participants (17 (52%) female, 65 ± 12 years old, 19 sCAA; 14 D-CAA) without symptomatic intracerebral hemorrhage (sICH) and ≥ 1 CMB were included for the main analysis.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Field Strength/Sequence&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;3 T MRI susceptibility-weighted imaging, multi-echo 3D gradient echo.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Assessment&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Spatial CMB patterns were evaluated by comparing observed versus expected CMB density within covariate-matched subgroups, accounting for age, sex, CAA-type, smoking, and hypertension. CMBs were manually annotated and assigned to six arterial flow territories (left/right ACA, MCA, PCA) using a standardized atlas. A sensitivity analysis was performed on all participants (including sICH+ participants).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Statistical Tests&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Expected values were calculated by multiplying flow territory CMB proportions per covariate-matched subgroup. Unweighted individual chi-square statistics were calculated, and statistical significance was assessed via permutation testing (10,000 replications) per covariate group.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Across covariate-matched subgroups, observed CMB counts significantly deviated from expected distributions, indicating substantial interindividual variability and within-participant clustering (&lt;i&gt;χ&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 6920). The effect remained significant when including sICH+ participant","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"64 5","pages":"1241-1249"},"PeriodicalIF":4.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmri.70454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148592145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural MRI Volumetry Index for Differentiation of Progressive Supranuclear Palsy From Parkinson's Disease and Multiple System Atrophy by Automatic Segmentation: A Comparison With Magnetic Resonance Parkinsonism Index","authors":"Mingnan Jia,&nbsp;Hui You,&nbsp;Zhaoxi Liu,&nbsp;Yichun Wang,&nbsp;Han Wang,&nbsp;Mingli Li,&nbsp;Feng Feng","doi":"10.1002/jmri.70418","DOIUrl":"10.1002/jmri.70418","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Although Parkinson's disease (PD) and Parkinsonism-Plus syndromes (PPS) exhibit distinct brain atrophy, current clinical differentiation heavily relies on manual, plane-specific 2D metrics, which are labor-intensive and observer-dependent. To fill this gap, this study developed an automated, software-independent metric—the parkinsonism volumetric index (PDSVI)—calculated as [(pons volume × third ventricle volume)/(midbrain volume × thalamus volume) × 10].&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To evaluate the performance of PDSVI in precisely differentiating progressive supranuclear palsy (PSP) from PD, multiple system atrophy cerebellar (MSA-C) and parkinsonian (MSA-P) types.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Type&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Retrospective.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Population&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;246 PD, 108 PSP, 63 MSA-C, 63 MSA-P (&lt;i&gt;N&lt;/i&gt; = 480, 253 males and 227 females; mean age: 63.3 ± 9.2 years) and 252 healthy controls (HC) (134 males and 118 females; mean age: 64.9 ± 8.2 years).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Field Strength/Sequence&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;3 T, 3D inversion-prepared fast spoiled gradient echo sequence T1-weighted Imaging.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Assessment&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Manual magnetic resonance parkinsonism index (MRPI) was measured by two raters. Automated brain volumetry was performed using a pre-trained cascaded VB-Net. Patients were randomly assigned to training (&lt;i&gt;n&lt;/i&gt; = 320) and internal validation (&lt;i&gt;n&lt;/i&gt; = 160) cohorts via 2:1 stratified randomization. Diagnostic performance of manual MRPI, pons/midbrain volume ratio, and PDSVI were assessed.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Statistical Tests&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Diagnostic performance was assessed via receiver operating characteristic (ROC) curve analysis and DeLong's test; &lt;i&gt;p&lt;/i&gt; &lt; 0.05 was considered statistically significant.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In internal validation cohort, ROC analysis showed that PDSVI distinguished PSP from non-PSP (AUC = 0.897), PD (0.873), MSA-P (0.950), MSA-C (0.939), and HC (0.909), comparable to manual MRPI (AUCs: 0.909, 0.886, 0.923, 0.984 and 0.862, &lt;i&gt;p&lt;/i&gt; values: 0.760, 0.786, 0.492, 0.092, 0.287, respectively).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This stud","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":"64 5","pages":"1250-1263"},"PeriodicalIF":4.3,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148361189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}