Clinical Neuroradiology最新文献

{"title":"Process, Performance, and Outcome Goals in Neurointervention Trials: Lessons from Sports Psychology.","authors":"Umberto Pensato, Johanna Ospel","doi":"10.1007/s00062-026-01663-3","DOIUrl":"https://doi.org/10.1007/s00062-026-01663-3","url":null,"abstract":"","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.","authors":"Hirofumi Sekino, Shiro Ishii, Tatsuya Ando, Yumi Saito, Anna Yamaki, Ryo Yamakuni, Kenji Fukushima, Hiroshi Ito","doi":"10.1007/s00062-026-01661-5","DOIUrl":"https://doi.org/10.1007/s00062-026-01661-5","url":null,"abstract":"<p><strong>Purpose: </strong>Deep learning reconstruction (DLR) is useful to reduce image noise and improve contrast resolution compared with hybrid iterative reconstruction (Hybrid IR). This study compared image quality and infarct detection between DLR and Hybrid IR using thin-slice brain CT.</p><p><strong>Materials and methods: </strong>Eighty-one patients (39 with acute infarction, 42 without) underwent 135 kVp non-contrast brain CT and MRI within 24 h of admission. CT images (2-mm thickness) were reconstructed using both Hybrid IR and DLR. Image noise was measured in white matter, gray matter, and infarct lesions. Three general radiologists independently assessed infarct presence. Sensitivity was evaluated using patient- and region-based analyses.</p><p><strong>Results: </strong>DLR demonstrated significantly lower image noise than Hybrid IR in white matter, gray matter, and infarct lesions (1.69 vs. 4.40, 1.43 vs. 3.93, and 1.68 vs. 3.94 HU, respectively; all p < 0.001). Contrast-to-noise ratio was significantly higher with DLR (5.10 vs. 2.36, p < 0.001). In patient-based analysis, infarct detection sensitivity was higher with DLR (66.7%-71.8%) than with Hybrid IR (59.0%-69.2%) (p > 0.05). In region-based analysis, DLR showed significantly higher sensitivity for one reader (60.5% vs. 50.0%, p = 0.004).</p><p><strong>Conclusion: </strong>In this study, DLR significantly reduces image noise and improves contrast-to-noise ratio in thin-slice brain CT. These improvements may help general radiologists in diagnosing acute cerebral infarction.</p>","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants of Radiation Exposure in Middle Meningeal Artery Embolization for Chronic Subdural Hematoma: A Single-Center Cohort Study.","authors":"Gregor Peter, Lukas Meyer, Luca Meucci, Felix Schlicht, Helge Kniep, Alexander Heitkamp, Christian Heitkamp, Laurens Winkelmeier, Bogdana Tokareva, Vincent Geest, Maxim Bester, Jens Fiehler, Matthias Bechstein, Christian Thaler","doi":"10.1007/s00062-026-01662-4","DOIUrl":"https://doi.org/10.1007/s00062-026-01662-4","url":null,"abstract":"<p><strong>Purpose: </strong>To identify patient- and procedure-related factors associated with radiation exposure during middle meningeal artery (MMA) embolization for chronic subdural hematoma (cSDH), with particular focus on embolic agent (particle vs. liquid) and arterial access route (transradial vs. transfemoral).</p><p><strong>Methods: </strong>In this retrospective single-center cohort study, we included all patients who underwent MMA embolization for cSDH between February 2021 and October 2025. MMA embolization was performed by fourteen experienced board-certified neuroradiologists. The primary outcome was dose area product (DAP, Gy · cm²). Univariable and multivariable linear regression analyses were performed to assess associations between DAP and age, sex, access route, treated side (unilateral vs bilateral), embolic agent, anesthesia, interventionalist, and guiding catheter.</p><p><strong>Results: </strong>A total of 112 patients (median age 81 years; 20% female) were included; 69 (62%) underwent embolization with particles and 43 (38%) with liquid agents. Transradial access was used in 17 (15%) and transfemoral access in 95 (85%) procedures. Overall median (IQR) DAP was 74.7 Gy · cm² (55.6-102.4), with 65.5 Gy · cm² (50.6-82.0) for unilateral and 107.6 Gy · cm² (84.1-161.6) for bilateral embolization. Univariable analyses showed no significant association between DAP and age, sex, embolic agent, access route, anesthesia, guiding catheter, or interventionalist. In multivariable analysis, unilateral right and left MMA embolization remained independently associated with significantly lower DAP compared with bilateral procedures, while embolic agent and access route were not significantly associated with radiation exposure.</p><p><strong>Conclusion: </strong>In MMA embolization for cSDH, radiation exposure is primarily driven by procedural extent, with higher DAP in bilateral than in unilateral embolization, whereas the choice of embolic material and arterial access route is not associated with radiation dose. These findings support selecting embolic agents and access routes based on anatomical and clinical considerations rather than radiation concerns.</p>","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Societies' Communications.","authors":"","doi":"10.1007/s00062-026-01658-0","DOIUrl":"https://doi.org/10.1007/s00062-026-01658-0","url":null,"abstract":"","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plaque-Level Machine Learning Prediction of Intraplaque Hemorrhage in Carotid Arteries Using Computed Tomography Angiography.","authors":"Juan Long, Xiaohan Liu, He Zhang, Zhen Wang, Yang Wu, Chong Meng, He Zhang, Zhongxiao Liu, Aiyun Sun, Chunfeng Hu, Kai Xu, Yankai Meng","doi":"10.1007/s00062-026-01656-2","DOIUrl":"https://doi.org/10.1007/s00062-026-01656-2","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Carotid artery plaques, especially those with intraplaque hemorrhage (IPH), are significant contributors to ischemic stroke. Although high-resolution magnetic resonance vessel-wall imaging (HR-MR-VWI) is the gold standard for assessing plaque vulnerability, its limited availability and high cost pose challenges. Computed tomography angiography (CTA) offers a more accessible, non-invasive alternative, but its ability to detect IPH and assess plaque instability remains underexplored. Machine learning techniques have shown promise in improving the prediction of carotid plaque vulnerability using CTA.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;This study aimed to develop and validate a machine learning model using CTA to predict IPH in carotid plaques. The model integrated key imaging features, including plaque composition, vascular lumen geometry, and perivascular adipose tissue (PVAT), with HR-MR-VWI serving as the reference standard. The goal was to evaluate the model's potential for non-invasive plaque vulnerability prediction, particularly in clinical settings where MRI is not available.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A retrospective analysis was conducted on patients who underwent both carotid CTA and HR-MR-VWI within one month. Key plaque features, including composition, vascular lumen geometry, and PVAT, were extracted from CTA. The dataset was split into training (70%) and validation (30%) sets. Feature selection was performed using LASSO regression, followed by model development with logistic regression, random forest, XGBoost, and support vector machine (SVM). Hyperparameter tuning was performed using 10-fold cross-validation. Model performance was assessed using AUC, ROC curves, calibration curves, precision-recall curves, and confusion matrices. SHAP analysis was employed to evaluate the importance of each feature.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In the validation cohort, the Random Forest model achieved an AUC of 0.679, sensitivity of 86.0%, specificity of 45.6%, accuracy of 63.0%, positive predictive value (PPV) of 54.4%, and negative predictive value (NPV) of 81.2%. Feature selection using LASSO regression identified perivascular adipose tissue (PVAT) attenuation, maximum diameter stenosis (MDS), and fibrotic volume (FV) as the most important predictors. SHAP analysis confirmed MDS as the most influential feature, followed by FV and PVAT. The model demonstrated good calibration, with predicted probabilities aligning closely with observed outcomes. Decision Curve Analysis (DCA) showed that the Random Forest model provided the highest net benefit at higher decision thresholds, supporting its clinical potential for predicting plaque vulnerability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;This study demonstrates the potential of a CTA-based machine learning model for predicting carotid plaque vulnerability, with PVAT, MDS, and FV as key predictors. While the model shows good sensitivity, its moderate specificity suggest","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Redefining First Pass in the New Era of Super-Large-Bore Aspiration Catheters.","authors":"Mayank Goyal, Sophia Hohenstatt","doi":"10.1007/s00062-026-01655-3","DOIUrl":"https://doi.org/10.1007/s00062-026-01655-3","url":null,"abstract":"","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Deep Learning-Based Demyelination Load Segmentation in Metachromatic Leukodystrophy.","authors":"Pascal Martin, Joël Schaerer, Thomas Cajgfinger, Alessandro Delmonte, Benjamin Bender, David Whiteman, C J Malanga, Alexandre Fusellier, David Scott, Joyce Suhy, Hendrik Rosewich, Samuel Groeschel, Luc Bracoud","doi":"10.1007/s00062-026-01652-6","DOIUrl":"https://doi.org/10.1007/s00062-026-01652-6","url":null,"abstract":"<p><strong>Purpose: </strong>Metachromatic leukodystrophy (MLD) is a rare lysosomal storage disorder characterized by progressive white matter demyelination. Quantification of demyelinated white matter on MRI-typically expressed as the demyelination load-serves as a key imaging biomarker of disease burden, enabling objective monitoring beyond visual rating scales. However, current semi-automated pipelines are limited by manual interaction, pediatric brain variability, and differences in MRI acquisition. This study aimed to develop and validate a self-configuring convolutional neural network (CNN) for automated segmentation of demyelinated white matter in MLD and to compare its performance with a conventional semi-automated method across heterogeneous MRI datasets.</p><p><strong>Methods: </strong>An nnU-Net was trained on 189 3D T1- and axial T2-weighted scans from 35 MLD patients using visually controlled conventional masks as ground truth. Independent testing was performed on 130 scans (73 high-resolution 3D, 57 lower-resolution 2D T1-weighted) from 49 patients. Performance was assessed by Dice coefficient, Bland-Altman bias, correlation with Gross Motor Function Classification (GMFC-MLD), MLD MRI severity score, longitudinal consistency, and qualitative review of outliers.</p><p><strong>Results: </strong>CNN-based segmentation showed strong spatial agreement with the reference method, with a median Dice coefficient of 0.82 for 3D T1-weighted scans and 0.75 for 2D scans. Volumetric bias was minimal on Bland-Altman analysis. CNN-derived demyelination load correlated significantly with motor impairment (r_S = 0.38 for 3D and r = 0.56 for 2D; both p < 0.001) and showed a stronger association with the MLD MRI severity score than conventional segmentation (3D: r_S = 0.48 vs. 0.28; 2D: r_S = 0.83 vs. 0.29). Correlations with clinical status were slightly lower (CNN: r_S = 0.38, p < 0.001; conventional: (r_S = 0.26, p < 0.025)) Longitudinal analyses demonstrated stable, monotonic changes over time, and qualitative review revealed fewer boundary misclassifications.</p><p><strong>Conclusion: </strong>The nnU-Net enables fast, reproducible, and clinically meaningful segmentation of demyelinated white matter in MLD. It generalizes across MRI protocols, correlates with motor function, and offers a scalable tool for standardized biomarker extraction in clinical trials and other leukodystrophies.</p>","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distal Thrombectomy Reconsidered: Lessons from DISTALS and ORIENTAL-MEVO.","authors":"Senta Frol, Matija Zupan, René Chapot, Tudor G Jovin","doi":"10.1007/s00062-026-01659-z","DOIUrl":"https://doi.org/10.1007/s00062-026-01659-z","url":null,"abstract":"","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Initial Multicenter Evaluation of a Novel Anchoring Exchange Device: AI-based Quantitative Distal Tip-Motion Analysis.","authors":"Kenichi Kono, Nobuyuki Sakai, Yuya Sakakura, Takeshi Fujimoto, Yoshinori Akiyama, Fuminori Shimizu","doi":"10.1007/s00062-026-01650-8","DOIUrl":"https://doi.org/10.1007/s00062-026-01650-8","url":null,"abstract":"<p><strong>Purpose: </strong>Exchange maneuvers during intracranial angioplasty and stent deployment can cause unintended distal tip motion, potentially leading to vessel injury. We evaluated a novel anchoring exchange device (Medilizer; Medikit, Tokyo, Japan), a 320-cm 0.014-inch wire with a retrievable self-expanding stent at the distal tip, designed to stabilize the distal position during exchange maneuvers and approved for clinical use in Japan.</p><p><strong>Methods: </strong>We compared exchange maneuvers using conventional wires with those using a Medilizer during angioplasty of intracranial stenosis performed at three institutions. Biplane fluoroscopic sequences (frontal and lateral views) were analyzed using an AI-assisted tracking system to quantify the trajectory length of distal tip motion in each view.</p><p><strong>Results: </strong>Seventeen consecutive procedures (10 conventional wire cases and 7 Medilizer cases) were included. In one Medilizer case, the distal tip was outside the field of view in one projection, which was excluded from the analysis. Consequently, 20 conventional wire and 13 Medilizer views were analyzed. The trajectory length of the distal tip was significantly shorter with the Medilizer than with conventional wires (3.3 ± 1.5 mm vs. 35.9 ± 20.0 mm; p < 0.001), corresponding to an approximately 90% reduction. No adverse events occurred in any of the 17 procedures.</p><p><strong>Conclusion: </strong>Even in this initial cohort, the magnitude of improvement in distal tip motion strongly suggests clinically meaningful stability advantages of this anchoring exchange device. Furthermore, AI-assisted quantitative evaluation can provide an objective measure of endovascular device performance, warranting further validation studies.</p>","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MR Elastography Findings in a Cytotoxic Lesion of the Corpus Callosum (CLOCC): a Case Report with Longitudinal Follow-up.","authors":"Hannah Mies, Peter Arthur Ringleb, Ralph Sinkus, Katharina Schregel","doi":"10.1007/s00062-026-01649-1","DOIUrl":"https://doi.org/10.1007/s00062-026-01649-1","url":null,"abstract":"","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}