A miniscope with cloud-based operation and multicontrast capabilities enables continuous recording of neuronal activity, hemodynamics or cellular dynamics over multiple days. This allows monitoring br...

Background: Parkinson's disease (PD) is characterized by dopaminergic neuron degeneration, leading to motor and neuropsychological symptoms. PD is clinically divided into tremor-dominant (TD) and post...

SUMMARY: The quantitative multicontrast atherosclerosis characterization (qMATCH) technique enables high-resolution 3D multicontrast evaluation of carotid atherosclerosis, but the optimal acquisition orientation for reproducible image quality remains unclear. This prospective study compared image quality, motion robustness, and reproducibility between transverse and coronal qMATCH acquisitions. Thirty participants with moderate-to-severe carotid stenosis each underwent 2 scans, totaling 60 imaging sessions. Coronal imaging demonstrated significantly fewer motion artifacts compared with transverse imaging (34.7% versus 41.5%; P < .05) and yielded higher image quality scores (2.78 versus 2.72; P < .05) and arterial wall apparent SNR (5.10 versus 4.36; P < .05). In motion-corrupted images, coronal imaging also showed superior image quality (2.75 versus 2.65; P < .05) and arterial wall apparent SNR (4.89 versus 4.03; P < .05). Reproducibility for T1/T2 quantification was better for coronal acquisitions, particularly in motion-affected cases. These findings suggest that coronal qMATCH is more robust for motion-prone patients and better suited for longitudinal carotid imaging studies. aCNR : apparent contrast to noise ratio aSNR : apparent SNR ICC : intraclass correlation coefficient LoA : limits of agreement qMATCH : quantitative multicontrast atherosclerosis characterization RADS : Reporting and Data System

YouTube video by Center for Physical Genomics and Engineering

YouTube video by Center for Physical Genomics and Engineering

BACKGROUND AND PURPOSE: Ischemic stroke poses a significant global health burden. Accurately identifying symptomatic carotid atherosclerotic plaques, beyond relying solely on stenosis degree, remains a critical challenge for precise stroke risk stratification. We aimed to develop and validate a deep learning radiomics (DLR) signature based on multicontrast MRI to identify symptomatic carotid plaques accurately. MATERIALS AND METHODS: In this retrospective multicenter study, 409 carotid arteries from 355 patients with carotid atherosclerosis were enrolled (219 training, 95 internal validation, 95 external test). Deep learning (DL) and radiomics features were extracted and combined from automatically segmented plaque regions on multicontrast MRI. The optimized DLR signature derived from a 3-stage feature selection pipeline was leveraged to train diverse machine learning classifiers for robust identification of symptomatic carotid plaques. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and compared against clinical models, radiomics-only models, and DL-only models. Subgroup analysis across stenosis severities and comparison of MRI-based American Heart Association lesion types between DLR-defined risk groups were performed. RESULTS: The DLR model with logistic regression demonstrated excellent performance in identifying symptomatic plaques, achieving AUROCs of 0.975 (95% CI, 0.954–0.992), 0.933 (95% CI, 0.876–0.976), and 0.881 (95% CI, 0.807–0.939) in the training, internal validation, and external validation cohorts, respectively. It significantly outperformed the clinical model (AUROCs of 0.701, 0.749, 0.711; P < .05), radiomics-only model (AUROCs of 0.877, 0.839, 0.789; P < .05), and DL-only model (AUROCs of 0.948, 0.894, 0.845; P < .05 in training/external). Performance remained consistently high across stenosis severity subgroups (AUROCs of 0.895–0.982 for severe, 0.863–0.971 for mild-moderate stenosis). DLR-defined symptomatic groups showed significantly higher prevalence of complex type VI lesions (internal: 50.0% versus 14.8%, P < .001; external: 48.7% versus 20.7%, P = .004) and lower prevalence of predominantly calcified type VII lesions (external: 8.1% versus 43.1%, P < .001) compared with asymptomatic groups. CONCLUSIONS: The developed multicontrast MRI-based DLR signature provides a highly accurate and robust tool for the automated identification of symptomatic carotid plaques, underscoring its potential value as a noninvasive tool to guide personalized stroke prevention strategies. AUROC : area under the receive operating characteristic AHA : American Heart Association DCA : decision curve analysis DL : deep learning DLR : deep learning radiomics Grad-CAM : gradient-weighted class activation mapping HR-MRI : high-resolution MRI IPH : intraplaque hemorrhage IS : ischemic stroke LRNC : lipid-rich necrotic core TOAST : Trial of Org 10172 in Acute Stroke Treatment