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Explaining cognitive function in multiple sclerosis through networks of grey and white matter features: a joint independent component analysis.
J Neurol
January 2025
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.
Cognitive impairment (CI) in multiple sclerosis (MS) is only partially explained by whole-brain volume measures, but independent component analysis (ICA) can extract regional patterns of damage in grey matter (GM) or white matter (WM) that have proven more closely associated with CI. Pathology in GM and WM occurs in parallel, and so patterns can span both. This study assessed whether joint-ICA of GM and WM features better explained cognitive function compared to single-tissue ICA.
View Article and Find Full Text PDFSoma and neurite density abnormalities of paramagnetic rim lesions and core-sign lesions in multiple sclerosis.
J Neurol
January 2025
Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Background: In multiple sclerosis (MS), susceptibility-weighted imaging (SWI) may reveal white matter lesions (WML) with a paramagnetic rim ("paramagnetic rim lesions" [PRLs]) or diffuse hypointensity ("core-sign lesions"), reflecting different stages of WML evolution.
Objective: Using the soma and neurite density imaging (SANDI) model on diffusion-weighted magnetic resonance imaging (MRI), we characterized microstructural abnormalities of MS PRLs and core-sign lesions and their clinical relevance.
Methods: Forty MS patients and 20 healthy controls (HC) underwent a 3 T brain MRI.
Rare pancreatic cystic neoplasms: A pictorial review.
Eur J Radiol Open
June 2025
Department of Radiological Nuclear and Laboratory Medicine - Pisa University Hospital, Via Paradisa 2, Pisa 56124, Italy.
Since rare pancreatic cystic tumors may differ from common pancreatic cystic neoplasms in terms of treatment plan and prognosis, the differential diagnosis of these diseases is clinically relevant. Various imaging tests play an important role in the differential diagnosis of rare cystic pancreatic tumors, but accurately distinguishing these diseases solely on the basis of imaging findings is challenging. The purpose of this pictorial review is to present CT and in particular MR imaging features of rare pancreatic cystic tumors and discuss potential elements for differential diagnosis.
View Article and Find Full Text PDFNeuronal intranuclear inclusion disease with subclinical peripheral neuropathy: A case report.
Medicine (Baltimore)
November 2024
Department of Neurology, The People's Hospital of Suzhou New District, Suzhou, Jiangsu, China.
Rationale: Neuronal intranuclear inclusion disease (NIID) is a slowly progressing neurodegenerative disease with various manifestations and high heterogeneity. Clinical characteristics, imaging, skin biopsy, and genetic testing are necessary for its diagnosis. Electromyography may also be a useful tool for diagnosing NIID.
View Article and Find Full Text PDFDeep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer.
Radiology
January 2025
From the Department of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen University, Taoyuan Rd No. 89, Nanshan District, Shenzhen 518000, Guangdong, China (H.H., Z.D., Y.Q.); Medical AI Laboratory and Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China (J.M., R.L., B.H.); Department of Medical Imaging, People's Hospital of Longhua, Shenzhen, Guangdong, China (X.P., Y.Z.); and Department of Radiology, Shenzhen People's Hospital, Shenzhen, Guangdong, China (D.Z., G.H.).
Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1.
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