AIMC Topic: Magnetic Resonance Imaging
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UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases.
NeuroImage
Jan 30, 2025
In this study, we present a comprehensive pipeline to train and compare a broad spectrum of machine learning and deep learning brain clocks, integrating diverse preprocessing strategies and correction terms. Our analysis also includes established met...
High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI.
NeuroImage
Jan 30, 2025
Choroid plexus (CP) is an important brain structure that produces cerebrospinal fluid (CSF). CP perfusion has been studied using multi-delay arterial spin labeling (MD-ASL) in adults but not in pediatric populations due to the challenge of small CP s...
Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.
European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
Jan 30, 2025
PURPOSE: Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of the spinal or nerve root canal due to degenerative changes. Physicians use MRI scans to determine the severity of stenosis, occasionally complementing i...
Deep Learning-Based Precontrast CT Parcellation for MRI-Free Brain Amyloid PET Quantification.
Clinical nuclear medicine
Jan 29, 2025
PURPOSE: This study aimed to develop a deep learning (DL) model for brain region parcellation using CT data from PET/CT scans to enable accurate amyloid quantification in 18 F-FBB PET/CT without relying on high-resolution MRI.
Phase-contrast magnetic resonance imaging-based predictive modelling for surgical outcomes in patients with Chiari malformation type 1 with syringomyelia: a machine learning study.
Clinical radiology
Jan 29, 2025
AIM: Prospective outcome prediction plays a crucial role in guiding preoperative decision-making in patients with Chiari malformation type I (CM-Ⅰ) with syringomyelia. Here, we aimed to develop a predictive model for postoperative outcomes in patient...
DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks.
Medical image analysis
Jan 29, 2025
Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix a...
Graph Neural Network Learning on the Pediatric Structural Connectome.
Tomography (Ann Arbor, Mich.)
Jan 29, 2025
PURPOSE: Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has proven useful for studying cognitive function and impairment. While graph neural networks (GNNs), s...
Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.
Frontiers in immunology
Jan 29, 2025
PURPOSE: Distinguishing between Osteonecrosis of the femoral head (ONFH) and Osteoarthritis (OA) can be subjective and vary between users with different backgrounds and expertise. This study aimed to construct and evaluate several Radiomics-based mac...
M2OCNN: Many-to-One Collaboration Neural Networks for simultaneously multi-modal medical image synthesis and fusion.
Computer methods and programs in biomedicine
Jan 28, 2025
BACKGROUND AND OBJECTIVE: Acquiring comprehensive information from multi-modal medical images remains a challenge in clinical diagnostics and treatment, due to complex inter-modal dependencies and missing modalities. While cross-modal medical image s...
Artificial intelligence for brain neuroanatomical segmentation in magnetic resonance imaging: A literature review.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Jan 28, 2025
PURPOSE: This literature review aims to synthesise current research on the application of artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in magnetic resonance imaging (MRI).
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