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Magnetic Resonance Imaging

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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
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
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
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
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.)
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
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
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
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).

Dilated SE-DenseNet for brain tumor MRI classification.

Scientific reports
In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitat...

Data- and Physics-Driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies.

IEEE reviews in biomedical engineering
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advan...