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Magnetic Resonance Imaging

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Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks (part II).

Journal of the neurological sciences
Neural networks (NNs) possess the capability to learn complex data relationships, recognize inherent patterns by emulating human brain functions, and generate predictions based on novel data. We conducted deep learning utilizing an NN to differentiat...

AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions.

Artificial intelligence in medicine
Autism Spectrum Disorder (ASD) is a neurological condition, with recent statistics from the CDC indicating a rising prevalence of ASD diagnoses among infants and children. This trend emphasizes the critical importance of early detection, as timely di...

JotlasNet: Joint tensor low-rank and attention-based sparse unrolling network for accelerating dynamic MRI.

Magnetic resonance imaging
Joint low-rank and sparse unrolling networks have shown superior performance in dynamic MRI reconstruction. However, existing works mainly utilized matrix low-rank priors, neglecting the tensor characteristics of dynamic MRI images, and only a global...

Self-supervised 3D medical image segmentation by flow-guided mask propagation learning.

Medical image analysis
Despite significant progress in 3D medical image segmentation using deep learning, manual annotation remains a labor-intensive bottleneck. Self-supervised mask propagation (SMP) methods have emerged to alleviate this challenge, allowing intra-volume ...

Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Magnetic Resonance Imaging (MRI) generates medical images of multiple sequences, i.e., multimodal, from different contrasts. However, noise will reduce the quality of MR images, and then affect the doctor's diagnosis of diseases. Existing filtering m...

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
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
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
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...