AIMC Topic: Magnetic Resonance Imaging
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Radiomics Analysis of Different Machine Learning Models based on Multiparametric MRI to Identify Benign and Malignant Testicular Lesions.
Academic radiology
Feb 3, 2025
RATIONALE AND OBJECTIVES: To develop and validate a machine learning-based prediction model for the use of multiparametric magnetic resonance imaging(MRI) to predict benign and malignant lesions in the testis.
Harmonizing flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization.
Medical image analysis
Feb 2, 2025
Lack of standardization and various intrinsic parameters for magnetic resonance (MR) image acquisition results in heterogeneous images across different sites and devices, which adversely affects the generalization of deep neural networks. To alleviat...
Multi-knowledge informed deep learning model for multi-point prediction of Alzheimer's disease progression.
Neural networks : the official journal of the International Neural Network Society
Feb 1, 2025
The diagnosis of Alzheimer's disease (AD) based on visual features-informed by clinical knowledge has achieved excellent results. Our study endeavors to present an innovative and detailed deep learning framework designed to accurately predict the pro...
SDR-Former: A Siamese Dual-Resolution Transformer for liver lesion classification using 3D multi-phase imaging.
Neural networks : the official journal of the International Neural Network Society
Feb 1, 2025
Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion class...
Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.
NeuroImage
Feb 1, 2025
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...
Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.
Magma (New York, N.Y.)
Feb 1, 2025
OBJECT: Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction...
Statin use and longitudinal bone marrow lesion burden: analysis of knees without osteoarthritis from the Osteoarthritis Initiative study.
Skeletal radiology
Jan 31, 2025
OBJECTIVES: Knee subchondral bone marrow lesions (BMLs) are one of the hallmark features of structural osteoarthritis (OA) and are potential targets for statins' disease-modifying effect. We aimed to determine the association between statin use and l...
Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks (part II).
Journal of the neurological sciences
Jan 31, 2025
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
Jan 31, 2025
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...
Multiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma.
European radiology
Jan 30, 2025
OBJECTIVES: We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification.
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