OBJECTIVE: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI.
OBJECTIVE: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2) and a conventional T...
BACKGROUND: The BraTS Generalizability Across Tumors (BraTS-GoAT) initiative addresses the critical need for robust and generalizable models in brain tumor segmentation. Despite advancements in automated segmentation techniques, the variability in tu...
Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharm...
Purpose To evaluate and compare the performance of different artificial intelligence (AI) models in differentiating between benign and malignant breast tumors at diffusion-weighted imaging (DWI), including comparison with radiologist assessments. Mat...
Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired betwe...
BACKGROUND: Wireless capsule endoscopy (WCE) has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology. However, the complexity of the digestive ...
International journal of rheumatic diseases
Dec 1, 2024
OBJECTIVES: The aim of this study is to develop and validate a model for predicting axial spondyloarthritis (axSpA) based on sacroiliac joint (SIJ)-MRI imaging findings and clinical risk factors.
Purpose To assess the influence of deep learning (DL)-based image reconstruction on acquisition time, volumetric results, and image quality of cine sequences in cardiac MRI. Materials and Methods This prospective study (performed from January 2023 to...
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