AIMC Topic: Magnetic Resonance Imaging
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External validation of the deep learning system "SpineNet" for grading radiological features of degeneration on MRIs of the lumbar spine.
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
Jul 14, 2022
BACKGROUND: Magnetic resonance imaging (MRI) is used to detect degenerative changes of the lumbar spine. SpineNet (SN), a computer vision-based system, performs an automated analysis of degenerative features in MRI scans aiming to provide high accura...
Editorial for "Cognitive Challenges Are Better in Distinguishing Binge From Nonbinge Drinkers: An Exploratory Deep-Learning Study of fMRI Data of Multiple Behavioral Tasks and Resting State".
Journal of magnetic resonance imaging : JMRI
Jul 13, 2022
Compact pediatric cardiac magnetic resonance imaging protocols.
Pediatric radiology
Jul 12, 2022
Cardiac MRI is in many respects an ideal modality for pediatric cardiovascular imaging, enabling a complete noninvasive assessment of anatomy, morphology, function and flow in one radiation-free and potentially non-contrast exam. Nonetheless, traditi...
Development and clinical applicability of MRI-based 3D prostate models in the planning of nerve-sparing robot-assisted radical prostatectomy.
Journal of robotic surgery
Jul 12, 2022
The interpretation of conventional MRI may be limited by the two-dimensional presentation of the images. To develop patient-specific MRI prostate-based virtual and three-dimensional (3D)-printed models. To assess the association between 3D imaging an...
A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features.
Sensors (Basel, Switzerland)
Jul 12, 2022
Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as "black boxes". Prediction ...
MRI-based and robot-assisted stereotactic biopsy with intraoperative CT imaging.
Acta neurochirurgica
Jul 11, 2022
BACKGROUND: As a pioneer center in the field of stereotaxy, Sainte-Anne school has always advocated the use of intraoperative imaging for stereotactic procedures to optimize both safety and accuracy. With the advent of intraoperative mobile CT unit, ...
Commercially Available Deep-learning-reconstruction of MR Imaging of the Knee at 1.5T Has Higher Image Quality Than Conventionally-reconstructed Imaging at 3T: A Normal Volunteer Study.
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Jul 9, 2022
PURPOSE: This study aimed to evaluate whether the image quality of 1.5T magnetic resonance imaging (MRI) of the knee is equal to or higher than that of 3T MRI by applying deep learning reconstruction (DLR).
Cognitive Challenges Are Better in Distinguishing Binge From Nonbinge Drinkers: An Exploratory Deep-Learning Study of fMRI Data of Multiple Behavioral Tasks and Resting State.
Journal of magnetic resonance imaging : JMRI
Jul 9, 2022
BACKGROUND: Studies have identified imaging markers of binge drinking. Functional connectivity during both task challenges and resting state was shown to distinguish binge and nonbinge drinkers. However, no studies have compared the efficacy of task ...
SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans.
Sensors (Basel, Switzerland)
Jul 8, 2022
In this paper, we propose a novel squeeze M-SegNet (SM-SegNet) architecture featuring a fire module to perform accurate as well as fast segmentation of the brain on magnetic resonance imaging (MRI) scans. The proposed model utilizes uniform input pat...
Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity.
Academic radiology
Jul 6, 2022
RATIONALE AND OBJECTIVES: To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interp...
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