AIMC Topic: Magnetic Resonance Imaging
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A Deep Learning-Based Automatic Collateral Assessment in Patients with Acute Ischemic Stroke.
Translational stroke research
May 21, 2022
This study aimed to develop a supervised deep learning (DL) model for grading collateral status from dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) images from patients with large vessel occlusion (LVO) acute ischemic stroke (...
Hippocampal representations for deep learning on Alzheimer's disease.
Scientific reports
May 21, 2022
Deep learning offers a powerful approach for analyzing hippocampal changes in Alzheimer's disease (AD) without relying on handcrafted features. Nevertheless, an input format needs to be selected to pass the image information to the neural network, wh...
Deep Learning and Imaging for the Orthopaedic Surgeon: How Machines "Read" Radiographs.
The Journal of bone and joint surgery. American volume
May 20, 2022
➤: In the not-so-distant future, orthopaedic surgeons will be exposed to machines that begin to automatically "read" medical imaging studies using a technology called deep learning.
Deep learning-based 3D MRI contrast-enhanced synthesis from a 2D noncontrast T2Flair sequence.
Medical physics
May 20, 2022
PURPOSE: Gadolinium-based contrast agents (GBCAs) have been successfully applied in magnetic resonance (MR) imaging to facilitate better lesion visualization. However, gadolinium deposition in the human brain raised widespread concerns recently. On t...
Deep learning network for integrated coil inhomogeneity correction and brain extraction of mixed MRI data.
Scientific reports
May 20, 2022
Magnetic Resonance Imaging (MRI) has been widely used to acquire structural and functional information about the brain. In a group- or voxel-wise analysis, it is essential to correct the bias field of the radiofrequency coil and to extract the brain ...
Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images.
Computational intelligence and neuroscience
May 20, 2022
In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the development of a computer-specific pattern recogni...
A Novel U-Net Based Deep Learning Method for 3D Cardiovascular MRI Segmentation.
Computational intelligence and neuroscience
May 20, 2022
Medical multiobjective image segmentation aims to group pixels to form multiple regions based on the different properties of the medical images. Segmenting the 3D cardiovascular magnetic resonance (CMR) images is still a challenging task owing to sev...
A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer.
Journal of magnetic resonance imaging : JMRI
May 19, 2022
BACKGROUND: Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI-based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear...
Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images.
Magma (New York, N.Y.)
May 18, 2022
OBJECTIVE: We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed long-axis (RLA) images of the center of the RV region in routine short axis (SA) cardiovascular magnetic reson...
Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence.
Diagnostic and interventional imaging
May 18, 2022
PURPOSE: Acceleration of MRI acquisitions and especially of T2-weighted sequences is essential to reduce the duration of MRI examinations but also kinetic artifacts in liver imaging. The purpose of this study was to compare the acquisition time and t...
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