AIMC Topic: Magnetic Resonance Imaging

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The clinical application of neuro-robot in the resection of epileptic foci: a novel method assisting epilepsy surgery.

Journal of robotic surgery
During surgery for foci-related epilepsy, neurosurgeons face significant difficulties in identifying and resecting MRI-negative or deep-seated epileptic foci. Here, we present a neuro-robotic navigation system that is specifically designed for resect...

Deep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study.

European journal of radiology
BACKGROUND: Although systems such as Prostate Imaging Quality (PI-QUAL) have been proposed for quality assessment, visual evaluations by human readers remain somewhat inconsistent, particularly among less-experienced readers.

Review on deep learning fetal brain segmentation from Magnetic Resonance images.

Artificial intelligence in medicine
Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging ...

A Feasibility Study on Deep Learning Reconstruction to Improve Image Quality With PROPELLER Acquisition in the Setting of T2-Weighted Gynecologic Pelvic Magnetic Resonance Imaging.

Journal of computer assisted tomography
OBJECTIVES: Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction technique) for 3 T magnetic resonance imaging of the female pelvis.

Joint liver and hepatic lesion segmentation in MRI using a hybrid CNN with transformer layers.

Computer methods and programs in biomedicine
UNLABELLED: Backgound and Objective: Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants w...

Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations.

Nature communications
Deep neural networks (DNNs) optimized for visual tasks learn representations that align layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this finding is that hierarchical representations are necessary to accu...

Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study.

European radiology experimental
BACKGROUND: International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast as a supplemental diagnostic tool. In our study, we tested the app...

Deep learning-based Lorentzian fitting of water saturation shift referencing spectra in MRI.

Magnetic resonance in medicine
PURPOSE: Water saturation shift referencing (WASSR) Z-spectra are used commonly for field referencing in chemical exchange saturation transfer (CEST) MRI. However, their analysis using least-squares (LS) Lorentzian fitting is time-consuming and prone...