AIMC Topic: Magnetic Resonance Imaging

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An appraisal of the performance of AI tools for chronic stroke lesion segmentation.

Computers in biology and medicine
Automated demarcation of stoke lesions from monospectral magnetic resonance imaging scans is extremely useful for diverse research and clinical applications, including lesion-symptom mapping to explain deficits and predict recovery. There is a signif...

Super-resolution of magnetic resonance images using Generative Adversarial Networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Magnetic Resonance Imaging (MRI) typically comes at the cost of small spatial coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less measurements yields the potential to relax these inherent forfeits. Recent breakthr...

Detection of Pacemaker and Identification of MRI-conditional Pacemaker Based on Deep-learning Convolutional Neural Networks to Improve Patient Safety.

Journal of medical systems
With the increased availability of magnetic resonance imaging (MRI) and a progressive rise in the frequency of cardiac device implantation, there is an increased chance that patients with implanted cardiac devices require MRI examination during their...

Deep learning pipeline for quality filtering of MRSI spectra.

NMR in biomedicine
With the rise of novel 3D magnetic resonance spectroscopy imaging (MRSI) acquisition protocols in clinical practice, which are capable of capturing a large number of spectra from a subject's brain, there is a need for an automated preprocessing pipel...

Numerical and Clinical Evaluation of the Robustness of Open-source Networks for Parallel MR Imaging Reconstruction.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Deep neural networks (DNNs) for MRI reconstruction often require large datasets for training. Still, in clinical settings, the domains of datasets are diverse, and how robust DNNs are to domain differences between training and testing datase...

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.

Investigative radiology
OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power...

Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability.

European radiology
OBJECTIVES: To evaluate a fully automatic deep learning system to detect and segment clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different institutions not contributing to training of the system.

Assessment of deep learning-based reconstruction on T2-weighted and diffusion-weighted prostate MRI image quality.

European journal of radiology
PURPOSE: To evaluate the impact of a commercially available deep learning-based reconstruction (DLR) algorithm with varying combinations of DLR noise reduction settings and imaging parameters on quantitative and qualitative image quality, PI-RADS cla...