Predicting slot lengths of MRI exams to decrease observed discrepancies between planning and execution.

This retrospective study aimed to reveal discrepancies between planned (T) and actual (T) slot lengths of abdomen MRI exams, and to improve T by predicting slot lengths via a machine learning algorithm. T and T were retrieved from RIS and modality logfiles, respectively, covering 3038 MRI exams of 17 protocols performed at an abdomen department. Comparisons showed that 30% of exams exceeded planned slot lengths.

Accelerated liver water T mapping using single-shot continuous inversion-recovery spiral imaging.

Purpose: Liver T mapping techniques typically require long breath holds or long scan time in free-breathing, need correction for inhomogeneities and process composite (water and fat) signals. The purpose of this work is to accelerate the multi-slice acquisition of liver water selective T (wT) mapping in a single breath hold, improving the k-space sampling efficiency.

Methods: The proposed continuous inversion-recovery (IR) Look-Locker methodology combines a single-shot gradient echo spiral readout, Dixon processing and a dictionary-based analysis for liver wT mapping at 3 T.

Analysis and predictability of technologists' perception of MR exam complexity.

Introduction: As MRI becomes a routine clinical diagnostic method, its complexity of techniques, protocols and scanning is growing. On the other hand, aggravated by the ubiquitous shortage of workforce, technologists' stress level and burnout rates are increasing. In this context, our study aims to shed light on technologists' perceived complexity of MR exams, by analyzing a multidimensional dataset composed of workflow, patient, and operational details, and further predicting perceived exam complexity.

High-fidelity direct contrast synthesis from magnetic resonance fingerprinting.

Purpose: This work was aimed at proposing a supervised learning-based method that directly synthesizes contrast-weighted images from the Magnetic Resonance Fingerprinting (MRF) data without performing quantitative mapping and spin-dynamics simulations.

Methods: To implement our direct contrast synthesis (DCS) method, we deploy a conditional generative adversarial network (GAN) framework with a multi-branch U-Net as the generator and a multilayer CNN (PatchGAN) as the discriminator. We refer to our proposed approach as N-DCSNet.

Comparing myelin-sensitive magnetic resonance imaging measures and resulting g-ratios in healthy and multiple sclerosis brains.

The myelin concentration and the degree of myelination of nerve fibers can provide valuable information on the integrity of human brain tissue. Magnetic resonance imaging (MRI) of myelin-sensitive parameters can help to non-invasively evaluate demyelinating diseases such as multiple sclerosis (MS). Several different myelin-sensitive MRI methods have been proposed to determine measures of the degree of myelination, in particular the g-ratio.