A single 1-min brain MRI scan for generating multiple synthetic image contrasts in awake children from quantitative relaxometry maps.

Background: Diagnostically adequate contrast and spatial resolution in brain MRI require prolonged scan times, leading to motion artifacts and image degradation in awake children. Rapid multi-parametric techniques can produce diagnostic images in awake children, which could help to avoid the need for sedation.

Objective: To evaluate the utility of a rapid echo-planar imaging (EPI)-based multi-inversion spin and gradient echo (MI-SAGE) technique for generating multi-parametric quantitative brain maps and synthetic contrast images in awake pediatric participants.

Evaluating feasibility of high resolution T1-perfusion MRI with whole brain coverage using compressed SENSE: Application to glioma grading.

Purpose: To evaluate the efficacy of optimized T1-Perfusion MRI protocol (protocol-2) with whole brain coverage and improved spatial resolution using Compressed-SENSE (CSENSE) to differentiate high-grade-glioma (HGG) and low-grade-glioma (LGG) and to compare it with the conventional protocol (protocol-1) with partial brain coverage used in our center.

Methods: This study included MRI data from 5 healthy volunteers, a phantom and 126 brain tumor patients. Current study had two parts: To analyze the effect of CSENSE on 3D-T1-weighted (W) fast-field-echo (FFE) images, T1-W, dual-PDT2-W turbo-spin-echo images and T1 maps, and to evaluate the performance of high resolution T1-Perfusion MRI protocol with whole brain coverage optimized using CSENSE.

Evaluation of Gall Bladder Stones Using Susceptibility Weighted Imaging.

Objective: To evaluate the visualization of gallbladder stones on susceptibility-weighted imaging (SWI).

Materials And Methods: Imaging data from 47 patients who underwent clinically indicated cholecystectomy was reviewed. Breath-hold SWI was added to the magnetic resonance imaging protocol and magnitude and phase data was reviewed for gall-stones visualization.

Glioma grading using a machine-learning framework based on optimized features obtained from T perfusion MRI and volumes of tumor components.

Background: Glioma grading between intermediate grades (Grade II vs. III and Grade III vs. IV) as well as multiclass grades (Grade II vs.