Synthetic T2-weighted fat sat based on a generative adversarial network shows potential for scan time reduction in spine imaging in a multicenter test dataset.

Objectives: T2-weighted (w) fat sat (fs) sequences, which are important in spine MRI, require a significant amount of scan time. Generative adversarial networks (GANs) can generate synthetic T2-w fs images. We evaluated the potential of synthetic T2-w fs images by comparing them to their true counterpart regarding image and fat saturation quality, and diagnostic agreement in a heterogenous, multicenter dataset.

Implementation of GAN-Based, Synthetic T2-Weighted Fat Saturated Images in the Routine Radiological Workflow Improves Spinal Pathology Detection.

(1) Background and Purpose: In magnetic resonance imaging (MRI) of the spine, T2-weighted (T2-w) fat-saturated (fs) images improve the diagnostic assessment of pathologies. However, in the daily clinical setting, additional T2-w fs images are frequently missing due to time constraints or motion artifacts. Generative adversarial networks (GANs) can generate synthetic T2-w fs images in a clinically feasible time.

Mapping of Arithmetic Processing by Navigated Repetitive Transcranial Magnetic Stimulation in Patients with Parietal Brain Tumors and Correlation with Postoperative Outcome.

Background: Preserving functionality is important during neurosurgical resection of brain tumors. Specialized centers also map further brain functions apart from motor and language functions, such as arithmetic processing (AP). The mapping of AP by navigated repetitive transcranial magnetic stimulation (nrTMS) in healthy volunteers has been reported.