Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.

Introduction: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction embedded in a physical model within an end-to-end automated processing pipeline to obtain high-quality metabolic maps.

WALINET: A water and lipid identification convolutional neural network for nuisance signal removal in MR spectroscopic imaging.

Purpose: Proton magnetic resonance spectroscopic imaging ( -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain -MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution -MRSI to accurately remove lipid and water signals while preserving the metabolite signal.

Predicting dynamic, motion-related changes in B field in the brain at a 7T MRI using a subject-specific fine-trained U-net.

Purpose: Subject movement during the MR examination is inevitable and causes not only image artifacts but also deteriorates the homogeneity of the main magnetic field (B ), which is a prerequisite for high quality data. Thus, characterization of changes to B , for example induced by patient movement, is important for MR applications that are prone to B inhomogeneities.

Methods: We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real-time correction.