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

ABSTRACT:

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.

Methods

Fast high-resolution whole-brain metabolic imaging was performed at 3.4 mm³ isotropic resolution with acquisition times between 4:11-9:21 min:s using ECCENTRIC pu

SUBMITTER: Weiser PJ 

PROVIDER: S-EPMC11952141 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature