Joint Sequence Optimization Beats Pure Neural Network Approaches for Super-Resolution TSE
MCML Authors
Vladimir Golkov
Dr.
Daniel Cremers
Prof. Dr.
Director
Abstract
Vladimir Golkov
Dr.
Daniel Cremers
Prof. Dr.
Director
Abstract
Current MRI super-resolution (SR) methods only use existing contrasts acquired from typical clinical sequences as input for the neural network (NN). In turbo spin echo sequences (TSE) the sequence parameters can have a strong influence on the actual resolution of the acquired image and have consequently a considera-ble impact on the performance of the NN. We propose a known-operator learning approach to perform an end-to-end optimization of MR sequence and neural net-work parameters for SR-TSE. This MR-physics-informed training procedure jointly optimizes the radiofrequency pulse train of a proton density- (PD-) and T2-weighted TSE and a subsequently applied convolutional neural network to predict the corresponding PDw and T2w super-resolution TSE images. The found radiofrequency pulse train designs generate an optimal signal for the NN to perform the SR task. Our method generalizes from the simulation-based optimi-zation to in vivo measurements and the acquired physics-informed SR images show higher correlation with a time-consuming segmented high-resolution TSE sequence compared to a pure network training approach.
inproceedings
ISMRM 2024
International Society for Magnetic Resonance in Medicine Annual Meeting. Singapore, May 04-09, 2024.Authors
H. N. Dang • V. Golkov • J. Endres • S. Weinmüller • F. Glang • T. Wimmer • D. Cremers • A. Dörfler • A. Maier • M. ZaissLinks
URLResearch Area
BibTeXKey: DGE+24