Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution

• URL: http://arxiv.org/abs/2305.07524v1
• Date: Fri, 12 May 2023 14:40:25 GMT
• Title: Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution
• Authors: Hoai Nam Dang, Vladimir Golkov, Thomas Wimmer, Daniel Cremers, Andreas Maier and Moritz Zaiss
• Abstract summary: Current MRI super-resolution (SR) methods only use existing contrasts acquired from typical clinical sequences as input for the neural network (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.
• Score: 44.52688267348063
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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