Deep neural-network based optimization for the design of a multi-element surface magnet for MRI applications

Inverse Problems, vol. 38, pp. 035003

Abstract

We present a combination of a CNN-based encoder with an analytical forward map for solving inverse problems. We call it an encoder-analytic (EA) hybrid model. It does not require a dedicated training dataset and can train itself from the connected forward map in a direct learning fashion. A separate regularization term is not required either, since the forward map also acts as a regularizer. As it is not a generalization model it does not suffer from overfitting. We further show that the model can be customized to either find a specific target solution or one that follows a given heuristic. As an example, we apply this approach to the design of a multi-element surface magnet for low-field magnetic resonance imaging (MRI). We further show that the EA model can outperform the benchmark genetic algorithm model currently used for magnet design in MRI, obtaining almost 10 times better results.

Authors 3

1. Sumit Tewari Aachen C.J. Gorter Center for High Field MRI

   Leiden University Medical Center

   Affiliation as printed

   C.J. Gorter Center for High Field MRI, Radiology, Leiden University Medical Center, Leiden, The Netherlands

2. Sahar Yousefi Aachen Division of Image Processing (LKEB)

   Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands

3. ANDREW G. WEBB corresponding Aachen C.J. Gorter Center for High Field MRI

   Leiden University Medical Center

   Affiliation as printed

   C.J. Gorter Center for High Field MRI, Radiology, Leiden University Medical Center, Leiden, The Netherlands