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Deep learning‐based reconstruction of in vivo pelvis conductivity with a 3D patch‐based convolutional neural network trained on simulated MR data
Author(s) -
Gavazzi Soraya,
den Berg Cornelis A. T.,
Savenije Mark H. F.,
Kok H. Petra,
Boer Peter,
Stalpers Lukas J. A.,
Lagendijk Jan J. W.,
Crezee Hans,
Lier Astrid L. H. M. W.
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28285
Subject(s) - conductivity , ground truth , convolutional neural network , in vivo , materials science , computer science , pattern recognition (psychology) , mathematics , biomedical engineering , algorithm , artificial intelligence , medicine , physics , quantum mechanics , microbiology and biotechnology , biology
Purpose To demonstrate that mapping pelvis conductivity at 3T with deep learning (DL) is feasible. Methods 210 dielectric pelvic models were generated based on CT scans of 42 cervical cancer patients. For all dielectric models, electromagnetic and MR simulations with realistic accuracy and precision were performed to obtainB 1 +and transceive phase ( ϕ ± ). SimulatedB 1 +and ϕ ± served as input to a 3D patch‐based convolutional neural network, which was trained in a supervised fashion to retrieve the conductivity. The same network architecture was retrained using only ϕ ± in input. Both network configurations were tested on simulated MR data and their conductivity reconstruction accuracy and precision were assessed. Furthermore, both network configurations were used to reconstruct conductivity maps from a healthy volunteer and two cervical cancer patients. DL‐based conductivity was compared in vivo and in silico to Helmholtz‐based (H‐EPT) conductivity. Results Conductivity maps obtained from both network configurations were comparable. Accuracy was assessed by mean error (ME) with respect to ground truth conductivity. On average, ME < 0.1 Sm −1 for all tissues. Maximum MEs were 0.2 Sm −1 for muscle and tumour, and 0.4 Sm −1 for bladder. Precision was indicated with the difference between 90 th and 10 th conductivity percentiles, and was below 0.1 Sm −1 for fat, bone and muscle, 0.2 Sm −1 for tumour and 0.3 Sm −1 for bladder. In vivo, DL‐based conductivity had median values in agreement with H‐EPT values, but a higher precision. Conclusion Anatomically detailed, noise‐robust 3D conductivity maps with good sensitivity to tissue conductivity variations were reconstructed in the pelvis with DL.