Deep learning-based reconstruction of in vivo pelvis conductivity with a 3D patch-based convolutional neural network trained on simulated MR data

Abstract

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 obtain $\left(B_1^{+}\right)$ and transceive phase (ϕ^± ). Simulated $\left(B_1^{+}\right)$ 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.

Keywords: MR simulations; conductivity mapping; deep learning EPT; pelvis MRI.