Self-supervised IVIM DWI parameter estimation with a physics based forward model

To assess the robustness and repeatability of intravoxel incoherent motion model (IVIM) parameter estimation for the diffusion-weighted MRI in the abdominal organs under the constraints of noisy diffusion signal using a novel neural network method.

Clinically acquired abdominal scans of Crohn's disease patients were retrospectively analyzed with regions segmented in the kidney cortex, spleen, liver, and bowel. A novel IVIM parameter fitting method based on the principle of a physics guided self-supervised convolutional neural network that does not require reference parameter estimates for training was compared to a conventional non-linear least squares (NNLS) algorithm, and a voxelwise trained artificial neural network (ANN).

Results showed substantial increase in parameter robustness to the noise corrupted signal. In an intra-session repeatability experiment, the proposed method showed reduced coefficient of variation (CoV) over multiple acquisitions in comparison to conventional NLLS method and comparable performance to ANN. The use of D and f estimates from the proposed method led to the smallest misclassification error in linear discriminant analysis for characterization between normal and abnormal Crohn's disease bowel tissue. The fitting of $$ parameter remains to be challenging.

The proposed method yields robust estimates of D and f IVIM parameters under the constraints of noisy diffusion signal. This indicates a potential for the use of the proposed method in conjunction with accelerated DW-MRI acquisition strategies, which would typically result in lower signal to noise ratio.