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

ABSTRACT:

Purpose

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.

Methods

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

Results showed sub

SUBMITTER: Vasylechko SD 

PROVIDER: S-EPMC8627432 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature