Serge Vasylechko1,2, Simon K. Warfield1,2, Onur Afacan1,2, and Sila Kurugol1,2
1Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, United States, 2Harvard Medical School, Boston, MA, United States
1Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, United States, 2Harvard Medical School, Boston, MA, United States
Assessment of 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 training method.
Figure 1. A network structure of the proposed method for a physics guided IVIM parameter estimation with a self supervised U-net architecture. An input consists of a 3 dimensional array, which is a concatenation of a 2D slice acquired at 7 b-values. The input is passed through a U-net to produce 4 IVIM parameter estimates at each pixel. In the second stage, the parameter estimates are used in the IVIM equation to reconstruct the original input image array. L2 loss between the output and the original input is used to propagate gradients backwards through the network.
Figure 2. An example of IVIM parameter estimates with the conventional voxelwise IVIM fitting method, DeepIVIM and the proposed self-supervised approach. Difference image between the proposed method and each of the alternative methods is shown in the bottom two rows. The proposed method shows strong agreement with estimates of the conventional fitting method for the estimates of D and f, but a more coherent visual graph of D*.