Comparison of iterative parametric and indirect deep learning based reconstruction methods in highly

Our work focuses on estimating the permeability parameters from highly undersampled Dynamic Contrast-Enhanced (DCE) MR images. In this investigation, we systematically compare the performance of iterative direct and indirect parametric reconstruction methods with indirect deep learning-based reconstruction methods in estimating tracer-kinetic parameters from highly undersampled DCE-MR Imaging breast data. Our investigations conclude that the deep learning-based indirect techniques perform at par with direct estimation techniques for lower undersampling rates in the breast DCE-MR imaging. However, at higher undersampling rates, they are not able to provide much needed generalization. Direct estimation techniques are able to provide more accurate results than both deep learning and parametric-based indirect methods in these high undersampling scenarios.

This article is accepted for publication in Medical Physics journal

Aditya Rastogi and Phaneendra K. Yalavarthy, “Comparison of iterative parametric and indirect deep learning-based reconstruction methods in highly undersampled DCE‐MR Imaging of the breast,” Medical Physics, (2020), https://doi.org/10.1002/mp.14447

Inverse Model

The forward and inverse modelling of Tracer Kinetic parameter estimation problem is shown below:

Results

We estimated the Ktrans Maps from 20X, 50X and 100X undersampled DCE-MRI data using golden radial angle undersampling pattern. The result for PAT B for all reconstruction methods methods mentioned above is shown below:

The difference images are shown below: