An automated end-to-end deep learning reconstruction and quantification workflow for fast quantitative DCE-MRI

Motivation: Despite extensive research and promising initial results, quantitative dynamic contrast-enhanced (DCE) MRI is marginal in clinical practice, due to lack of automation and low reproducibility. Goal(s): Introduce an end-to-end deep learning approach for an automated and more reproducible DCE-MRI pipeline. Approach: Two networks, one reconstructing undersampled k-t data via Movienet and the other estimating perfusion and MR parameters, were merged into a unified, automated pipeline. The approach was tested on a volunteer and a patient with cervical cancer. Results: Automated processing yielded images in under 2 seconds, comparable in quality to GRASP and providing multiparametric mapping of perfusion and MR from one acquisition. Impact: The proposed fast automated data processing pipeline including deep learning reconstruction and quantification can be an important clinical tool to exploit the information from DCE-MRI to improve tumor diagnosis and treatment response evaluation.