Validating Quantitative Transport Mapping (QTM) on a Perfused Liver Phantom

Motivation: To validate deep learning based Quantitative Transport Mapping (QTMnet) on a perfused tissue phantom. Goal(s): Evaluate the accuracy of QTMnet derived flow and compare to traditional tracer-kinetic flow estimation. Approach: We developed a workflow to prepare porcine liver as a perfusion phantom1. We perfused n=8 porcine livers with a controllable pump and acquired DCE-MRI. We then estimated the liver flow with QTMnet and traditional tracer-kinetics. Results: QTMnet accurately estimates our phantom flow (mean error: -2.82%, mean absolute error: 10.0%). Furthermore, QTMnet flow estimation was more accurate than traditional tracer-kinetics flow estimation (mean error: -43.29%, mean absolute error: 58.9%, P<0.00001). Impact: Our liver phantom workflow allows demonstrating accuracy of estimated flows. Superior accuracy was observed using QTMnet compared to traditional tracer-kinetics. Accurate estimation of liver blood flow allows better diagnosis and follow-up in the imaging of primary and secondary liver cancer.