Abstract #0432

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

Dominick Romano^1,2, Qihao Zhang², Mert Şişman^2,3, Renjiu Hu^2,4, Benjamin Weppner^1,2, Thanh Nguyen², Pascal Spincemaille², Martin Prince^2,5, and Yi Wang²

¹Biomedical Engineering, Cornell University, Ithaca, NY, United States, ²Radiology, Weill Cornell Medical College, New York, NY, United States, ³Electrical Engineering and Computer Science, Cornell University, Ithaca, NY, United States, ⁴Mechanical And Aerospace Engineering, Cornell University, Ithaca, NY, United States, ⁵Radiology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States

Synopsis

Keywords: Cancer, Perfusion, Dynamic Contrast Enhanced MRI; Liver; Validation; Deep Learning; Phantoms

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.

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