Validation of quantitative transport mapping (QTM) with an ex vivo perfused liver model

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PURPOSE: To evaluate the accuracy of a deep learning-based quantitative transport mapping method (QTMnet) for measuring total tissue perfusion. METHODS: QTMnet obtains tissue perfusion parametric maps from dynamic contrast-enhanced MRI images by training on simulated data. This data uses synthetic arterial and venous vasculature geometries with flow based on constrained constructive optimization. Gadolinium contrast agent distribution is governed by the transport-forward problem, allowing us to generate a synthetic concentration spacetime profile for a given flow, blood volume fraction, and boundary condition. Tissue flows determined by QTMnet were compared to those obtained with traditional perfusion quantification (Kety equation with Tofts generalization), which uses a global arterial input function. Their total flow accuracies were validated on explanted porcine livers that were connected to an MR compatible flow pump with specified total flow rate for dynamic contrast-enhanced MRI experiments. RESULTS: The mean total flow error for QTMnet was -0.34% ± 16.21% with range [-24.79%, 23.96%], compared to -35.74% ± 36.30% [-77.28%, 29.87%] for the Kety method. QTMnet provides 72% lower mean absolute error than the Kety method (12.15% vs. 43.21%, a 3.6-fold reduction). CONCLUSION: The fluid mechanics-based QTMnet accurately estimates total tissue flow in liver explants.

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