ArticleMagnetic resonance in medicine2025
Validation of quantitative transport mapping (QTM) with an ex vivo perfused liver model.
Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Female vulnerability to microvascular transport dysfunction emerges early across the Alzheimer's disease continuum.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Comparison of bi- and tri-component approaches for the analysis of short-TMagma (New York, N.Y.) · 2026Article
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13 authors.
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Abstract
purposeTo evaluate the accuracy of a deep learning-based quantitative transport mapping method (QTMnet) for measuring total tissue perfusion.
methodsQTMnet 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.
resultsThe 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).
conclusionThe fluid mechanics-based QTMnet accurately estimates total tissue flow in liver explants.
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