Keywords: Perfusion, DSC & DCE Perfusion, Head & Neck Imaging
Motivation: To use fluid-mechanics based deep learning method to predict perfusion parameters from dynamic images
Goal(s): We propose to explore the possibility to use neural network trained on simulated data from fluid mechanics simulation to analyze dynamic medical images.
Approach: We use quantitative transport mapping network (QTMnet), which is trained on simulated concentration propagation profile generated from constrained constructive optimization (CCO) and transport equation-based tracer propagation simulation, to predict perfusion parameters including flow rate, permeability, vasculature volume, from DCE MRI images.
Results: QTMnet predict perfusion parameters accurately in simulation study and can distinguish different gene expression group patients comparing with using traditional kinetics model.
Impact: Proposed QTMnet method can be used to predict different perfusion parameters related to dynamic images accurately and automatically without usage of arterial input function.
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