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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

C. Ulas, M.J. Thrippleton, I. Marshall, M.E. Davies, P.A. Armitage, S.D. Makin, J.M. Wardlaw, B. Menze
Estimation of Pharmacokinetic Parameters in Dynamic Contrast Enhanced MRI Via Random Forest Regression
Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM), Paris, France, June 2018 (bib)

We propose a novel alternative approach to estimate pharmacokinetic (PK) parameters of dynamic contrast enhanced (DCE)-MRI. Our approach leverages machine learning field and mainly targets to automatically learn temporal patterns of the voxel-wise concentration-time curves (CTCs) from a large amount of training samples in order to make accurate parameter estimations. We consider the estimation of parameters as a regression problem and specifically use Random Forest (RF) regression. We demonstrate its potential and utility to improve the conventional model-fitting based quantitative analysis of DCE-MRI especially in various noise conditions, and validate our method on clinical brain stroke datasets.
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