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. 2019 Jan 8;9:1147. doi: 10.3389/fneur.2018.01147

Figure 2.

Figure 2

The conventional pipeline of pharmacokinetic parameters for DCE-MRI. Our proposed machine learning (ML) model allows to directly infer the parameters from the acquired DCE image time series. To this end, the intermediate computational steps—i.e., conversion to contrast concentration, extraction of AIF, and fitting to a tracer kinetic (TK) model—can be eliminated when applied on a test data. We note that in our approach a specific TK model can be still used to estimate target parameter values during training.