Direct parametric reconstruction from (k, t)-space data in dynamic contrast enhanced MRI

Direct parametric reconstruction (DPR), offers a new perspective in MR, setting the model parameters as the aim of reconstruction by estimating them directly from k-space using a Bayesian inference algorithm. DPR was implemented to derive model parameters (i.e. plasma volume vp, extracellular extravascular volume (EES) ve, transfer rate between plasma and EES (min-1) Ktrans) from dynamic contrast enhanced (DCE) (k,t)-space data. Its performance was evaluated against the current indirect approach where (k,t)-space DCE data are reconstructed (either with a Fourier Transform or with kt-FOCUSS when undersampling was present) to images and then fitted using a pharmacokinetic (PK) model2. The purpose of this work is to address some of the limitations of the DPR algorithm, namely the suggested modifications are to jointly reconstruct proton density, and native T1 map, T10 from multi-flip angle and DCE data along with the PK parameters. Further, DPR was implemented for different PK models so as the enhancement at each pixel (tissue) is described by the appropriate PK model.

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