We present a method of generating metabolism maps from dynamic hyperpolarized carbon-13 MRI images. By incorporating prior information into our model-based reconstruction via spatial regularization of the parameter maps, we achieve two qualitative benefits: elimination of non-identifiability in unperfused background regions, and denoising. This method is illustrated on a simulated dataset and a clinical prostate cancer dataset.
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