We present a new strategy for incorporating high resolution structural information from MRI into the reconstruction of PET imagery via deep domain translated image priors. The strategy involves two steps: (1) predicting a PET uptake volume directly from MRI without requiring a radiation dose, and (2) using the predicted dose-free PET volume to impose sparsity constraints on the PET reconstruction from measured sinograms. The key idea of our approach is that domain translated PET imagery can capture the true spatial and sparsity patterns of PET imagery, which can be used to guide the convergence of the statistics-limited inverse problem. This scheme can be superior to joint-sparsity reconstruction, among other methods, since the mismatch between PET and MRI features is significantly reduced by using the domain translated zero-dose PET as the prior instead. We evaluate this technique on a wholebody 18F-FDG-PET dataset, demonstrating that dichromatic interpolation can recover high quality PET imagery from noisy and low dose PET/MRI, with no observed failure cases.
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