Keywords: Machine Learning/Artificial Intelligence, BrainIn this study, dynamic brain PET denoising was done using registered MRI and reconstructed PET images reconstructed by the OSEM method, while maintaining TACs that were quite near to the original noisy data. The fundamental challenge to using the supervised learning approach is the lack of ground truth. The resulting spatiotemporal images improved the image quality according to various CNR, SNR, and CRC parameters while maintaining TACs that were similar to the original raw PET. This method only needs one trained network, one set of matrices for a statistical temporal PCA model, and 4D dynamic PET data as input.
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