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Sparse domain approaches in dynamic SPECT imaging with high-performance computing.


Iterative reconstruction algorithms often have relatively large computation time affecting their clinical deployment. This is especially true for 4D reconstruction in dynamic imaging (DI). In this work, we have shown how sparse domain approaches and parallelization for static 3D image reconstruction and 4D dynamic image reconstruction (directly from sinogram) in Single Photon Emission Computed Tomography (SPECT), without any intermediate 3D reconstructions, can improve computational efficiency. DI in SPECT is one of the hardest inverse problems in medical image reconstruction area and slow reconstruction is a challenge for this promising protocol. Our work hopefully, paves a new direction toward making DI in SPECT clinically viable. Our 4D reconstruction also is a novel application of non-negative matrix factorization (NNMF) in an inverse problem.

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