Fast list-mode reconstruction of sparse TOF PET data with non-smooth priors - IEEE MIC/NSS 2021
Channel: Georg Schramm - Molecular Imaging
Date: 2021-10-18

My talk on "Fast list-mode reconstruction of sparse TOF PET data with non-smooth priors" at the IEEE MIC/NSS 2021.


In this work, we propose and analyze a list-mode version of the stochastic primal-dual hybrid gradient (SPDHG) algorithm for image reconstruction from TOF PET data using subsets and non-smooth priors. By strongly exploiting sparsity in the TOF PET data, our proposed algorithm substantially reduces both memory requirements and computation time. To study the
behavior of the proposed algorithm in detail, its performance is investigated based on simulated 2D TOF data using a brain-like software phantom.
We find that our listmode-version of SPDHG converges essentially as fast the original version, which will lead to a substantial improvement in the time needed for a reconstruction of real 3D TOF PET data. However, as with the original algorithm, a careful choice of the ratio of the primal and dual step sizes, depending on the magnitude of the image to be reconstructed, is crucial to obtain fast convergence.