Jasmine Schirmer

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The efficient use of multicore architectures for sparse matrixvector multiplication (SpMV) is currently an open challenge. One algorithm which makes use of SpMV is the maximum likelihood expectation maximization (MLEM) algorithm. When using MLEM for positron emission tomography (PET) image reconstruction, one requires a particularly large matrix. We present(More)
Introduction A system matrix with high accuracy is essential when applying iterative reconstruction algorithms like MLEM and OSEM in PET imaging. However, using Monte Carlo simulations may not always be feasible, as they take a considerable amount of time. The detector response function (DRF) model has proven to be a good alternative [1]. Further(More)
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