Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction

2023/04/17 by Andreas Kofler, Kofler, Andreas, Fabian Altekrüger +15
Medicine · Engineering · #Medical Imaging Techniques and Applications #Advanced X-ray and CT Imaging #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.2304.08350

Abstract

We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based on two distinct sub-networks, with the first sub-network estimating the regularization parameter-map from the input data while the second one unrolling T iterations of the Primal-Dual Three-Operator Splitting (PD3O) algorithm. The latter approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean-corrupted data but crucially without the need of having access to labels for the optimal regularization parameter-maps.

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