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arXiv · 2006.06270

Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction

Alexander Denker·Maximilian Schmidt·Johannes Leuschner·Peter Maass·Jens Behrmann
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Abstract

Image reconstruction from computed tomography (CT) measurement is a challenging statistical inverse problem since a high-dimensional conditional distribution needs to be estimated. Based on training data obtained from high-quality reconstructions, we aim to learn a conditional density of images from noisy low-dose CT measurements. To tackle this problem, we propose a hybrid conditional normalizing flow, which integrates the physical model by using the filtered back-projection as conditioner. We evaluate our approach on a low-dose CT benchmark and demonstrate superior performance in terms of structural similarity of our flow-based method compared to other deep learning based approaches.

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Alexander Denker, Maximilian Schmidt, Johannes Leuschner, Peter Maass, Jens Behrmann. 2020-06-11. Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction. https://arxiv.org/abs/2006.06270

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