Deep Learning for Material Decomposition in Photon-Counting CT

• URL: http://arxiv.org/abs/2208.03360v1
• Date: Fri, 5 Aug 2022 19:05:16 GMT
• Title: Deep Learning for Material Decomposition in Photon-Counting CT
• Authors: Alma Eguizabal, Ozan \"Oktem, Mats U. Persson
• Abstract summary: We present a novel deep-learning solution for material decomposition in PCCT, based on an unrolled/unfolded iterative network. Our approach outperforms a maximum likelihood estimation, a variational method, as well as a fully-learned network.
• Score: 0.5801044612920815
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Photon-counting CT (PCCT) offers improved diagnostic performance through better spatial and energy resolution, but developing high-quality image reconstruction methods that can deal with these large datasets is challenging. Model-based solutions incorporate models of the physical acquisition in order to reconstruct more accurate images, but are dependent on an accurate forward operator and present difficulties with finding good regularization. Another approach is deep-learning reconstruction, which has shown great promise in CT. However, fully data-driven solutions typically need large amounts of training data and lack interpretability. To combine the benefits of both methods, while minimizing their respective drawbacks, it is desirable to develop reconstruction algorithms that combine both model-based and data-driven approaches. In this work, we present a novel deep-learning solution for material decomposition in PCCT, based on an unrolled/unfolded iterative network. We evaluate two cases: a learned post-processing, which implicitly utilizes model knowledge, and a learned gradient-descent, which has explicit model-based components in the architecture. With our proposed techniques, we solve a challenging PCCT simulation case: three-material decomposition in abdomen imaging with low dose, iodine contrast, and a very small training sample support. In this scenario, our approach outperforms a maximum likelihood estimation, a variational method, as well as a fully-learned network.

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