ATS-DDS: a general alternating training strategy for dual-domain sparse-view CT reconstruction

Zihan Deng; Zhisheng Wang; Legeng Lin; Shunli Wang; Junning Cui

doi:10.1117/12.3032411

13 September 2024 ATS-DDS: a general alternating training strategy for dual-domain sparse-view CT reconstruction

Zihan Deng, Zhisheng Wang, Legeng Lin, Shunli Wang, Junning Cui

Proceedings Volume 13178, Eleventh International Symposium on Precision Mechanical Measurements; 131780C (2024) https://doi.org/10.1117/12.3032411
Event: Eleventh International Symposium on Precision Mechanical Measurements, 2023, Guangzhou, China

Abstract

Sparse scanning methods are widely used to reduce the harmful effects of radiation doses on the human body. Recently, deep learning methods have been widely used for challenging reconstruction tasks such as sparse angles, while dual domain methods have natural advantages because they can handle information in both the sinusoidal and image domains. However, the existing methods in both fields do not pay enough attention to the allocation and strategy of training costs in both fields, but instead use the same training resources for unified training. To address this issue, this article designs a universal training strategy that can adapt to various network model structures: a general alternating training strategy for dual-domain sparse-view CT reconstruction (ATS-DDS) and conducts simulation experiments on various classic networks and the latest deep learning models. The results indicate that alternating training in dual-domain training scenarios can reduce the overall resources of network training and accelerate network training. At the same time, appropriately increasing information exchange between the two fields is beneficial for accelerating network training.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zihan Deng, Zhisheng Wang, Legeng Lin, Shunli Wang, and Junning Cui "ATS-DDS: a general alternating training strategy for dual-domain sparse-view CT reconstruction", Proc. SPIE 13178, Eleventh International Symposium on Precision Mechanical Measurements, 131780C (13 September 2024); https://doi.org/10.1117/12.3032411

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