Deep learning CT image restoration using system blur models

Yijie Yuan; Matthew Tivnan; Grace J. Gang; J. Webster Stayman

doi:10.1117/12.2655806

7 April 2023 Deep learning CT image restoration using system blur models

Yijie Yuan, Matthew Tivnan, Grace J. Gang, J. Webster Stayman

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124634J (2023) https://doi.org/10.1117/12.2655806
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Restoration of images contaminated by blur is an important processing tool across modalities including computed tomography where the blur induced by various system factors can be complex with dependencies on acquisition and reconstruction protocol, and even be patient-dependent. In many cases, such a blur can be modeled and predicted with high accuracy providing an important input to a classical deconvolution approach. While traditional deblurring methods tend to be highly noise magnifying, deep learning approaches have the potential to improve upon classic performance limits. However, most network architectures base their restoration on data inputs alone without knowledge of the system blur. In this work, we explore a deep learning approach that takes both image inputs as well as information that characterizes the system blur to combine modeling and deep learning approaches. We apply the approach to CT image restoration and compare with an image-only deep learning approach. We find that inclusion of the system blur model improves deblurring performance - suggesting the potential power of the combined modeling and deep learning technique.

Citation Download Citation

Yijie Yuan, Matthew Tivnan, Grace J. Gang, and J. Webster Stayman "Deep learning CT image restoration using system blur models", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124634J (7 April 2023); https://doi.org/10.1117/12.2655806

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