Feasibility of data-driven scatter correction method in a triple-source CT

Linjie Chen; Ying Cheng; Shuo Yang; Guohua Cao

doi:10.1117/12.3006084

1 April 2024 Feasibility of data-driven scatter correction method in a triple-source CT

Linjie Chen, Ying Cheng, Shuo Yang, Guohua Cao

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 1292522 (2024) https://doi.org/10.1117/12.3006084
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Cardiac computed tomography (CT) imaging faces challenges from cardiac and respiratory motion, which can result in motion artifacts. We have previously developed an innovative Triple-Source CT (TSCT) architecture which enables parallel acquisition of three projections to improve temporal resolution. However, forward and cross-scattering induced by multi-source exposures can severely degrade image quality. In this work, we evaluate various scatter correction approaches including a data-driven deep learning approach to mitigate scatter in a physical TSCT system. Phantom studies were performed under various configurations to investigate scatter effects and evaluate image quality pre- and post-correction. Evaluation metrics including HU profiles, HU uniformity and contrast-to-noise ratio (CNR) were analyzed. Among all evaluated scatter mitigation methods, the collimator-based hardware method achieved the best performance. Among all evaluated software-based methods, our deep-learning method performed slightly better than other deep learning methods.

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

Citation Download Citation

Linjie Chen, Ying Cheng, Shuo Yang, and Guohua Cao "Feasibility of data-driven scatter correction method in a triple-source CT", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 1292522 (1 April 2024); https://doi.org/10.1117/12.3006084

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