Image-domain material decomposition for dual-energy CT using a conditional diffusion model

Junbo Peng; Chih-Wei Chang; Mingdong Fan; Huiqiao Xie; Justin Roper; Richard L. J. Qiu; Tonghe Wang; Xiangyang Tang; Xiaofeng Yang

doi:10.1117/12.3006941

2 April 2024 Image-domain material decomposition for dual-energy CT using a conditional diffusion model

Junbo Peng, Chih-Wei Chang, Mingdong Fan, Huiqiao Xie, Justin Roper, Richard L. J. Qiu, Tonghe Wang, Xiangyang Tang, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 1293022 (2024) https://doi.org/10.1117/12.3006941
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Dual-Energy CT (DECT) serves an important role in quantitative imaging applications due to its capability for material differentiation. Nevertheless, material decomposition is highly sensitive to noise due to the large condition number of the linear system. To address this, iterative decomposition methods employ regularization terms to enforce noise suppression on the decomposed images. However, these conventional techniques rely on handcrafted image priors and have limited capabilities to characterize the material image distribution. In recent years, deep learning-based methods have been proposed for better distribution learning performance and high computation efficiency. Diffusion models are emerging generative approaches that show great performance in medical image synthesis and translation. In this work, we propose an image-domain material decomposition method for DECT using the conditional Denoising Diffusion Probabilistic Model (DDPM). The preliminary results show its superiority and potential in quantitative imaging tasks of DECT.

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

Citation Download Citation

Junbo Peng, Chih-Wei Chang, Mingdong Fan, Huiqiao Xie, Justin Roper, Richard L. J. Qiu, Tonghe Wang, Xiangyang Tang, and Xiaofeng Yang "Image-domain material decomposition for dual-energy CT using a conditional diffusion model", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 1293022 (2 April 2024); https://doi.org/10.1117/12.3006941

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