Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models

Qing Lyu; Josh Tan; Megan E. Lipford; Chuang Niu; Micheal E. Zapadka; Christopher M. Lack; Jonathan D. Clemente; Christopher T. Whitlow; Ge Wang

arXiv · 2308.13002

Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models

Qing Lyu·Josh Tan·Megan E. Lipford·Chuang Niu·Micheal E. Zapadka·Christopher M. Lack·Jonathan D. Clemente·Christopher T. Whitlow·Ge Wang

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Abstract

Iodinated contrast media is essential for dual-energy computed tomography (DECT) angiography. Previous studies show that iodinated contrast media may cause side effects, and the interruption of the supply chain in 2022 led to a severe contrast media shortage in the US. Both factors justify the necessity of contrast media reduction in relevant clinical applications. In this study, we propose a diffusion model-based deep learning framework to address this challenge. First, we simulate different levels of low contrast dosage DECT scans from the standard normal contrast dosage DECT scans using material decomposition. Conditional denoising diffusion probabilistic models are then trained to enhance the contrast media and create contrast-enhanced images. Our results demonstrate that the proposed methods can generate high-quality contrast-enhanced results even for images obtained with as low as 12.5% of the normal contrast dosage. Furthermore, our method outperforms selected competing methods in a human reader study.

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Qing Lyu, Josh Tan, Megan E. Lipford, Chuang Niu, Micheal E. Zapadka, Christopher M. Lack, Jonathan D. Clemente, Christopher T. Whitlow, Ge Wang. 2023-08-24. Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models. https://arxiv.org/abs/2308.13002

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