Iodine map generation from single-kV contrast-enhanced CT using a conditional generative model

Ran Zhang; Yijing Wu; John W. Garrett; Ke Li; Meghan G. Lubner; Thomas M. Grist; Guang-Hong Chen

doi:10.1117/12.3008641

3 April 2024 Iodine map generation from single-kV contrast-enhanced CT using a conditional generative model

Ran Zhang, Yijing Wu, John W. Garrett, Ke Li, Meghan G. Lubner, Thomas M. Grist, Guang-Hong Chen

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 1292509 (2024) https://doi.org/10.1117/12.3008641
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

This work introduces a conditional generative model using the denoising diffusion probabilistic model learning strategy to generate iodine maps from single-kV contrast-enhanced CT data. The purpose is to enhance the functionality of single-kV CT scanners, expanding their clinical applications. The model was trained using a clinical dataset of 17,284 paired images from 151 subjects. The testing dataset had 903 image slices from 12 subjects independent of the training set. The model's performance was assessed using quantitative metrics such as RMSE and SSIM, with median values of 0.58 mg/ml and 0.979, respectively. Bland-Altman analysis confirmed the consistency between DECT and the proposed method.

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Citation Download Citation

Ran Zhang, Yijing Wu, John W. Garrett, Ke Li, Meghan G. Lubner, Thomas M. Grist, and Guang-Hong Chen "Iodine map generation from single-kV contrast-enhanced CT using a conditional generative model", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 1292509 (3 April 2024); https://doi.org/10.1117/12.3008641

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KEYWORDS

Iodine

Computed tomography

Data modeling

Education and training

Biomedical applications

Diffusion

Performance modeling

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