PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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.
Ran Zhang,Yijing Wu,John W. Garrett,Ke Li,Meghan G. Lubner,Thomas M. Grist, andGuang-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
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Ran Zhang, Yijing Wu, John W. Garrett, Ke Li, Meghan G. Lubner, Thomas M. Grist, 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