Spectral computed tomography (CT) imaging employs two or more different energies to obtain energy-differential attenuation information of tissue properties. 
It allows quantitative characterization of tissue composition by material basis decomposition, which cannot be easily achieved by a PET scan. Thus, PET and spectral CT may complement each other to enable a new multiparametric imaging solution for more accurate disease diagnosis and characterization. However, integration of spectral CT with PET would require either a costly scanner hardware upgrade or significant modifications of imaging protocols to allow two X-ray CT scans, which is associated with increased radiation dose and scan cost.
Different from standard spectral CT methods which are based on x-rays, we propose a spectral CT imaging methodology based on time-of-flight PET/CT by combining x-ray and γ-ray data (Fig. 1; Wang, PMB 2020). This new method does not require a change of PET/CT scanner hardware or additional radiation dose except a standard time-of-flight PET/CT scan that is already available on most modern PET/CT scanners. We develop enabling algorithms using the kernel method with or without deep neural networks (Li et al, IEEE-TIP 2024; Li & Wang, PTRSA 2021; Wang, PMB 2020) to reconstruct high-energy “γ-ray CT” attenuation images from the PET/CT scans, which then are combined with the x-ray CT image (low-energy: ≤140 keV) to produce a pair of dual-energy CT images for spectral imaging. The feasibility of this PET-enabled dual-energy CT method for multi-material decomposition has been demonstrated using both real physical phantom scans and human patient scans (Zhu et al, 2024).
This project is supported in part by NIH R21EB027346.
Journal Papers and Conference Presentations:
- Li S, Zhu Y, Spencer BA, Wang GB.
Single-subject deep-learning image reconstruction with a neural optimization transfer algorithm for PET-enabled dual-energy CT imaging.
IEEE Transactions on Image Processing, accepted, June 2024.
[Preprint: arXiv:2310.03287. https://doi.org/10.48550/arXiv.2310.03287] - Zhu Y, Li S, Xie Z, Leung EK, Bayerlein R, Omidvari N, Cherry SR, Qi J, Badawi RD, Spencer BA, Wang GB.
Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and patient results.
arXiv:2402.02091. 3 Feb 2024. https://doi.org/10.48550/arXiv.2402.02091 - Zhu Y, Spencer BA, Xie Z, Leung EK, Bayerlein R, Omidvari N, Cherry SR, Qi J, Badawi RD, Wang GB.
Super-resolution reconstruction of γ-ray CT images for PET-enabled dual-energy CT imaging.
2023 SPIE Medical Imaging, San Diego, USA, February 19-23, 2023. (oral presentation) - Zhu Y, Li S, Xie Z, Leung EK, Bayerlein R, Omidvari N, Cherry SR, Qi J, Badawi RD, Spencer BA, Wang GB.
PET-enabled dual-energy CT: open-source implementation and real data validation.
2022 IEEE Nuclear Sciences Symposium and Medical Imaging Conference (NSS&MIC), Milan, Italy. Nov 9-12, 2022. (oral presentation) - Li SQ, Wang GB.
Neural MLAA for PET-enabled Dual-Energy CT Imaging.
Proc. SPIE Medical Imaging 2021: Physics of Medical Imaging, 115951G (15 February 2021). (oral presentation)
DOI: https://doi.org/10.1117/12.2582317 - Li SQ, Wang GB.
Modified Kernel MLAA Using Autoencoder for PET-enabled Dual-Energy CT.
Philosophical Transactions of the Royal Society A, 379(2204): 20200204, 2021.
(theme issue on Synergistic Tomographic Image Reconstruction, Part 2)
[Open Access PDF] [Preprint: arXiv:2010.07484. October 2020] - Li SQ, Wang GB.
Kernel MLAA Using Autoencoder for PET-enabled Dual-Energy CT.
16th Virtual International Meeting on Fully 3D Image Reconstruction, Leuven Belgium, July 2021. (oral presentation) - Wang GB.
PET-enabled Dual-Energy CT: Image Reconstruction and A Proof-of-Concept Computer Simulation Study.
Physics in Medicine and Biology, 65(24): 245028, 2020
[Preprint: arXiv:2008.09755. August 2020] - Wang GB.
PET-enabled Dual-energy CT: Exploring a New Way of Spectral Imaging Using Synergistic Reconstruction.
A talk at the Synergistic Reconstruction Symposium, Manchester, United Kingdom, November 3-4, 2019. - Wang GB.
PET-enabled dual-energy CT: A proof-of-concept simulation study.
2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS&MIC), Sydney, Australia, November 13-17, 2018. (oral presentation)
(DOI:10.1109/NSSMIC.2018.8824351)