PET-enabled Spectral CT

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 add 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 convolutional neural networks (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.

This project is supported in part by NIH R21EB027346.

Journal Papers and Conference Presentations:

  1. 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.
  2. 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)
  3. 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)
  4. 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)
  5. 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]
  6. 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)
  7. 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]
  8. 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.
  9. 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)