Automated nonlinear registration of coronary PET to CT angiography using pseudo-CT generated from PET with generative adversarial networks

Ananya Singh, Jacek Kwiecinski, Sebastien Cadet, Aditya Killekar, Evangelos Tzolos, Michelle C Williams, Marc R Dweck, David E Newby, Damini Dey, Piotr J Slomka*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Coronary 18F-sodium-fluoride (18F-NaF) positron emission tomography (PET) showed promise in imaging coronary artery disease activity. Currently image processing remains subjective due to the need for manual registration of PET and computed tomography (CT) angiography data. We aimed to develop a novel fully automated method to register coronary 18F-NaF PET to CT angiography using pseudo-CT generated from non-attenuation corrected (NAC) PET by generative adversarial networks (GAN). Non-rigid registration was used to register pseudo-CT to CT angiography and the resulting transformation was subsequently used to align PET with CT angiography.
A total of 169 patients, 139 in the training and 30 in the testing sets were considered. We compared translations at the location of plaques, maximal standard uptake value (SUVmax) and target to background ratio (TBRmax), obtained after observer and automated alignment. Automatic end-to-end registration was performed for 30 patients with 88 coronary vessels and took 95 seconds per patient. Difference in displacement motion vectors between GAN-based and observer-based registration in the x, y and z directions was 0.8 ± 3.0 mm, 0.7 ± 3.0 mm, and 1.7 ± 3.9 mm respectively. TBRmax had a coefficient of repeatability (CR) of 0.31, mean bias of 0.03 and narrow limits of agreement (LOA) (95% LOA: -0.29 to 0.33). SUVmax had CR of 0.26, mean bias of 0 and narrow LOA (95% LOA: -0.26 to 0.26).
In conclusion, pseudo-CT generated by GAN from PET, which are perfectly aligned with PET, can be used to facilitate quick and fully automated registration of PET and CT angiography.
Original languageEnglish
JournalJournal of Nuclear Medicine
Early online date14 Jun 2022
DOIs
Publication statusE-pub ahead of print - 14 Jun 2022

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