Ananya Singh1, Jacek Kwiecinski1,2, Sebastien Cadet1, Aditya Killekar1, Evangelos Tzolos3, Michelle C Williams3, Marc R Dweck3, David E Newby3, Damini Dey1, Piotr J Slomka4. 1. Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA. 2. Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland. 3. BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK. 4. Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA. Piotr.Slomka@cshs.org.
Abstract
BACKGROUND: 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 by generative adversarial networks (GAN). METHODS: A total of 169 patients, 139 in the training and 30 in the testing sets were considered for generation of pseudo-CT from non-attenuation corrected (NAC) PET using GAN. Non-rigid registration was used to register pseudo-CT to CT angiography and the resulting transformation was used to align PET with CT angiography. We compared translations, maximal standard uptake value (SUVmax) and target to background ratio (TBRmax) at the location of plaques, obtained after observer and automated alignment. RESULTS: Automatic end-to-end registration was performed for 30 patients with 88 coronary vessels and took 27.5 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, 0.7 ± 3.0, 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). CONCLUSION: Pseudo-CT generated by GAN are perfectly registered to PET can be used to facilitate quick and fully automated registration of PET and CT angiography.
BACKGROUND: 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 by generative adversarial networks (GAN). METHODS: A total of 169 patients, 139 in the training and 30 in the testing sets were considered for generation of pseudo-CT from non-attenuation corrected (NAC) PET using GAN. Non-rigid registration was used to register pseudo-CT to CT angiography and the resulting transformation was used to align PET with CT angiography. We compared translations, maximal standard uptake value (SUVmax) and target to background ratio (TBRmax) at the location of plaques, obtained after observer and automated alignment. RESULTS: Automatic end-to-end registration was performed for 30 patients with 88 coronary vessels and took 27.5 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, 0.7 ± 3.0, 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). CONCLUSION: Pseudo-CT generated by GAN are perfectly registered to PET can be used to facilitate quick and fully automated registration of PET and CT angiography.
Authors: Jacek Kwiecinski; Philip D Adamson; Martin L Lassen; Mhairi K Doris; Alastair J Moss; Sebastian Cadet; Maurits A Jansen; Damini Dey; Sang-Eun Lee; Mijin Yun; Hyuk-Jae Chang; Marc R Dweck; David E Newby; Daniel S Berman; Piotr J Slomka Journal: Circ Cardiovasc Imaging Date: 2018-12 Impact factor: 7.792