Attenuation correction of cardiac <sup>82</sup>Rb pet using deep learning generated synthetic CT.
Authors
Affiliations (5)
Affiliations (5)
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen, Denmark.
- Cluster for Molecular Imaging, Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, Copenhagen, Denmark. [email protected].
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Copenhagen, Denmark. [email protected].
Abstract
Ischemic heart disease remains a leading cause of mortality worldwide. Myocardial perfusion imaging (MPI) using Rubidium-82 (<sup>82</sup>Rb) positron emission tomography (PET) is a cornerstone in its evaluation. However, conventional CT-based attenuation correction (AC) is prone to artifacts, with misalignment between PET emission data and the CT-AC being a common problem. This study evaluates the feasibility of introducing a deep learning approach to generate synthetic CT (sCT) images directly from non-attenuation-corrected <sup>82</sup>Rb-PET images. To this end, we developed a cGAN using a conditional generative adversarial network (cGAN) with an Attention U-Net generator to produce sCT images to produce sCT-AC maps, based upon 544 PET/CT MPI scans. Image quality was assessed using structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and mean error (ME). Additionally, attenuation-corrected PET images based on sCT were evaluated in the cardiac region using relative mean error (RME) and relative mean absolute error (RMAE). Cardiac function and perfusion assessments, defined as the ischemic total perfusion deficit (iTPD) and the left ventricular ejection fraction reserve (LVEFR), were compared between sCT-based and conventional CT AC methods. Our sCT-images provided good correlation to the conventional CT-AC (SSIM = 0.91 ± 0.037, PSNR = 29.9 ± 3.2 dB). For the PET images, we report a slight bias in the cardiac region (RME = 4.2 ± 7.8%, RMAE = 6.9 ± 5.9%), likely due to a uniform overestimation of the soft-tissue u-maps within the sCT. Despite the bias, the quantification metrics remained comparable to those obtained with CT AC (mean iTPD: CT 3.73 ± 5.19% vs. sCT 3.67 ± 5.13%; mean LVEFR: CT 5.88 ± 5.96% vs. sCT 5.90 ± 6.11%). Additionally, the sCT-based approach appeared to reduce motion and implant-related artifacts, providing further motivation for its use over CT. This observation was made through visual inspection on a case-by-case basis. These results demonstrate the potential of deep learning-based sCT generation to maintain integrity in PET MPI while helping to mitigate issues related to misalignments and metal-induced artifacts.