Andrii Pozaruk1,2, Kamlesh Pawar1,3, Shenpeng Li1,4, Alexandra Carey1,5, Jeremy Cheng6, Viswanath P Sudarshan1,4,7, Marian Cholewa2, Jeremy Grummet6, Zhaolin Chen8,9, Gary Egan1,3. 1. Monash Biomedical Imaging, Monash University, Building 220, Clayton Campus, 770 Blackburn Rd, Clayton, Victoria, 3168, Australia. 2. Department of Biophysics, Faculty of Mathematics and Natural Sciences, University of Rzeszow, Rzeszow, Poland. 3. Monash Institute of Cognitive and Clinical Neurosciences and School of Psychological Sciences, Monash University, Clayton, Australia. 4. Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Australia. 5. Monash Imaging, Monash Health, Clayton, Australia. 6. Department of Surgery, Central Clinical School, Monash University, Melbourne, Australia. 7. Indian Institute of Technology Bombay, Mumbai, India. 8. Monash Biomedical Imaging, Monash University, Building 220, Clayton Campus, 770 Blackburn Rd, Clayton, Victoria, 3168, Australia. zhaolin.chen@monash.edu. 9. Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Australia. zhaolin.chen@monash.edu.
Abstract
PURPOSE: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challenging problem as it affects the quantitative nature of the modality. In this study, we aimed to improve the accuracy of estimated attenuation maps from MRI Dixon contrast images by training an augmented generative adversarial network (GANs) in a supervised manner. We augmented the GANs by perturbing the non-linear deformation field during image registration between MRI and the ground truth CT images. METHODS: We acquired the CT and the corresponding PET-MR images for a cohort of 28 prostate cancer patients. Data from 18 patients (2160 slices and later augmented to 270,000 slices) was used for training the GANs and others for validation. We calculated the error in bone and soft tissue regions for the AC μ-maps and the reconstructed PET images. RESULTS: For quantitative analysis, we use the average relative absolute errors and validate the proposed technique on 10 patients. The DL-based MR methods generated the pseudo-CT AC μ-maps with an accuracy of 4.5% more than standard MR-based techniques. Particularly, the proposed method demonstrates improved accuracy in the pelvic regions without affecting the uptake values. The lowest error of the AC μ-map in the pelvic region was 1.9% for μ-mapGAN + aug compared with 6.4% for μ-mapdixon, 5.9% for μ-mapdixon + bone, 2.1% for μ-mapU-Net and 2.0% for μ-mapU-Net + aug. For the reconstructed PET images, the lowest error was 2.2% for PETGAN + aug compared with 10.3% for PETdixon, 8.7% for PETdixon + bone, 2.6% for PETU-Net and 2.4% for PETU-Net + aug.. CONCLUSION: The proposed technique to augment the training datasets for training of the GAN results in improved accuracy of the estimated μ-map and consequently the PET quantification compared to the state of the art.
PURPOSE: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challenging problem as it affects the quantitative nature of the modality. In this study, we aimed to improve the accuracy of estimated attenuation maps from MRI Dixon contrast images by training an augmented generative adversarial network (GANs) in a supervised manner. We augmented the GANs by perturbing the non-linear deformation field during image registration between MRI and the ground truth CT images. METHODS: We acquired the CT and the corresponding PET-MR images for a cohort of 28 prostate cancerpatients. Data from 18 patients (2160 slices and later augmented to 270,000 slices) was used for training the GANs and others for validation. We calculated the error in bone and soft tissue regions for the AC μ-maps and the reconstructed PET images. RESULTS: For quantitative analysis, we use the average relative absolute errors and validate the proposed technique on 10 patients. The DL-based MR methods generated the pseudo-CT AC μ-maps with an accuracy of 4.5% more than standard MR-based techniques. Particularly, the proposed method demonstrates improved accuracy in the pelvic regions without affecting the uptake values. The lowest error of the AC μ-map in the pelvic region was 1.9% for μ-mapGAN + aug compared with 6.4% for μ-mapdixon, 5.9% for μ-mapdixon + bone, 2.1% for μ-mapU-Net and 2.0% for μ-mapU-Net + aug. For the reconstructed PET images, the lowest error was 2.2% for PETGAN + aug compared with 10.3% for PETdixon, 8.7% for PETdixon + bone, 2.6% for PETU-Net and 2.4% for PETU-Net + aug.. CONCLUSION: The proposed technique to augment the training datasets for training of the GAN results in improved accuracy of the estimated μ-map and consequently the PET quantification compared to the state of the art.
Authors: Hasan Sari; Mohammadreza Teimoorisichani; Clemens Mingels; Ian Alberts; Vladimir Panin; Deepak Bharkhada; Song Xue; George Prenosil; Kuangyu Shi; Maurizio Conti; Axel Rominger Journal: Eur J Nucl Med Mol Imaging Date: 2022-07-19 Impact factor: 10.057
Authors: Ioannis D Apostolopoulos; Nikolaos D Papathanasiou; Dimitris J Apostolopoulos; George S Panayiotakis Journal: Eur J Nucl Med Mol Imaging Date: 2022-04-22 Impact factor: 10.057
Authors: Hasan Sari; Ja Reaungamornrat; Onofrio A Catalano; Javier Vera-Olmos; David Izquierdo-Garcia; Manuel A Morales; Angel Torrado-Carvajal; Thomas S C Ng; Norberto Malpica; Ali Kamen; Ciprian Catana Journal: J Nucl Med Date: 2021-07-22 Impact factor: 11.082