Projects per year
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.
|Number of pages||12|
|Journal||European Journal of Nuclear Medicine and Molecular Imaging|
|Publication status||Published - Jan 2021|
- Attenuation correction
- Deep learning
- Prostate cancer
Egan, G., Rosa, M., Lowery, A., Stuart, G., Arabzadeh, E., Skafidas, E., Ibbotson, M., Petrou, S., Paxinos, G., Mattingley, J., Garrido, M., Sah, P., Robinson, P. A., Martin, P., Grunert, U., Tanaka, K., Mitra, P., Johnson, G., Diamond, M., Margrie, T., Leopold, D., Movshon, J., Markram, H., Victor, J., Hill, S. & Jirsa, V.
Australian National University (ANU), ETH Zurich, Australian Research Council (ARC), Karolinska Institute, QIMR Berghofer Medical Research Institute, Ecole Polytechnique Federale de Lausanne (EPFL) (Swiss Federal Institute of Technology in Lausanne) , Monash University, University of Melbourne, University of New South Wales (UNSW), University of Queensland , University of Sydney, Monash University – Internal University Contribution, National Institutes of Health (United States), Cornell University, New York University, MRC National Institute for Medical Research, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Duke University, Cold Spring Harbor Laboratory, RIKEN
25/06/14 → 31/12/21