Abstract
The joint maximum likelihood reconstruction of activity and attenuation (MLAA) for emission-based attenuation correction has regained attention since the advent of time-of-flight PET/MRI. Recently, we improved the performance of the MLAA algorithm using an MRI-constrained Gaussian mixture model (GMM). In this study, we compare the performance of our proposed algorithm with standard 4-class MR-based attenuation correction (MRAC) implemented on commercial systems. Methods: Five head and neck 18F-FDG patients were scanned on the Philips TF PET/MRI and Siemens mCT PET/CT scanners. Dixon fat and water MR images were registered to CT images. MRAC maps were derived by segmenting the MRI into 4 tissue classes and assigning predefined attenuation coefficients. For MLAA-GMM, MR images were segmented into known tissue classes, including fat, soft tissue, lung, background air and an unknown MR low-intensity class encompassing cortical bones, air cavities and metal artifacts. A co-registered bone probability map was also included in the unknown tissue class. Finally, the GMM prior was constrained over known tissue classes of attenuation maps using uni-modal Gaussians parameterized over a patient population. Results: The results showed that the MLAA-GMM algorithm outperforms the MRAC method by differentiating bones from air gaps and providing more accurate patient-specific attenuation coefficients of soft tissue and lungs. It was found that the MRAC and MLAA-GMM methods resulted in average SUV errors of –5.4 % and –3.5 % in the lungs, –7.4 % and –5.0% in soft tissues/lesions, –18.4% and –10.2% in bones, respectively. Conclusion: The proposed MLAA algorithm is promising for accurate derivation of attenuation maps on TOF PET/MR systems.
- Copyright © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.