• Abolfazl Mehranian and Habib Zaidi.
• Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
• J. Nucl. Med. 2015 Jun 1; 56 (6): 877-83.

UnlabelledThe 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/MR imaging. Recently, we improved the performance of the MLAA algorithm using an MR imaging-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.MethodsFive head and neck (18)F-FDG patients were scanned on PET/MR imaging and PET/CT scanners. Dixon fat and water MR images were registered to CT images. MRAC maps were derived by segmenting the MR images 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 coregistered 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 unimodal gaussians parameterized over a patient population.ResultsThe results showed that the MLAA-GMM algorithm outperformed 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 standardized uptake value errors of -5.4% and -3.5% in the lungs, -7.4% and -5.0% in soft tissues/lesions, and -18.4% and -10.2% in bones, respectively.ConclusionThe proposed MLAA algorithm is promising for accurate derivation of attenuation maps on time-of-flight PET/MR systems.© 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

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