Parisa Khateri1, Hamidreza Saligheh Rad1,2, Amir Homayoun Jafari2,3,4, Anahita Fathi Kazerooni1,2, Afshin Akbarzadeh1, Mohsen Shojae Moghadam3,4, Arvin Aryan5, Pardis Ghafarian6,7, Mohammad Reza Ay8,9. 1. Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran. 2. Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran. 3. Research Center for Biomedical and Robotics Technology, Tehran University of Medical Sciences, Tehran, Iran. 4. MRI Imaging Center, Payambaran Hospital, Tehran, Iran. 5. Imaging Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran. 6. Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran. 7. PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 8. Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran. mohammadreza_ay@tums.ac.ir. 9. Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran. mohammadreza_ay@tums.ac.ir.
Abstract
PURPOSE: The aim of this study is to generate a four-class magnetic resonance imaging (MRI)-based attenuation map (μ-map) for attenuation correction of positron emission tomography (PET) data of the head area using a novel combination of short echo time (STE)/Dixon-MRI and a dedicated image segmentation method. PROCEDURES: MR images of the head area were acquired using STE and two-point Dixon sequences. μ-maps were derived from MRI images based on a fuzzy C-means (FCM) clustering method along with morphologic operations. Quantitative assessment was performed to evaluate generated MRI-based μ-maps compared to X-ray computed tomography (CT)-based μ-maps. RESULTS: The voxel-by-voxel comparison of MR-based and CT-based segmentation results yielded an average of more than 95 % for accuracy and specificity in the cortical bone, soft tissue, and air region. MRI-based μ-maps show a high correlation with those derived from CT scans (R (2) > 0.95). CONCLUSIONS: Results indicate that STE/Dixon-MRI data in combination with FCM-based segmentation yields precise MR-based μ-maps for PET attenuation correction in hybrid PET/MRI systems.
PURPOSE: The aim of this study is to generate a four-class magnetic resonance imaging (MRI)-based attenuation map (μ-map) for attenuation correction of positron emission tomography (PET) data of the head area using a novel combination of short echo time (STE)/Dixon-MRI and a dedicated image segmentation method. PROCEDURES: MR images of the head area were acquired using STE and two-point Dixon sequences. μ-maps were derived from MRI images based on a fuzzy C-means (FCM) clustering method along with morphologic operations. Quantitative assessment was performed to evaluate generated MRI-based μ-maps compared to X-ray computed tomography (CT)-based μ-maps. RESULTS: The voxel-by-voxel comparison of MR-based and CT-based segmentation results yielded an average of more than 95 % for accuracy and specificity in the cortical bone, soft tissue, and air region. MRI-based μ-maps show a high correlation with those derived from CT scans (R (2) > 0.95). CONCLUSIONS: Results indicate that STE/Dixon-MRI data in combination with FCM-based segmentation yields precise MR-based μ-maps for PET attenuation correction in hybrid PET/MRI systems.
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