M Shirin Shandiz1,2, H Saligheh Rad1,2, P Ghafarian3,4, M Bakhshayesh Karam3,4, Afshin Akbarzadeh2, Mohammad Reza Ay5,6. 1. Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran. 2. Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran. 3. Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran. 4. PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 5. Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran. mohammadreza_ay@tums.ac.ir. 6. Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran. mohammadreza_ay@tums.ac.ir.
Abstract
PURPOSE: Prostate imaging is one of the major application of hybrid PET/MRI systems. Inaccurate attenuation maps (µ-maps) derived by direct segmentation (SEG) in which the cortical bone is ignored and the volume of the air in cavities is underestimated is the main challenge of commercial PET/MRI systems for the quantitative analysis of the pelvic region. The present study considered the cortical bone and air cavity along with soft tissue, fat, and background air in the µ-map of the pelvic region using a method based on SEG. The proposed method uses a dedicated imaging technique that increases the contrast between regions and a hybrid segmentation method to classify MR images based on intensity and morphologic characteristics of tissues, such as symmetry and similarity of bony structures. PROCEDURES: Ten healthy volunteers underwent MRI and ultra-low dose CT imaging. The dedicated MR imaging technique uses the short echo time (STE) based on the conventional sequencing implemented on a clinical 1.5T MRI scanner. The generation of a µ-map comprises the following steps: (1) bias field correction; (2) hybrid segmentation (HSEG), including segmenting images into clusters of cortical bone-air, soft tissue, and fat using spatial fuzzy c-means (SFCM), and separation of cortical bone and internal air cavities using morphologic characteristics; (3) the active contour approach for the separation of background air; and (4) the generation of a five-class μ-map for cortical bone, internal air cavity, soft tissue, fat tissue, and background air. Validation was done by comparison with segmented CT images. RESULTS: The Dice and sensitivity metrics of cortical bone structures and internal air cavities were 72 ± 11 and 66 ± 13 and 73 ± 10 and 68 ± 20 %, respectively. High correlation was observed between CT and HSEG-based µ-maps (R 2 > 0.99) and the corresponding sinograms (R 2 > 0.98). CONCLUSIONS: Currently, pelvis µ-maps provided by the current PET/MRI systems and the ultra-short echo time and atlas-based methods tend to be inaccurate. The proposed method acceptably generated a five-class μ-map using only one image.
PURPOSE: Prostate imaging is one of the major application of hybrid PET/MRI systems. Inaccurate attenuation maps (µ-maps) derived by direct segmentation (SEG) in which the cortical bone is ignored and the volume of the air in cavities is underestimated is the main challenge of commercial PET/MRI systems for the quantitative analysis of the pelvic region. The present study considered the cortical bone and air cavity along with soft tissue, fat, and background air in the µ-map of the pelvic region using a method based on SEG. The proposed method uses a dedicated imaging technique that increases the contrast between regions and a hybrid segmentation method to classify MR images based on intensity and morphologic characteristics of tissues, such as symmetry and similarity of bony structures. PROCEDURES: Ten healthy volunteers underwent MRI and ultra-low dose CT imaging. The dedicated MR imaging technique uses the short echo time (STE) based on the conventional sequencing implemented on a clinical 1.5T MRI scanner. The generation of a µ-map comprises the following steps: (1) bias field correction; (2) hybrid segmentation (HSEG), including segmenting images into clusters of cortical bone-air, soft tissue, and fat using spatial fuzzy c-means (SFCM), and separation of cortical bone and internal air cavities using morphologic characteristics; (3) the active contour approach for the separation of background air; and (4) the generation of a five-class μ-map for cortical bone, internal air cavity, soft tissue, fat tissue, and background air. Validation was done by comparison with segmented CT images. RESULTS: The Dice and sensitivity metrics of cortical bone structures and internal air cavities were 72 ± 11 and 66 ± 13 and 73 ± 10 and 68 ± 20 %, respectively. High correlation was observed between CT and HSEG-based µ-maps (R 2 > 0.99) and the corresponding sinograms (R 2 > 0.98). CONCLUSIONS: Currently, pelvis µ-maps provided by the current PET/MRI systems and the ultra-short echo time and atlas-based methods tend to be inaccurate. The proposed method acceptably generated a five-class μ-map using only one image.