Single STE-MR Acquisition in MR-Based Attenuation Correction of Brain PET Imaging Employing a Fully Automated and Reproducible Level-Set Segmentation Approach
| Autor: | Anahita Fathi Kazerooni, Saman Arfaie, Mohammad Reza Ay, Parisa Khateri, Hamidreza Saligheh Rad |
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| Rok vydání: | 2016 |
| Předmět: |
Cancer Research
Time Factors Computer science For Attenuation Correction Multimodal Imaging 030218 nuclear medicine & medical imaging Automation 03 medical and health sciences 0302 clinical medicine Histogram Image Processing Computer-Assisted medicine Humans Radiology Nuclear Medicine and imaging Segmentation Cluster analysis medicine.diagnostic_test business.industry Reproducibility of Results Magnetic resonance imaging Magnetic Resonance Imaging medicine.anatomical_structure Oncology Positron emission tomography Positron-Emission Tomography 030220 oncology & carcinogenesis Cortical bone Tomography X-Ray Computed Nuclear medicine business Correction for attenuation Biomedical engineering |
| Zdroj: | Molecular Imaging and Biology. 19:143-152 |
| ISSN: | 1860-2002 1536-1632 |
| Popis: | The aim of this study is to introduce a fully automatic and reproducible short echo-time (STE) magnetic resonance imaging (MRI) segmentation approach for MR-based attenuation correction of positron emission tomography (PET) data in head region. Single STE-MR imaging was followed by generating attenuation correction maps (μ-maps) through exploiting an automated clustering-based level-set segmentation approach to classify head images into three regions of cortical bone, air, and soft tissue. Quantitative assessment was performed by comparing the STE-derived region classes with the corresponding regions extracted from X-ray computed tomography (CT) images. The proposed segmentation method returned accuracy and specificity values of over 90 % for cortical bone, air, and soft tissue regions. The MR- and CT-derived μ-maps were compared by quantitative histogram analysis. The results suggest that the proposed automated segmentation approach can reliably discriminate bony structures from the proximal air and soft tissue in single STE-MR images, which is suitable for generating MR-based μ-maps for attenuation correction of PET data. |
| Databáze: | OpenAIRE |
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