Generation of mr-based attenuation correction map of pet images in the brain employing joint segmentation of skull and soft-tissue from single short-TE MR imaging modality

Abstract

Recently introduced PET/MRI scanners present significant advantages in comparison with PET/CT, including better soft-tissue contrast, lower radiation dose, and truly simultaneous imaging capabilities. However, the lack of an accurate method for generation ofMR-based attenuation map (μ-map) at 511 keV is hampering further development and wider acceptance of this technology. Here, we present a new method for the MR-based attenuation correction map (μ-map), employing a proposed short echo-time (STE) MR imaging technique along with the nearly automatic segmentation. This method repeatedly applies active contours inhomogeneity correction, multi-class spatial fuzzy clustering (SFCM), followed by shape analysis, to classify the images into cortical bone, air, and soft tissue classes. The proposed segmentation method returned sensitivity of 81% for cortical bone and above 90% for soft tissue and air. These results suggest that this technique is accurate, efficient, and robust for discriminating bony structures from the neighboring air and soft tissue in STE-MR images, which is suitable for generating MR-based μ-maps.