Identifying bone tissue is particularly relevant for accurate attenuation correction in neurological PET studies as this tissue class has the highest linear attenuation coefficient and inaccuracies in its estimation can introduce large biases in the adjacently located cortical structures. Since using conventional MRI pulse sequences bone tissue and air-filled cavities are difficult to distinguish, novel sequences have been developed to address this challenge. We were among the first to propose using ultra-short echo time MR sequences (optimized for imaging tissues with very short T2 relaxation times) for generating segmented head attenuation maps. In a first approach, the head was segmented into three compartments (i.e. bone and soft tissue and air cavities) based on the relationship between the two echoes on a voxel-by-voxel basis (Catana 2010).
Subsequently, combining the dual-echo UTE and T1-weighted MR data using probabilistic atlases allowed the generation of substantially improved segmented attenuation maps (Poynton, Chen et al 2014). However, one of the main limitations of early generation UTE-based methods was that only three compartments (i.e. soft tissue, bone, and air cavities) could be identified.