MR-Based Attenuation Correction for PET/MR Hybrid Imaging
The recent and successful integration of positron emission tomography (PET) and magnetic resonance imaging (MRI) modalities in one device has gained wide attention. This new hybrid imaging modality now makes it possible to image the functional metabolism from PET in conjunction with MRI with its excellent soft tissue contrast. Besides providing specific anatomical detail, MRI also eliminates any ionizing radiation from eg. computed tomography (CT) examinations that is otherwise performed in standard PET/CT hybrid imaging systems. However, an unsolved problem is the question of how to correct for the PET attenuation in an PET/MR system. In this respect, the knowledge of the spatial distribution of linear attenuation coefficients (LAC) of the patient at the PET energy level of 511 keV is required. In standalone PET systems, transmission scans using radioactive sources were used for PET attenuation correction (AC) and if needed were scaled to the PET photon energy level. While in PET/CT systems, the CT information was scaled to PET energies for the same purpose. However, in PET/MR hybrid imaging systems, this approach is not feasible as MR and CT measure aspects of proton and electron densities respectively. Therefore alternate approaches to extract attenuation information have to be pursued. One such approach is to use MR information to estimate the distribution of attenuation coefficients within the imaging subject. This is done by using a simple limited class segmentation procedure to delineate air, soft tissue, fat and lung classes and subsequent assignment of their respective attenuation coefficients at PET energy of 511 keV. This way of generating attenuation maps (μ-maps) is however far from ideal as the most attenuating medium such as cortical bone is ignored. They are instead replaced by the attenuation coefficient of a soft tissue. While this approximation has been widely accepted for PET quantification in whole-body research, it has severe underestimation effects for brain studies. In this thesis, we propose an improved MR-based μ-map generation approach. We demonstrate that dedicated MR sequences such as ultrashort echo time sequences (UTE) are useful for the purpose of attenuation correction. From a multitude of MR images, we generate μ-maps that include cortical bone and contain continuous Hounsfield units (HU) akin to a patient CT. These are then compared against segmentation based approaches. The efficacy of continuous valued μ-maps towards PET quantification is analyzed against different μ-maps such as patient CT, segmented patient CT with bone and segmented patient CT without bone. Results indicate that the proposed MR-based μ-maps provide a less than 5% error in PET quantification than any segmentation based μ-maps for brain studies.