University of North Carolina at Chapel Hill Graduate School
Doi
Abstract
Positron emission tomography (PET) is a functional imaging modality that allows clinicians to visualize complex physiological processes such as metabolism, proliferation, perfusion, and receptor binding. Magnetic resonance imaging (MRI) is a versatile imaging modality that provides detailed anatomical images as well as functional information. Hybrid PET/MRI systems have been recently proposed as a means to combine the high-sensitivity functional information provided by PET with the high-resolution anatomical information provided by MRI. Furthermore, PET/MRI systems have the capability to provide complementary functional information acquired from both modalities. These systems have garnered significant clinical interest particularly in neurological imaging due to these capabilities. A major drawback of PET/MRI systems is the lack of an accurate, clinically feasible MRI-based method for performing PET photon attenuation correction. The current vendor-provided methods lack accuracy, and more accurate methods proposed in literature are not clinically feasible due to long computation times. The inaccuracies of the vendor-provided methods result from misidentification of tissues, particularly bone, or the assumption of homogenous attenuation coefficients inside each tissue. Therefore, the goal of this work was to develop an MR-based attenuation correction method that addresses both of these challenges in a clinically feasible framework. To achieve this goal, we propose an ultrashort echo-time method that acquires all necessary data using one sequence and produces the necessary attenuation maps quickly. The proposed sequence utilizes a dual flip-angle, dual echo-time ultrashort echo time (UTE) acquisition to segment all tissues of interest to attenuation correction in the head and neck. Next, continuous-valued attenuation coefficients are assigned to all imaging voxels through a conversion from MR relaxation rate R1. The capability of the method to generate accurate PET images was assessed by comparison to the gold standard CT-based method in a large number of subjects. The results show that the proposed method is significantly more accurate in the whole brain as well as in several smaller regions of interest when compared to the corresponding vendor-provided method. The proposed method has been fully automated and can be easily incorporated into the PET/MRI clinical work-flow.Doctor of Philosoph
