Positron Emission Tomography (PET) has played a unique role in brain research over the past 25 years. PET imaging has had wide applications in neuropsychiatric research due to 1) the use of specific radiotracers to produce a highly targeted molecular signal, 2) PET scanners that produce quantitative radioactivity images, and 3) tracer kinetic modeling techniques that allow production of images of physiological parameters (flow, metabolism, receptor number) from dynamic (4-D) data and measurements of the arterial input function. However, widespread application of these quantitative techniques has been limited primarily to research studies in a small number of academic centers due to their overall complexity and expense. Thus, the development of robust algorithms for analysis of PET data could lead to a dramatic expansion in the applicability of this technology in clinical and research studies. In addition, PET has been limited, compared to MRI, due to its lower spatial resolution. Recently, the High Resolution Research Tomograph (HRRT), a new scanner designed for human brain studies, has become available. The HRRT provides high sensitivity, list mode acquisition, a large axial field-of-view, and resolution better than 3 mm. Although there is a large potential improvement in the quality of physiological information from the HRRT, there are many scientific and practical challenges accompanying this new technology. In other words, the complexity of quantitative brain PET studies has increased even further with a scanner like the HRRT. To address these challenges and to work towards the ultimate goal of facilitating widespread use of quantitative brain PET methods, the following aims are proposed:
Aim 1 : Extend and validate our cluster-based listmode reconstruction algorithm to improve resolution and quantitative accuracy and to reduce noise.
Aim 2 : Correct for head motion during scanning without loss of resolution by incorporation of direct motion measurements into the image reconstruction process.
Aim 3 : Develop and validate methods to extract the arterial input function using image-based measurements of the carotid arteries and suitable brain reference regions.
Aim 4 : Extend the reconstruction algorithm to incorporate spatial information from MR-based anatomical images and temporal information from tracer kinetic models.
Aim 5 : Demonstrate the practical effect of these reconstruction, physics modeling, and kinetic modeling innovations on human PET data.
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