Utilization of Lutetium Background Radiation for Quantitative Total-Body PET Imaging

Project Summary/Abstract Positron emission tomography (PET) is widely used as a clinical and research tool for diagnosis, prognosis, and treatment planning in oncology, while increasingly being adopted in other fields such as cardiology, neurology, and inflammatory disorders. The latest generation of PET scanners offer significant enhancements in sensitivity

with an increased axial imaging extent. The sensitivity gain in these scanners directly translates into improved image quality in standard imaging protocols and enables use of new scan protocols with ultrashort time frames or ultralow-dose scans. The long axial-field-of-view (AFOV) in these systems offers unique opportunities and

challenges in addressing quantification barriers in PET. Among a wide range of factors affecting the quantitative accuracy of PET, the effects from attenuation, scatter, and human motion are common and predominant. In this project, we aim to utilize the lutetium (Lu) background radiation present in these scanners: first, for attenuation

correction (AC) and scatter correction (SC), and second, for motion correction (MC), with particular attention on ultralow-dose PET scans. AC and SC are usually performed using the attenuation maps (μ-maps) obtained from a computed tomography (CT) scan performed prior to PET. As ultralow-dose PET scans are now made possible

with long-AFOV PET scanners, it is desirable to further reduce the radiation dose by estimating the μ-maps from the Lu background radiation, when an additional CT scan can be avoided. We will primarily study our methodology with the uEXPLORER scanner, which is the world’s first total-body PET scanner that can

simultaneously image the entire body, and will employ the Lu background data together with the PET emission data in maximum likelihood reconstruction of attenuation and activity (MLAA). We expect to achieve improved quantitative accuracy compared to prior studies as the high sensitivity and increased flux of background radiation

originating from the large volume of Lu-based detectors in long-AFOV PET scanners, in addition to the ability of scanning the entire body in a single bed position, enables more efficient utilization of the Lu background. Finally, as human motion causes quantification bias by introducing image blurring and attenuation-emission mismatches,

we will use a data-driven total-body MC framework to correct both the Lu-background-based μ-maps and the PET images. We will study the effects of different types of motion on PET quantification in two cases: first, when PET emission data is used for motion-estimation and second, feasibility of utilizing the Lu background data in

motion-estimation, particularly in ultralow-dose scans or body regions with low radiotracer uptake, in which standard emission-based data-driven motion estimation methods are prone to error. This is especially important in total-body PET, as motion in one region of body could affect the AC and SC in other regions. We believe this

contribution will improve the quantification and diagnosis capability in the new generation of PET scanners and will enable wider clinical and research applications of ultralow-dose PET, particularly in sensitive populations such as infants, children, and adolescents, leading to better understanding of human health.