Deep Learning Methods for Improving Gallium 68-Based PET Imaging

Abstract Neuroendocrine tumors (NETs) are a heterogeneous group of tumors with increasing incidence, which are hard to identify in the early stage and repeatedly misdiagnosed, yielding 20%-50% of patients with distant metastases at initial diagnosis. Prostate cancer (PCa) is the most common solid-organ malignancy in men in the United

States. Distinguishing indolent from aggressive PCa and differentiation of localized disease and metastatic spread are essential for PCa treatment selection. For both NETs and PCa, disease-specific overexpression exists in cancer cells. 68Ga-DOTATATE and 68Ga-PSMA-11, the two most widely used Gallium-68 PET tracers,

can target the overexpression in cancer cells of NETs and PCa, respectively. They are essential imaging techniques for NETs and PCa management, given their high sensitivity and specificity in detecting primary tumor and metastatic spread. Due to the shorter half-life and larger positron range of Gallium 68 and the lower injection

dose limited by generator capacity, Gallium-68 PET has a lower image quality compared with 18F-FDG PET, which significantly compromises its lesion detectability and quantification accuracy. As Gallium-68 PET is increasingly adopted in clinics, there are unmet needs to further optimize it for better disease management. The

goal of this project is to improve Gallium 68-based PET image quality through deep learning (DL)-based motion correction, image reconstruction, and kinetic modeling. Aim 1 of this project is to develop a data-driven respiratory motion-correction framework with phase-matched attenuation correction. Aim 2 of this project is to

develop a DL-based image reconstruction method to improve static Gallium 68-based PET imaging. Aim 3 of this project is to further develop DL-based kinetic-modeling methods to improve dynamic Gallium 68-based PET imaging. Aim 4 of this study is to perform comprehensive clinical evaluations of the developed methods. We

expect that the integrated outcome of the four aims will be novel, effective, and robust motion correction and reconstruction methods for Gallium-68 PET that can improve its lesion detectability and quantification accuracy.