- Optimization of tau PET imaging for Alzheimer’s Disease
Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by memory loss, cognitive impairments, and behavioral disorders. 6.2 million people aged 65 and older are living with AD in the United States in 2021. Earlier diagnosis of AD holds particular significance as therapies are most effective during the pre-symptomatic stages before irreversible brain damage has occurred. Tau neurofibrillary tangles (NFTs), accumulating decades before symptomatic onset, can indicate the pre-symptomatic stages. According to Braak staging, tau NFTs start from transentorhinal, then spreading to hippocampus and other cortices at later stages. Detecting tau NFTs during early stages and clearly resolving their patterns is essential for early diagnosis and treatment monitoring of AD. With recent breakthroughs in tau tracer developments, Positron Emission Tomography (PET) can detect accumulation of tau NFTs in vivo. However, due to signal-to-noise ratio (SNR) and resolution limits of PET, accurate recovery of tau retention patterns in thin cortical regions is difficult. This is especially true for early stages when tau signal is weak. Additionally, recent longitudinal studies show that the accumulation change of tau deposits detected by PET is around 3 to 6 % per year for the AD group, and less for the preclinical AD group. This small annual change further challenges the signal detectability of current PET systems. Furthermore, 18F-MK-6240 is a newly developed tau tracer with higher affinity to tau NFTs and no off-target bindings near early Braak-staging regions, which makes it highly promising for early AD diagnosis. However, one issue with 18F-MK-6240 is the off-target bindings in the meninges. Given the thin nature of the cortical ribbon and its proximity to the meninges, quantitative accuracy of tau accumulation is significantly compromised. Consequently, there are unmet needs to further improve PET resolution and SNR for tau imaging. Our lab is currently working on deep learning (DL)-based image reconstruction methods that can improve the resolution and signal-to-noise ratio (SNR) of tau imaging. This project is supported by NIA grants R01AG078250 and R21AG067422.
- Optimizing PET imaging of neuroendocrine tumors
PET is a molecular imaging modality widely used in oncology studies due to its high sensitivity and the potential of early diagnosis. For neuroendocrine tumors (NETs), 68Ga-DOTATATE PET has been recently used in clinical routine for imaging NETs in adult and pediatric patients since 2016. It plays an important role in the diagnosis and staging of NETs. However, compared to 18F-FDG PET, the image quality of 68Ga-DOTATATE PET is lower due to much larger positron range, shorter half-life, and lower dose administration limited by generator capacity. All of these compromises the lesion detectability of 68Ga-DOTATATE PET, especially for small lesions, and can potentially lead to inaccurate NET diagnosis. As 68Ga-DOTATATE PET is increasingly used in clinics, there is an urgent and unmet need to further optimize 68Ga-DOTATATE PET/CT imaging for NET detection. Recently, data-driven methods have been developed for PET image denoising, where the PET system model is not considered. As the tumor-to-background ratio of 68Ga-DOTATATE PET is greater than 18F-FDG PET, the lesion recovery of 68Ga-DOTATATE PET can be hugely influenced by the smoothing effects as well as potential mismatches between training and testing datasets. In this project, we are developing task-driven supervised learning and transfer learning approaches to improve 68Ga-DOTATATE PET for better disease management of NETs. This project is supported by NIBIB grant R03EB030280.
Gong Lab @ UF BME 