Automatic SUV Extraction and Biodistribution Analysis of Preclinical PET

Preclinical Positron Emission Tomography (PET) plays a significant role in monitoring radiotracer biodistributions for the development of new drugs and cancer therapies. The Standardized Uptake Value (SUV) and the kinetic tissue parameters are some of the most important (semi-) quantitation measures in static and dynamic PET analysis. However, the calculation of those quantities hinges on the ability to clearly delineate the organ Region-Of-Interest (ROI) from an anatomical reference. Cross-comparison of different biodistributions is also cumbersome due to complex registration strategies of each animal with an unknown pose and size. Operator bias and lack of efficient co-registration strategies result in poor data reproducibility, high data variability, low data throughput and prohibits the use of fully automated data parsing and data analysis for predicting early therapy outcomes with high sensitivity. In Vivo Analytics will directly address these shortcomings by developing InVivoAX. It will be a cloud-based PET data analysis tool, which will enable automatic organ ROI extraction followed by an instantaneous biodistribution analysis of the SUV and kinetic tissue parameters. InVivoAX will automatically co-register PET images to the animal’s anatomy and will calculate biodistributions in almost real-time. InVivoAX consists of several novel parts. First, a Body Conforming Animal Mold (BCAM) enables consistent spatial and longitudinal registration of the animal’s pose and location to the PET data. Second, a statistical mouse atlas based on an Organ Probability Map (OPM) provides a digital and operator- independent organ ROI template. Third, cloud-based software with a browser-based user interface enables an automatic biodistribution analysis. InVivoAX does not rely on manual delineation of organs. A machine-driven data analysis fully eliminates operator-dependent variability and increases data reproducibility. It will enable the drug development team to quantitate the impact of candidate therapeutics with the highest accuracy, reduces the time to enter clinical trials, reduces costs, and ensures the quantification and consistency of PET data. In Aim 1, we will modify the BCAM for PET imaging by including respiratory monitoring and extended limb positioning. In addition, we will build a multi-animal plugin module that can hold up to four animals and will enable high-throughput imaging. In Aim 2, we will build an Ad Hoc OPM that increases the spatial registration accuracy of the OPM. The OPM will be built from segmented cryosections and CT scans. Furthermore, we will perform PET imaging of tumor bearing mice using 18F-FDG and confirm the ability for improved biodistribution analysis of the Ad Hoc OPM vs the static OPM. In Aim 3, we will build the cloud-based data analysis platform. The successful completion of the proposed project will help to commercialize InVivoAX, which will be sold as a Software-as-a-Service (SaaS) using different plugin modules for existing PET systems.