# Automatic SUV Extraction and Biodistribution Analysis of Preclinical PET

> **NIH NIH R44** · IN VIVO ANALYTICS, INC. · 2024 · $908,835

## Abstract

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 f...

## Key facts

- **NIH application ID:** 10917862
- **Project number:** 2R44CA224841-02A1
- **Recipient organization:** IN VIVO ANALYTICS, INC.
- **Principal Investigator:** Alexander D. Klose
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $908,835
- **Award type:** 2
- **Project period:** 2019-03-11 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10917862

## Citation

> US National Institutes of Health, RePORTER application 10917862, Automatic SUV Extraction and Biodistribution Analysis of Preclinical PET (2R44CA224841-02A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10917862. Licensed CC0.

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