# Quantitative receptor occupancy PET

> **NIH NIH R21** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $198,940

## Abstract

PROJECT SUMMARY
Positron emission tomography (PET) receptor occupancy imaging plays an increasingly important role in
the development of central nervous system (CNS) drugs, providing critical information on drug brain
penetration, target engagement and dosing. The conventional approach to measure occupancy of a CNS drug
is to scan a subject twice, at baseline and after administration of the drug, independently apply image
reconstruction and kinetic modeling to the data of each scan, and compute occupancy by measuring fractional
reductions in specific ligand binding between the scans. The drawback of this approach, however, is the low
precision of the estimated occupancy values. We propose to develop a novel parametric reconstruction
approach that jointly reconstructs and analyzes the dynamic projections measured in the baseline and
post-drug scans, leading to direct, quantitative estimation of receptor occupancy maps with a drastically
higher signal-to-noise ratio. We expect our approach to significantly improve the precision and accuracy of
occupancy quantification, allowing more robust characterization of dose-occupancy relationships and thereby
greatly improving the quality of the information extracted from PET occupancy studies. The proposed
methodology will be evaluated in an animal model.

## Key facts

- **NIH application ID:** 10024082
- **Project number:** 5R21MH121812-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Marc David Normandin
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $198,940
- **Award type:** 5
- **Project period:** 2019-09-25 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10024082

## Citation

> US National Institutes of Health, RePORTER application 10024082, Quantitative receptor occupancy PET (5R21MH121812-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10024082. Licensed CC0.

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