# TR&D 3: Enriching the Data Stream: MR and PET in Concert

> **NIH NIH P41** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $209,683

## Abstract

TRD3 Project Summary 
 The broad mission of our Center for Advanced Imaging Innovation and Research (CAI2R) is to bring 
together collaborative translational research teams for the development of high-impact biomedical imaging 
technologies, with the ultimate goal of changing day-to-day clinical practice. Technology Research and 
Development (TR&D) Project 3 aims to exploit unique synergies between Magnetic Resonance Imaging (MRI) 
and Positron Emission Tomography (PET), in order to improve and inform imaging-based evaluations of tissue 
structure and function in disease. Using modern methods of machine learning and other enabling hardware 
and software, we will combine these two complementary imaging modalities much as distinct sensory 
modalities are combined into a multifaceted multisensory stream. Concrete outcomes of our work will include 
1) new techniques and technologies for motion correction in MR and PET; 2) new algorithms for the extraction 
of complementary information from MR and PET acquisitions; 3) new tracers that are tailored for combined 
MR-PET scanning rather than merely being addressed at traditional molecular targets; and 4) new means of 
elucidating the intrinsic structure and function of tissue.

## Key facts

- **NIH application ID:** 10246949
- **Project number:** 5P41EB017183-08
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Hersh Chandarana
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $209,683
- **Award type:** 5
- **Project period:** 2014-09-30 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246949

## Citation

> US National Institutes of Health, RePORTER application 10246949, TR&D 3: Enriching the Data Stream: MR and PET in Concert (5P41EB017183-08). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10246949. Licensed CC0.

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