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

> **NIH NIH P41** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $213,679

## 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:** 9996681
- **Project number:** 5P41EB017183-07
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Fernando E Boada
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $213,679
- **Award type:** 5
- **Project period:** 2014-09-30 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9996681

## Citation

> US National Institutes of Health, RePORTER application 9996681, TR&D 3: Enriching the Data Stream: MR and PET in Concert (5P41EB017183-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9996681. Licensed CC0.

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