# TR&D2: Advanced Statistical Image Reconstruction & Physics Informed Artificial Intelligence for Quantitative PET/MR

> **NIH NIH P41** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $280,805

## Abstract

The development of artificial intelligence (AI) methodology is of profound importance and is expected
to have major societal impact, especially its effect on medicine. In the past funding cycle, we pioneered
the application of deep neural networks (DNN) in various image reconstruction tasks and built a solid
understanding and extensive experience of its applications in medical imaging. In this new TR&D, we
propose use deep learning (DL) to push the application of AI in medical imaging beyond the traditional
image reconstruction problem. The study of novel contrast mechanisms (e.g. new MRI sequence and
new PET tracer) is a major frontline of PET/MR innovation. To achieve improved image quality, we
will incorporate anatomic image and motion correction in a novel DL-based image reconstruction
framework. We will also build our AI model based on the accumulated big imaging data at MGH while
also providing a methodology to transfer this knowledge to new studies with few existing data. Our
proposed domain adaptation and domain adaptation few shot learning technology will largely address
some of the biggest challenges of AI in medical imaging, i.e., limited training data, the generalizability
problem, thus enabling AI to be practically disseminated and used in clinical environment. Finally, we
propose to estimate the posterior distribution of the reconstructed image. The availability of the
uncertainty of reconstruction will open a new window for much more elegant and accurate diagnostic
protocols and early treatment response evaluation in precision medicine thus leading to a significant
number of new applications for PET/MR.

## Key facts

- **NIH application ID:** 10424117
- **Project number:** 2P41EB022544-06
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Jinsong Ouyang
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $280,805
- **Award type:** 2
- **Project period:** 2017-09-30 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10424117

## Citation

> US National Institutes of Health, RePORTER application 10424117, TR&D2: Advanced Statistical Image Reconstruction & Physics Informed Artificial Intelligence for Quantitative PET/MR (2P41EB022544-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10424117. Licensed CC0.

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