# Artificial Intelligence Driven Platform for PET/MR Imaging

> **NIH NIH R56** · EMORY UNIVERSITY · 2022 · $763,919

## Abstract

PROJECT SUMMARY This project will develop and test a novel patient data-driven artificial intelligence
(AI)-assisted image processing platform to enable dose-reduced multimodal parametric simultaneous positron
emission tomography/magnetic resonance imaging (PET/MRI). Data driven multiparametric imaging is an
important approach in precision medicine with the state-of-the-art whole-body hybrid PET/MRI technology
playing increasing roles. However, the broader application and promising potential of the has yet to be realized.
Radiation exposure from the PET radiotracers remains to be a concern when using PET is used repeatedly in
follow-up examinations, especially for pediatric patients. Corrections for PET attenuation and scatter using non-
CT images from MRI for PET image reconstruction and quantification needs to run additional non-diagnostic
MRI sequences to derive PET attenuation correction (AC) maps, not only making the scan time longer but also
containing image artifact, especially when used in whole-body imaging applications. Collecting quantitative and
functional image measurements with additional MRI sequences for parametric diagnostic information can further
extend the scan time, making PET/MRI scans often intolerable by pediatric, elderly and motion-prone patients.
With the proposed AI frameworks, we will perform MRI-based attenuation correction (MbAC), not just limited in
brain but whole body which has not been solved, using patient-specific diagnostic MRI data that are collected
on the integrated PET/MRI system and to generate diagnostic-quality full-dose-equivalent PET images from low-
dose data (e.g., a fraction of standard dose of the radioactive tracer). Furthermore, high-resolution and multi-
orientation MR images can be “synthesized” from low-resolution and noisy images collected from fast MRI scans
using the developed AI frameworks. Building on our recent development of deep learning algorithms for MbAC
and synthesizing MRI, CT and PET images from various types of source images, we will: 1) develop and optimize
AI frameworks to generate desired high-resolution MR images with different contrasts from low-resolution data
collected by rapid MRI scans; 2) develop and refine AI-driven MbAC method using synthesized high-resolution
MR images and MRI-aided synthesis of full-dose-equivalent PET images from low-dose data; and 3) determine
and evaluate the performance of developed AI-driven low-dose and fast PET/MRI platform in a cohort of patients
who have received standard of care PET/CT. We will use quantitative image quality metrics and expert-review
to compare AI-generated full-dose-equivalent images with those of the “ground truth” full-dose PET/MRI and
matching PET/CT exams. This innovative low-dose and fast PET/MRI imaging approach can be implemented in
clinical settings with high efficiency, reduced cost and better patient experience, especially for those who cannot
use the standard of care PET/CT or PET/MRI procedures, enabling th...

## Key facts

- **NIH application ID:** 10652112
- **Project number:** 1R56EB033332-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Hui Mao
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $763,919
- **Award type:** 1
- **Project period:** 2022-09-19 → 2024-09-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10652112

## Citation

> US National Institutes of Health, RePORTER application 10652112, Artificial Intelligence Driven Platform for PET/MR Imaging (1R56EB033332-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10652112. Licensed CC0.

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