# TRD 4: Platforms for multi-modal and multi-scale imaging data

> **NIH NIH P41** · HUGO W. MOSER RES INST KENNEDY KRIEGER · 2024 · $263,977

## Abstract

TRD 4: Platforms for multi-modal and multi-scale imaging data
Lead Principal investigator: Susumu Mori, Professor of Radiology; Co-Principal investigators: Brian Caffo,
Professor of Biostatistics; Jeremias Sulam, Assistant professor of Biomedical Engineering
Co-investigators: Andreia Faria, Michael Miller, Tilak Ratnanather, Laurent Younes
The role of TRD4 is to develop new technologies and platforms to integrate and analyze complex multi-modal
and multi-scale imaging data via collaboration with other TRDs and CPs. In the past two decades, we have
witnessed remarkable advances in image acquisition, processing, and analysis technologies for brain MRI. As
we enter a new decade, however, there remain several key areas in combining information across the macro-
meso-micro scales, and discovering predictive models for which we require significant advances in existing
tools and technologies. One of the significant opportunities going forward is to leverage the rapidly evolving
data science technologies which are now emerging and opening new frontiers for researchers. This will require
advances not only in collections of software to analyze each dimension but also a new generalized framework
to integrate and explore the data. Another important development in recent years is the surge of heavily data-
driven approaches such as deep learning. We believe this is a great time to invest time and resources to
evaluate their capability by comparing them with conventional engineering approaches. More importantly, there
is great potential in combining these two approaches to test improvements in precision, accuracy, and/or
efficiency. We are uniquely positioned to take a lead in this sphere by leveraging our experiences and
resources (tools and data) accumulated in the past 20 years. Our specific aims will be: (1) To integrate and test
deep learning (DL) approaches in image data acquisitions and analyses to solve inverse problems for
estimation of latent variables in brain MRI. These variables include enhancement of SNR, anatomical
resolutions, and underlying anatomical features such as axonal alignments; (2) To develop technologies and
platforms for characterizing models that predict the key factors determining brain diseases by integrating
multiple imaging modalities, time-domain data, and non-imaging information through statistics. Our models will
focus on etiology, pathology and prognosis considering retrospective, cross-sectional and prospective data; (3)
To integrate DL approaches to brain mapping strategies such as registration, image segmentation, and lesion
detections. Using our rich resources for annotated image libraries (atlases), conventional segmentation /
detection tools, and expertise, we will develop DL-based approaches and evaluate their efficacy, including
comparison with conventional approaches in terms of accuracy and efficiency. Improvement of the
performance by combining these two approaches will be tested. Furthermore, we will develop a f...

## Key facts

- **NIH application ID:** 10828792
- **Project number:** 5P41EB031771-04
- **Recipient organization:** HUGO W. MOSER RES INST KENNEDY KRIEGER
- **Principal Investigator:** SUSUMU MORI
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $263,977
- **Award type:** 5
- **Project period:** 2021-07-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10828792

## Citation

> US National Institutes of Health, RePORTER application 10828792, TRD 4: Platforms for multi-modal and multi-scale imaging data (5P41EB031771-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10828792. Licensed CC0.

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