# TR&D 4: Algorithms for functional and anatomical brain analysis

> **NIH NIH P41** · HUGO W. MOSER RES INST KENNEDY KRIEGER · 2020 · $330,762

## Abstract

TR&D 4.
Algorithms for functional and anatomical brain analysis
 Principal investigators:
 Susumu Mori, Professor of Radiology
Michael Miller, Professor of Biomedical Engineering
SUMMARY
 The overall objective of TR&D4 is to develop cutting-edge image analysis technologies that can integrate
various anatomical representations (multi-manifold) of the brain based on multi-modal MRI that includes not
only multiple MRI contrasts (e.g. T1, T2, diffusion, susceptibility imaging, CEST imaging) but also functional
MRI (fMRI), physiological MRI (measures of cerebral blood volume, CBV, flow, CBF and oxygen metabolism,
CMRO2), and MR spectroscopy (MRS) into a common anatomical framework. As TR&D 1-3 further develop
new image acquisition and signal processing techniques, the richness of the modern MRI-based observations
continues to grow with more contrasts, higher spatial dimensions, as well as the addition of a time axis, which
eventually needs to be correlated with clinical features. For example, stratification of patients by anatomical,
functional, and spectroscopic scans could play a crucial role in many collaborative projects for brain
development, ADHD, schizophrenia, and Huntington's Disease. For neonatal, Alzheimer's disease, multiple
sclerosis, and primary progressive aphasia studies, anatomical and diffusion scan results need to be integrated
over the longitudinal dimension. For certain applications, there are specific structures of interest such as the
limbic system, for which detailed surface shape analysis based on high-resolution atlases are needed. The
various image analysis tools we have developed in the past 15 years are now being used by investigators
around the world. These tools are now widely distributed through www.mristudio.org with more than 9,000
registered users, the number of processed data sets in our server exceeded 10,000 in 2014 alone, a number
that is still increasing. For the next five years, our specific aims are:
Aim 1: Integration of diffeomorphic brain mapping and multi-atlas based image feature analysis
We will integrate cutting-edge multi-atlas fusion algorithms into our LDDMM-based pipelines and develop a
new image segmentation platform through the integration of tools developed in Aim 2. We will also continue to
develop brain atlas resources through continued development of multi-atlas libraries that covers the entire age
range.
Aim 2: Integrative analysis for multi-contrast and multi-manifold mapping of the whole brain anatomy
Tools to integrate multiple image contrasts and multiple shape objects will be developed for advanced analysis,
which will include the modern MRI data developed in TR&D 1-3, high-resolution atlases based on 7T/11.7T
scanners, and tools for time dependent anatomical change analysis.
Aim 3: Deployment and Maintenance of Cloud-based service system
Toward the sunset of this National Resource Grant, it is important to establish a sustainable platform for
dissemination and services. In 2015, we intr...

## Key facts

- **NIH application ID:** 9997696
- **Project number:** 5P41EB015909-20
- **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:** 2020
- **Award amount:** $330,762
- **Award type:** 5
- **Project period:** 2000-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9997696

## Citation

> US National Institutes of Health, RePORTER application 9997696, TR&D 4: Algorithms for functional and anatomical brain analysis (5P41EB015909-20). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9997696. Licensed CC0.

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