# TR&D3: Intrinsic Surface Mapping

> **NIH NIH P41** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2021 · $275,937

## Abstract

PROJECT SUMMARY - TR&D3: INTRINSIC SURFACE MAPPING
For brain imaging studies, surface mapping methods have played an important role in various scientific
discoveries from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in
Alzheimer's disease (AD). There are, however, two fundamental limitations in current surface mapping
techniques. Firstly, current methods typically parameterize different brain surfaces with the unit sphere before
their registration. The inevitable metric distortions during this parameterization step can lead to errors in the
registration of brain anatomy and reduced power in the detection of disease induced changes. Secondly, current
surface mapping tools such as FreeSurfer depend on geometric features that have limited accuracy in mapping
high order brain regions and do not consider disease-related biological mechanisms. In this project, we will
develop a novel computational framework to overcome these fundamental limitations. This novel approach builds
upon our series of shape analysis work in the Laplace-Beltrami embedding space of anatomical surfaces. This
embedding is isometric, so it eliminates the metric distortion due to spherical parameterization and resulting
errors in the maps computed by spherical registration. This general framework also enables the incorporation of
multimodal imaging features to compute diffeomorphic surface maps that improve the accuracy in aligning
corresponding anatomy and functions of human brains. Overall there are three specific aims in this project. Aim
1. Development of the surface mapping software tools under the Riemannian metric optimization framework. In
this aim, we will focus on developing a user friendly software toolset that implements the algorithms for
Riemannian Metric Optimization on Surfaces (RMOS) in the LB embedding space. Aim 2. Development of novel
RMOS surface mapping methods driven by rich contextual features. In this aim, we will develop a rich set of
contextual features to drive the RMOS computational engine and provide more anatomically meaningful brain
mapping results. Aim 3. Development of longitudinal surface mapping methods in the Laplace-Beltrami
embedding space. In this aim, we will use the RMOS framework to develop novel methods for studying the
longitudinal evolution of brain anatomy. All software tools developed in this project will be continuously distributed
in our software called Metric Optimization for Computational Anatomy (MOCA) on LONIR website.

## Key facts

- **NIH application ID:** 10135695
- **Project number:** 5P41EB015922-24
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Yonggang Shi
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $275,937
- **Award type:** 5
- **Project period:** 1998-09-30 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10135695

## Citation

> US National Institutes of Health, RePORTER application 10135695, TR&D3: Intrinsic Surface Mapping (5P41EB015922-24). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10135695. Licensed CC0.

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