# Computational and Statistical Framework to Model Tissue Shape and Mechanics

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2022 · $565,031

## Abstract

PROJECT SUMMARY
 The morphologic and mechanical characteristics of a tissue are fundamental to understanding the
development, homeostasis, and pathology of the human body. During the previous period of funding, we
developed statistical shape modeling (SSM) methods and applied these to the study of structural hip disease.
We also developed the initial framework to integrate SSM with finite element (FE) analysis to enable the study
of shape and mechanics together. If incorporated into clinical practice, SSM and FE analysis could identify
features of the anatomy likely responsible for injury, remodeling, or repair. Geometry needed for SSM and FE
models is typically generated by segmentation of volumetric imaging data. This step can be painstakingly slow,
error prone, and cost prohibitive, which hampers clinical application of these computational techniques. We
have created a deep machine learning algorithm ‘DeepSSM’ that uses a convolutional neural network to
establish the correspondence model directly from unsegmented images. In Aim 1 we will apply DepSSM to
improve clinical understanding of structural hip disease by characterizing differences in anatomy between
symptomatic and asymptomatic individuals; these morphometric comparisons will identify anatomic features
most telling of disease, thereby guiding improvements in diagnosis. Computational advancements have
simplified the process to generate patient-specific FE models, enabling clinically focused research. However,
there is no framework to collectively visualize, compare, and interpret (i.e., post-process) results from multiple
FE models. Currently, inter-subject comparisons require oversimplifications such as averaging results over
subjectively defined regions. In Aim 2 we will develop new post-processing methods to collectively visualize,
interpret and statistically analyze FE results across multiple subjects and study groups. We will map FE results
to synthetic anatomies representing statistically meaningful distributions using the correspondence model.
Statistical parametric mapping will be applied to preserve anatomic detail through statistical testing. We will
use our published FE models of hip joint mechanics as the test system. Finally, volumetric images provide a
wealth of information that is delivered to physicians in a familiar format. Yet, tools are not available to interpret
model data with clinical findings from volumetric images. In Aim 3, we will develop methods that evaluate
relationships between shape, mechanics, and clinical findings gleaned from imaging through integrated
statistical tests and semi-automatic medical image annotation tools that utilize standard ontologies.
Quantitative CT and MRI images of the hip, which estimate bone density and cartilage ultrastructure,
respectively, will be evaluated as test datasets. To impart broad impact, we will disseminate our methods to
the community as open source software that will call core functionality provided by existing, op...

## Key facts

- **NIH application ID:** 10471785
- **Project number:** 5R01EB016701-07
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Andrew Edward Anderson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $565,031
- **Award type:** 5
- **Project period:** 2013-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10471785

## Citation

> US National Institutes of Health, RePORTER application 10471785, Computational and Statistical Framework to Model Tissue Shape and Mechanics (5R01EB016701-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10471785. Licensed CC0.

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