# Strain and Bone Fracture Healing: Image-Based Mechanics Models to Redefine the Rules

> **NIH NIH R21** · LEHIGH UNIVERSITY · 2022 · $148,318

## Abstract

PROJECT SUMMARY
The long-term goal of this research is to understand the mechanical factors that influence bone fracture healing
in large animals and humans. In the early stages of bone healing, the fragments of a broken bone can move
relative to one another. These small movements stretch the soft tissues that are involved in early fracture repair,
producing a mechanical effect known as strain. Since the 1970s, strain has been strongly linked with the biology
of fracture repair, but the conceptual framework for explaining strain in the context of bone healing has not
evolved in four decades. Today, orthopaedic surgeons are keenly aware that strain regulates fracture healing,
but they cannot measure it in their patients. Authoritative clinical textbooks are riddled with nonspecific, alarming,
and impractical advice about the risks of fixing a fracture with a bad strain environment. In the absence of clear
guidance, surgeons learn to rely on biomechanical rules of thumb for how to select the right implant for certain
types of fractures. Decades of mixed messaging and indirect discussion about strain and bone healing have
created significant barriers to innovation in clinical training and implant design. There is now a major unmet need
to develop innovative new research tools that can provide insights on how mechanical strain regulates bone
healing. To address this need, we will bring together a suite of sophisticated physics-based models and image
analysis techniques to do what has been impossible until now: directly assess strain at the tissue level and show
its association with the processes of fracture healing. This research has two technical aims. For the first aim, we
will use micro-computed tomography (µCT) scans to create 3D virtual reconstructions of the shinbones of sheep
with fractures that healed after surgery. We will simulate gait-induced loads on the bones and use the models to
measure strain in and around the fracture line. Strain measured from the models will be spatially correlated with
the new bone formation and a threshold for allowable strain will be determined. In the second aim, the focus will
be on adaptive changes that occur in old bone near a healing fracture. The image-based models will again be
used to measure strain, but now spatial cross-correlation of high-resolution data from the images will be used to
assess whether strain on the outer surface of the old bone is associated with an internal loss of bone mineral
density compared to before the injury. The results from this project will pave the way for a new paradigm of
thinking about strain and bone healing. Although we will be studying sheep, the groundbreaking methodologies
developed for this project have high translational potential for use in clinical research. The same types of
modeling and image data-mining techniques could be used to study fracture healing in human patients. This will
ultimately help improve clinical decision-making for treatment of complex frac...

## Key facts

- **NIH application ID:** 10510045
- **Project number:** 1R21AR081435-01
- **Recipient organization:** LEHIGH UNIVERSITY
- **Principal Investigator:** Hannah Dailey
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $148,318
- **Award type:** 1
- **Project period:** 2022-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10510045

## Citation

> US National Institutes of Health, RePORTER application 10510045, Strain and Bone Fracture Healing: Image-Based Mechanics Models to Redefine the Rules (1R21AR081435-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10510045. Licensed CC0.

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