Valijonov, Jon, Soar, Peter ORCID: https://orcid.org/0000-0003-1745-9443, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673 and Tozzi, Gianluca (2026) Evaluation of direct strain field prediction in bone with data-driven image mechanics (D2IM-Strain). [Working Paper] (doi:10.64898/2026.03.31.715417)

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Abstract

Digital volume correlation (DVC) has become the benchmark experimental technique for full-field strain measurement in bone mechanics. In our previous work we developed a novel data-driven image mechanics (D2IM) approach that learns from DVC data and predicts displacement fields directly from undeformed X-ray computed tomography (XCT) images, deriving strain fields from such predictions. However, strain fields derived through numerical differentiation of displacement fields amplify high-frequency noise, and regularization techniques compromise spatial resolution while incurring substantial computational costs. Here we propose the upgrade D2IM-Strain to predict strain fields directly from XCT images of bone. Two prediction strategies were compared: displacement-derived strain and direct strain prediction. The direct strain prediction model significantly improved accuracy particularly for strain magnitudes below 10000 microstrain, taken as a representative threshold value for bone tissue yielding in compression. In addition, the direct approach reduced false-positive high-strain classifications by 75%. By eliminating numerical differentiation, the approach reduces noise amplification while maintaining computational efficiency. These findings represent a critical step toward developing robust data-driven volume correlation methods for hierarchical materials.

Item Type:	Working Paper
Uncontrolled Keywords:	strain, bone, X-ray computed tomography, digital volume correlation, deep learning, convolutional neural network, data-driven image mechanics
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	08 May 2026 11:59
URI:	https://gala.gre.ac.uk/id/eprint/53360

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