3d elastic-modulus imaging using ultrasound linear arrays and efficient data-driven training strategies.

Overview

Authors: Will Newman^1,2, Jamshid Ghaboussi³, Michael F Insana^1,2

Grainger College of Engineering, Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
Beckman Institute of Advanced Science and Technology, Urbana, IL 61801 USA
Grainger College of Engineering, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA

Institutions: University of Illinois Urbana-Champaign (United States)

Journal: Biomechanics and modeling in mechanobiology, volume 25, issue 2, article 34

Dates: received 18 November 2025; accepted 19 February 2026; published online 30 March 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1007/s10237-026-02056-8 · PMID 41910792 · PMCID PMC13035627 · OpenAlex W7143511889

Open access: hybrid, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: other (modality)

Methods: Connectivity

Keywords: Constitutive modeling, Elastography, Finite-element analysis, Machine learning, Ultrasound

MeSH: Elastic Modulus*, Imaging, Three-Dimensional*, Algorithms, Machine Learning, Phantoms, Imaging, Stress, Mechanical, Ultrasonography (* major topic)

Topic: Ultrasound Imaging and Elastography (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex

Funding: Nvidia; Concordia University; University of Illinois at Urbana-Champaign

Citations: not cited yet (Europe PMC); 30 references in the paper

Abstract

We are developing ultrasonic-based techniques for elastic modulus imaging throughout a tissue volume using the autoprogressive (AutoP) method with linear array transducers. AutoP discovers the constitutive properties of media by estimating the stress and strain in a volume from force-displacement measurements recorded in planes. AutoP is a data-driven machine learning technique that combines shallow neural network structures with object-specific measurements, in stark contrast with traditional deep learning techniques. In this paper, we outline a strategy for acquiring measurements from a series of compression planes across the volume and applying them in a training sequence that efficiently trains networks to model volumetric deformation patterns. We show with phantom studies that measurements collected while compressing the medium in parallel planes throughout the volume can yield elastic modulus image values within 10% of values measured independently. Deformation models are developed accurately in minutes using a comprehensive set of exact measurements. However, experimental limitations slow the learning process and ultimately limit the contrast and spatial resolution of modulus images in ways that can be minimized. An efficient imaging strategy balances the need to provide more planes of force-displacement measurements to enhance learning with the need to manage measurement errors. Test statistics obtained from the developing model can guide the learning process.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

Zenodo 17095486

License: MIT

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

At the source: zenodo.org/records/17095486

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Data

Datasets cited

github.com/willn2/newman-planar-elast-25, at github.com; found in the Zenodo archive record

Data availability

The data presented in this article are publicly available at zenodo.org/records/17095486

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 7 MeSH terms, 3 funders, 29 references.

Cite

This paper

Newman, W., Ghaboussi, J., & Insana, M. F. (2026). 3d elastic-modulus imaging using ultrasound linear arrays and efficient data-driven training strategies. Biomechanics and modeling in mechanobiology, 25(2), 34. https://doi.org/10.1007/s10237-026-02056-8

BibTeX

@article{newman20263d, author = {Newman, Will and Ghaboussi, Jamshid and Insana, Michael F}, title = {{3d elastic-modulus imaging using ultrasound linear arrays and efficient data-driven training strategies}}, journal = {Biomechanics and modeling in mechanobiology}, year = {2026}, month = mar, volume = {25}, number = {2}, pages = {34}, publisher = {Springer Science+Business Media}, issn = {1617-7959}, doi = {10.1007/s10237-026-02056-8}, url = {https://doi.org/10.1007/s10237-026-02056-8}, pmid = {41910792}, pmcid = {PMC13035627} }

RIS

TY - JOUR AU - Newman, Will AU - Ghaboussi, Jamshid AU - Insana, Michael F TI - 3d elastic-modulus imaging using ultrasound linear arrays and efficient data-driven training strategies T2 - Biomechanics and modeling in mechanobiology J2 - Biomech Model Mechanobiol PY - 2026 DA - 2026/03/30 VL - 25 IS - 2 SP - 34 SN - 1617-7959 PB - Springer Science+Business Media DO - 10.1007/s10237-026-02056-8 UR - https://doi.org/10.1007/s10237-026-02056-8 LA - en ER -

CSL-JSON

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