An overview of elastography-an emerging branch of medical imaging

A Sarvazyan, TJ Hall, MW Urban… - Current Medical …, 2011 - ingentaconnect.com
From times immemorial manual palpation served as a source of information on the state of
soft tissues and allowed detection of various diseases accompanied by changes in tissue …

Elastography imaging: the 30 year perspective

J Ormachea, KJ Parker - Physics in Medicine & Biology, 2020 - iopscience.iop.org
From the development of x-ray imaging in the late 19th century, the field of medical imaging
developed an impressive array of modalities. These can measure and image a variety of …

Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio

A Kamali, M Sarabian, K Laksari - Acta biomaterialia, 2023 - Elsevier
Elasticity imaging is a technique that discovers the spatial distribution of mechanical
properties of tissue using deformation and force measurements under various loading …

Physics‐informed deep‐learning for elasticity: forward, inverse, and mixed problems

CT Chen, GX Gu - Advanced Science, 2023 - Wiley Online Library
Elastography is a medical imaging technique used to measure the elasticity of tissues by
comparing ultrasound signals before and after a light compression. The lateral resolution of …

Learning hidden elasticity with deep neural networks

CT Chen, GX Gu - Proceedings of the National Academy of …, 2021 - National Acad Sciences
Elastography is an imaging technique to reconstruct elasticity distributions of heterogeneous
objects. Since cancerous tissues are stiffer than healthy ones, for decades, elastography has …

Linear and nonlinear elastic modulus imaging: an application to breast cancer diagnosis

S Goenezen, JF Dord, Z Sink… - IEEE transactions on …, 2012 - ieeexplore.ieee.org
We reconstruct the in vivo spatial distribution of linear and nonlinear elastic parameters in
ten patients with benign (five) and malignant (five) tumors. The mechanical behavior of …

The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems

D Ray, H Ramaswamy, DV Patel, AA Oberai - arXiv preprint arXiv …, 2022 - arxiv.org
In this work, we train conditional Wasserstein generative adversarial networks to effectively
sample from the posterior of physics-based Bayesian inference problems. The generator is …

[HTML][HTML] What challenges must be overcome before ultrasound elasticity imaging is ready for the clinic?

ML Palmeri, KR Nightingale - Imaging in medicine, 2011 - ncbi.nlm.nih.gov
Ultrasound elasticity imaging has been a research interest for the past 20 years with the goal
of generating novel images of soft tissues based on their material properties (ie, stiffness …

[HTML][HTML] Resolving engineering challenges: Deep learning in frequency domain for 3D inverse identification of heterogeneous composite properties

Y Liu, Y Mei, Y Chen, B Ding - Composites Part B: Engineering, 2024 - Elsevier
The inverse identification of heterogeneous composite properties from measured
displacement/strain fields is pivotal in engineering. Traditional methodologies and emerging …

Recent results in nonlinear strain and modulus imaging

TJ Hall, PE Barboneg, AA Oberai… - Current Medical …, 2011 - ingentaconnect.com
We report a summary of recent developments and current status of our team's efforts to
image and quantify in vivo nonlinear strain and tissue mechanical properties. Our work is …