ArticleBiomedical optics express2019
Volumetric quantitative optical coherence elastography with an iterative inversion method.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Phantoms for use in optical coherence elastography.Journal of biomedical optics · 2026Review
- Analysis of image formation in optical palpation.Journal of biophotonics · 2025Article
- Three-dimensional quantitative micro-elastography reveals alterations in spatial elasticity patterns in murine ovaries during ageing.Communications biology · 2025Article
- Multimodal mechano-microscopy reveals mechanical phenotypes of breast cancer spheroids in three dimensions.APL bioengineering · 2024Article
- Article
- Three-dimensional mechanical characterization of murine skeletal muscle using quantitative micro-elastography.Biomedical optics express · 2022Article
- Intravascular optical coherence elastography.Biomedical optics express · 2022Article
- Introduction to optical coherence elastography: tutorial.Journal of the Optical Society of America. A, Optics, image science, and vision · 2022Article
- Optical coherence tomography.Nature reviews. Methods primers · 2022Article
- Histological validation of in vivo assessment of cancer tissue inhomogeneity and automated morphological segmentation enabled by Optical Coherence Elastography.Scientific reports · 2020Article
- Three-dimensional imaging of cell and extracellular matrix elasticity using quantitative micro-elastography.Biomedical optics express · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is widely accepted that accurate mechanical properties of three-dimensional soft tissues and cellular samples are not available on the microscale. Current methods based on optical coherence elastography can measure displacements at the necessary resolution, and over the volumes required for this task. However, in converting this data to maps of elastic properties, they often impose assumptions regarding homogeneity in stress or elastic properties that are violated in most realistic scenarios. Here, we introduce novel, rigorous, and computationally efficient inverse problem techniques that do not make these assumptions, to realize quantitative volumetric elasticity imaging on the microscale. Specifically, we iteratively solve the three-dimensional elasticity inverse problem using displacement maps obtained from compression optical coherence elastography. This is made computationally feasible with adaptive mesh refinement and domain decomposition methods. By employing a transparent, compliant surface layer with known shear modulus as a reference for the measurement, absolute shear modulus values are produced within a millimeter-scale sample volume. We demonstrate the method on phantoms, on a breast cancer sample
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.