ReviewProgress in biomedical engineering (Bristol, England)2026
Lagrangian deformation tracking for strain imaging.
Review in Progress in biomedical engineering (Bristol, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Lagrangian strain imaging requires knowledge of the prior state of the deformation at its very core and can be accomplished if all three components of the displacement vector field can be accurately estimated. However, most ultrasound imaging is still performed along two-dimensional imaging planes, with the feasibility of computing both in plane displacement vectors (axial and lateral) key to Lagrangian deformation tracking and accumulation. Higher frame-rates can mitigate some of the impacts of out-of-plane motion in the elevational direction. Our group has been engaged in developing algorithms for Lagrangian deformation tracking and strain tensor imaging for carotid, cardiac and liver ablation imaging over the last two decades. Our algorithms currently implemented on a graphics processing unit estimate both axial and lateral displacement vectors accurately and with high spatial resolution using radiofrequency (RF) echo signals. We utilize a coarse-to-fine multilevel strategy and sinc-interpolation to both interpolate RF data at the final processing level for improved lateral displacement vector estimation and for accurate and unbiased sub-sample displacement estimation.
Indexed as
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.