Evidence map›Paper›PMID 42086379›Full record

ArticleUltrasound in medicine & biology2026

3D Freehand Ultrasound Reconstruction of the Carotid Artery.

Yimeng Dou, Maxwell J Kiernan, Zhiwei Zhang, Carol Mitchell, Amber Possell, Matthew Lee, Tomy Varghese

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ultrasound in medicine & biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yimeng DouDepartment of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. Electronic address: ydou8@wisc.edu.
Maxwell J KiernanDepartment of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Zhiwei ZhangDepartment of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Carol MitchellDepartment of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Amber PossellDepartment of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Matthew LeeDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Tomy VargheseDepartment of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.

Funding

Early Detection of Vascular Dysfunction Using Biomarkers from Lagrangian Carotid Strain ImagingR01HL147866 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI VARGHESE, TOMY · 2020 to 2023
$2.9M
NHLBI NIH HHS R01 HL147866
6 · The paper itself

Abstract

objectiveSensorless alignment of two-dimensional (2D) freehand ultrasound scans for three-dimensional US (3DUS) reconstruction offers significant advantages due to its ease of use. Prior approaches have used transducers with motion sensors, which are cumbersome and inconvenient in a clinical setting, linear wobblers, or motorized 2D scanning which suffer from a small field of view (FOV) and low volume acquisition rates.

methodFreehand transverse B-mode data loops from 20 human volunteers (10 males, 10 females) were used for 3DUS reconstruction Our two-stream Physics inspired Learning-based Prediction of Pose Information (PLPPI) model explicitly integrates and utilizes speckle decorrelation as an inductive bias (temporal information) along with spatial information for alignment using 2D convolutions. A correlation layer then synergizes spatiotemporal cues for freehand frame alignment. A residual neural network (ResNet) predicted the spatial location of the input frames.

resultsPLPPI outperformed baseline deep learning networks (DLN), i.e. 2D CNN, ConvLSTM, and DC

conclusionOur model has fewer parameters, requiring less GPU memory to train for freehand 3DUS reconstruction along with a major reduction in computation time (106% speedup and 131% reduction in GPU memory usage) compared to baseline DLN.

Indexed as

Carotid ArteriesImaging, Three-DimensionalUltrasonography, Carotid ArteriesAdultFemaleHumansMaleCarotid ArteryFreehand ultrasoundReconstructionSensorlessThree-dimensionalTrackerless

Identifiers

PMID42086379
PMCPMC13402037

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Registered trials

None linked

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.