Evidence map›Paper›PMID 40504720›Full record

ArticleIEEE transactions on medical imaging2025

OneTouch Automated Photoacoustic and Ultrasound Imaging of Breast in Standing Pose.

Huijuan Zhang, Emily Zheng, Wenhan Zheng, Chuqin Huang, Yunqi Xi, Yanda Cheng, Shuliang Yu, Saptarshi Chakraborty, Ermelinda Bonaccio, Kazuaki Takabe and 3 more

Abstract read
In one paragraph

Article in IEEE transactions on medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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  7. Review
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

13 authors.

Huijuan Zhang
Emily Zheng
Wenhan Zheng
Chuqin Huang
Yunqi Xi
Yanda Cheng
Shuliang Yu
Saptarshi Chakraborty
Ermelinda Bonaccio
Kazuaki Takabe
Xinhao C Fan
Wenyao Xu
Jun Xia

Funding

CTSA UM1 State University of New York at BuffaloUM1TR005296 · NCATS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI Sanjay Sethi · 2025 to 2026
$8.3M
mHealth Technologies for Assessing Blood Perfusion in Chronic WoundsR01EB035188 · NIBIB · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI Wenyao Xu · 2024 to 2026
$2.0M
Development of photoacoustic tomography for non-invasive, label-free imaging of tissue perfusion in chronic woundsR01EB028978 · NIBIB · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI XIA, JUN · 2021 to 2024
$1.6M
Multiparametric photoacoustic and ultrasonic imaging of the breast in cranial-caudal viewR01EB029596 · NIBIB · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI XIA, JUN · 2020 to 2023
$1.4M
NCATS NIH HHS UM1 TR005296NIBIB NIH HHS R01 EB028978NIBIB NIH HHS R01 EB029596NIBIB NIH HHS R01 EB035188
6 · The paper itself

Abstract

We developed an automated photoacoustic and ultrasound breast tomography system that images the patient in the standing pose. The system, named OneTouch-PAT, utilized linear transducer arrays with optical-acoustic combiners for effective dual-modal imaging. During scanning, subjects only need to gently attach their breasts to the imaging window, and co-registered three-dimensional ultrasonic and photoacoustic images of the breast can be obtained within one minute. Our system has a large field of view of 17 cm by 15 cm and achieves an imaging depth of 3 cm with sub-millimeter resolution. A three-dimensional deep-learning network was also developed to further improve the image quality by improving the 3D resolution, enhancing vasculature, eliminating skin signals, and reducing noise. The performance of the system was tested on four healthy subjects and 61 patients with breast cancer. Our results indicate that the ultrasound structural information can be combined with the photoacoustic vascular information for better tissue characterization. Representative cases from different molecular subtypes have indicated different photoacoustic and ultrasound features that could potentially be used for imaging-based cancer classification. Statistical analysis among all patients indicates that the regional photoacoustic intensity and vessel branching points are indicators of breast malignancy. These promising results suggest that our system could significantly enhance breast cancer diagnosis and classification.

Indexed as

BreastPhotoacoustic TechniquesUltrasonography, MammaryAdultBreast NeoplasmsDeep LearningFemaleHumansImaging, Three-DimensionalMiddle AgedPosture

Identifiers

PMID40504720
PMCPMC12707420

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Registered trials

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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.