Evidence map›Paper›PMID 40082492›Full record

ArticleScientific reports2025

Widefield ultra-high-density optical breast tomography system supplementing x-ray mammography.

Miguel Mireles, Edward Xu, Morris Vanegas, Ailis Muldoon, Rahul Ragunathan, Shijie Yan, Bin Deng, Jayne Cormier, Mansi Saksena, Stefan A Carp and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

11 authors.

Miguel Mireles *Department of Bioengineering, Northeastern University, Boston, 02115, USA.
Edward Xu *Department of Bioengineering, Northeastern University, Boston, 02115, USA.
Morris VanegasDepartment of Bioengineering, Northeastern University, Boston, 02115, USA.
Ailis MuldoonAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, 02129, USA.
Rahul RagunathanDepartment of Bioengineering, Northeastern University, Boston, 02115, USA.
Shijie YanDepartment of Electrical and Computer Engineering, Northeastern University, Boston, 02115, USA.
Bin DengAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, 02129, USA.
Jayne CormierBreast Imaging Division, Massachusetts General Hospital, Boston, 02114, USA.
Mansi SaksenaBreast Imaging Division, Massachusetts General Hospital, Boston, 02114, USA.
Stefan A CarpAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, 02129, USA.
Qianqian FangDepartment of Bioengineering, Northeastern University, Boston, 02115, USA. q.fang@northeastern.edu.

Funding

Next-generation Monte Carlo eXtreme Light Transport Simulation PlatformR01GM114365 · NIGMS · NORTHEASTERN UNIVERSITY · PI FANG, QIANQIAN · 2015 to 2023
$2.9M
Ultra-high-density, compressive and media- adaptive optical breast tomography platformR01CA204443 · NCI · NORTHEASTERN UNIVERSITY · PI FANG, QIANQIAN · 2016 to 2025
$2.5M
DeepTOBIDx: deep learning-enhanced multimodal diagnostic breast imagingR01EB035186 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Bin Deng · 2024 to 2026
$2.1M
NCI NIH HHS R01 CA204443NIBIB NIH HHS R01 EB035186NIGMS NIH HHS R01 GM114365NIH HHS R01-CA204443
6 · The paper itself

Abstract

We report a wide-field compressive diffuse optical tomography (DOT) system - optical mammography co-imager (OMCI) - which aims to augment tens of thousands of existing x-ray mammography or tomosynthesis systems worldwide by adding functional assessment of breast tissue and improve cancer diagnosis. The OMCI system utilizes large field-of-view structured light illumination and single-pixel-camera based detection techniques to produce ultra-high spatial sampling density while ensuring that the inverse problem remains compact via the development of a unique target-adaptive pattern optimization technique to achieve compressive-sensing based measurements. The reconstructed images can be further enhanced by applying a compositional-prior-guided DOT reconstruction algorithm with tissue structural priors derived from a separately acquired x-ray mammography scans. In this report, we describe the design details and performance characterization of the imaging hardware as well as DOT image reconstruction pipelines. To validate this multi-modal breast DOT system, we include reconstruction results from both tissue-mimicking optical phantoms as well as clinical measurements from normal breasts obtained from a clinical study. Sample reconstructions from a breast containing a malignant tumor are also included, showing the potential of localizing and characterizing breast lesions using multi-modal measurements combining x-ray and DOT.

Indexed as

BreastBreast NeoplasmsMammographyTomography, OpticalAlgorithmsFemaleHumansImage Processing, Computer-AssistedPhantoms, Imaging

Identifiers

PMID40082492
PMCPMC11906649

What OpenQuestion holds

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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.