Evidence map›Paper›PMID 39687485›Full record

ArticlePhotoacoustics2025

Ultrasound-guided photoacoustic image annotation toolkit in MATLAB (PHANTOM) for preclinical applications.

Allison Sweeney, Aayush Arora, Skye A Edwards, Srivalleesha Mallidi

Abstract read
In one paragraph

Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  4. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Allison SweeneyDepartment of Biomedical Engineering, Tufts University, Medford, MA, United States.
Aayush AroraDepartment of Biomedical Engineering, Tufts University, Medford, MA, United States.
Skye A EdwardsDepartment of Biomedical Engineering, Tufts University, Medford, MA, United States.
Srivalleesha MallidiDepartment of Biomedical Engineering, Tufts University, Medford, MA, United States.

Funding

Dual function theranostic constructs for photoacoustic guided surgery and photodynamic therapyR01CA231606 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI HASAN, TAYYABA · 2019 to 2025
$3.1M
Ultrasound and Photoacoustic Imaging (USPAI) SystemS10OD026844 · OD · TUFTS UNIVERSITY MEDFORD · PI MALLIDI, SRIVALLEESHA · 2020 to 2020
$600k
Biodegradable liquid metal nanoagents for photoacoustic image-guided photodynamic therapyR21CA263694 · NCI · TUFTS UNIVERSITY MEDFORD · PI MALLIDI, SRIVALLEESHA · 2022 to 2023
$399k
NCI NIH HHS R01 CA231606NCI NIH HHS R21 CA263694NIH HHS S10 OD026844
6 · The paper itself

Abstract

Depth-dependent fluence-compensation in photoacoustic (PA) imaging is paramount for accurate quantification of chromophores from deep tissues. Here we present a user-friendly toolkit named PHANTOM (PHotoacoustic ANnotation TOolkit for MATLAB) that includes a graphical interface and assists in the segmentation of ultrasound-guided PA images. We modelled the light source configuration with Monte Carlo eXtreme and utilized 3D segmented tissues from ultrasound to generate fluence maps to depth compensate PA images. The methodology was used to analyze PA images of phantoms with varying blood oxygenation and results were validated with oxygen electrode measurements. Two preclinical models, a subcutaneous tumor and a calcified placenta, were imaged and fluence-compensated using the PHANTOM toolkit and the results were verified with immunohistochemistry. The PHANTOM toolkit provides scripts and auxiliary functions to enable biomedical researchers not specialized in optical imaging to apply fluence correction to PA images, enhancing accessibility of quantitative PAI for researchers in various fields.

Indexed as

Depth dependent fluence compensationOxygen saturationPreclinical imagingToolkit

Identifiers

PMID39687485
PMCPMC11648259

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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.