Evidence map›Paper›PMID 40072864›Full record

ArticleIEEE transactions on bio-medical engineering2025

Time of Flight Transmission Mode Ultrasound Computed Tomography With Expected Gradient and Boundary Optimization.

Roberto C Ceccato, Andre V Pigatto, Richard C Aster, Chi-Nan Pai, Jennifer L Mueller, Sergio S Furuie

Abstract read
In one paragraph

Article in IEEE transactions on bio-medical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

6 authors.

Roberto C Ceccato
Andre V Pigatto
Richard C Aster
Chi-Nan Pai
Jennifer L Mueller
Sergio S Furuie

Funding

An integrated electrical impedance/ultrasound tomography system for pulmonary monitoring and diagnosisR21EB024683 · NIBIB · COLORADO STATE UNIVERSITY · PI MUELLER, JENNIFER LYNN · 2017 to 2019
$560k
NIBIB NIH HHS R21 EB024683
6 · The paper itself

Abstract

objectiveQuantitative time of flight in transmission mode ultrasound computed tomography (TFTM USCT) is a promising, cost-effective, and non-invasive modality, particularly suited for functional imaging. However, TFTM USCT encounters resolution challenges due to path information concentration in specific medium regions and uncertainty in transducer positioning. This study proposes a method to enhance resolution and robustness, focusing on low-frequency TFTM USCT for pulmonary imaging.

methodsThe proposed technique improves the orientation of steepest descent algorithm steps, preventing resolution degradation due to path information concentration, while allowing for a posteriori sensor positioning retrieval. Total variation regularization is employed to stabilize the inverse problem, and a modified Barzilai-Borwein method determined the step size in the steepest descent algorithm. The proposed method was validated through simulations of data on healthy and abnormal cross-sections of a human chest using MATLAB's k-Wave toolbox. Additionally, experimental data were collected using a Verasonics Vantage 64 low-frequency system and a ballistic gel torso-mimicking phantom to assess robustness under a more realistic environment, closer to that of a clinical situation.

resultsThe results showed that the proposed method significantly improved image quality and successfully retrieved sensor locations from imprecise positioning. SIGNIFICANCE: This study is the first to address transducer location uncertainty on a transducer belt in TFTM USCT and to apply an estimated gradient approach. Additionally, low-frequency USCT for lung imaging is quite novel, and this work addresses practical questions that will be important for translational development.

Indexed as

Image Processing, Computer-AssistedTomography, X-Ray ComputedAlgorithmsComputer SimulationHumansLungPhantoms, ImagingUltrasonography

Identifiers

PMID40072864
PMCPMC12481619

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.