Evidence map›Paper›PMID 28152129›Full record

ArticleJournal of biomedical optics2017

Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix.

Murad Althobaiti, Hamed Vavadi, Quing Zhu

Open access · goldAbstract read
In one paragraph

Article in Journal of biomedical optics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
3.6field-weighted citation impact, top 7% of its field
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

17 citing papers in PubMed, 36 citations in OpenAlex.

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  8. Enhanced diffuse optical tomographic reconstruction using concurrent ultrasound information.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021
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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

3 authors at 2 institutions in 1 country.

Murad AlthobaitiUniversity of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut, United States.
Hamed VavadiUniversity of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut, United States.
Quing ZhuWashington University in St. Louis, Department of Biomedical Engineering, Missouri, United States.
University of Connecticut · USWashington University in St. Louis · US

Funding

Near Infrared Diffused Light Imaging with Ultrasound Guidance: Predicting Neoadjuvant Chemotherapy ResponseR01EB002136 · NIBIB · WASHINGTON UNIVERSITY · PI ZHU, QUING · 2003 to 2016
$3.4M
NIBIB NIH HHS R01 EB002136
6 · The paper itself

Abstract

Ultrasound-guided diffuse optical tomography (DOT) is a promising imaging technique that maps hemoglobin concentrations of breast lesions to assist ultrasound (US) for cancer diagnosis and treatment monitoring. The accurate recovery of breast lesion optical properties requires an effective image reconstruction method. We introduce a reconstruction approach in which US images are encoded as prior information for regularization of the inversion matrix. The framework of this approach is based on image reconstruction package “NIRFAST.” We compare this approach to the US-guided dual-zone mesh reconstruction method, which is based on Born approximation and conjugate gradient optimization developed in our laboratory. Results were evaluated using phantoms and clinical data. This method improves classification of malignant and benign lesions by increasing malignant to benign lesion absorption contrast. The results also show improvements in reconstructed lesion shapes and the spatial distribution of absorption maps.

Indexed as

Tomography, OpticalUltrasonographyAlgorithmsBreastBreast NeoplasmsFemaleHumansPhantoms, Imaging

Identifiers

PMID28152129
PMCPMC5299136
OpenAlexW2584308697

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.