Evidence map›Paper›PMID 34310297›Full record

ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2021

Model-Based X-Ray-Induced Acoustic Computed Tomography.

Prabodh Kumar Pandey, Siqi Wang, Hari Om Aggrawal, Kristina Bjegovic, Salime Boucher, Liangzhong Xiang

Open access · greenAbstract read
In one paragraph

Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

20 citing papers in PubMed, 26 citations in OpenAlex.

  1. Volumetric FLASH dosimetryPhysics in medicine and biology · 2026
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  13. Inflammation and aging: signaling pathways and intervention therapies.Signal transduction and targeted therapy · 2023
    Review
  14. Model-Based 3-D X-Ray Induced Acoustic Computerized Tomography.IEEE transactions on radiation and plasma medical sciences · 2023
    Article
  15. Article
  16. Article
  17. Ring artifacts removal in X-ray-induced acoustic computed tomography.Journal of innovative optical health sciences · 2022
    Article
  18. Article
  19. Article
  20. 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

6 authors at 3 institutions in 2 countries.

Prabodh Kumar Pandey
Siqi Wang
Hari Om Aggrawal
Kristina Bjegovic
Salime Boucher
Liangzhong Xiang
University of California, Irvine · USRadiaBeam Technologies (United States) · USUniversity of Lübeck · DE

Funding

Univ.of Calif., Irvine Cancer Center Support GrantP30CA062203 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Melanie Funes · 1994 to 2026
$57.9M
Academic-Industry Partnership for the Translation of a 4D in vivo Dosimetry Approach for Radiation TherapyR37CA240806 · NCI · UNIVERSITY OF OKLAHOMA · PI CHEN, YONG, IVANOV, VASSILI · 2019 to 2025
$2.9M
NCI NIH HHS P30 CA062203NCI NIH HHS R37 CA240806
6 · The paper itself

Abstract

X-ray-induced acoustic computed tomography (XACT) provides X-ray absorption-based contrast with acoustic detection. For its clinical translation, XACT imaging often has a limited field of view. This can result in image artifacts and overall loss of quantification accuracy. In this article, we aim to demonstrate model-based XACT image reconstruction to address these problems. An efficient matrix-free implementation of the regularized LSQR (MF-LSQR)-based minimization scheme and a noniterative model back-projection (MBP) scheme for computing XACT reconstructions have been demonstrated in this article. The proposed algorithms have been numerically validated and then used to perform reconstructions from experimental measurements obtained from an XACT setup. While the commonly used back-projection (BP) algorithm produces limited-view and noisy artifacts in the region of interest (ROI), model-based LSQR minimization overcomes these issues. The model-based algorithms also reduce the ring artifacts caused due to the nonuniformity response of the multichannel data acquisition. Using the model-based reconstruction algorithms, we are able to obtain reasonable XACT reconstructions for acoustic measurements of up to 120° view. Although the MBP is more efficient than the model-based LSQR algorithm, it provides only the structural information of the ROI. Overall, it has been demonstrated that the model-based image reconstruction yields better image quality for XACT than the standard BP. Moreover, the combination of model-based image reconstruction with different regularization methods can solve the limited-view problem for XACT imaging (in many realistic cases where the full-view dataset is unavailable), and hence pave the way for future clinical translation.

Indexed as

Image Processing, Computer-AssistedTomography, X-Ray ComputedAcousticsAlgorithmsArtifactsPhantoms, ImagingX-Rays

Identifiers

PMID34310297
PMCPMC8739265
OpenAlexW3186104914

What OpenQuestion holds

Textmetadata
LicenceTDM
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.