Evidence map›Paper›PMID 32323097›Full record

ArticleVisual computing for industry, biomedicine, and art2020

Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors.

Gengsheng L Zeng, Edward V DiBella

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Visual computing for industry, biomedicine, and art, 2020. 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. Predicting microvascular invasion in hepatocellular carcinoma: A dual-institution study on gadoxetate disodium-enhanced MRI.Liver international : official journal of the International Association for the Study of the Liver · 2022
    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

2 authors.

Gengsheng L ZengDepartment of Radiology and Imaging Sciences, University of Utah, 729 Arapeen Drive, Salt Lake City, UT, 84108, USA. larry.zeng@hsc.utah.edu.ORCID http://orcid.org/0000-0003-0790-6043
Edward V DiBellaDepartment of Radiology and Imaging Sciences, University of Utah, 729 Arapeen Drive, Salt Lake City, UT, 84108, USA.

Funding

Reducing Metal Artifacts in Clinical X-Ray CT via Image Reconstruction TechniquesR15EB024283 · NIBIB · WEBER STATE UNIVERSITY · PI ZENG, GENGSHENG · 2018 to 2022
$644k
American Hart Association (USA) 18AJML34280074NIBIB NIH HHS R15 EB024283
6 · The paper itself

Abstract

The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based. They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints. This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm. The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study. The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm.

Indexed as

Analytics reconstructionFast magnetic resonance imagingTomographic image reconstructionUnder-sampled measurements

Identifiers

PMID32323097
PMCPMC7176778

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.