Evidence map›Paper›PMID 34698286›Full record

ArticleTomography (Ann Arbor, Mich.)2021

Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver.

Hsin-Chia Chen, Haw-Chiao Yang, Chih-Ching Chen, Seb Harrevelt, Yu-Chieh Chao, Jyh-Miin Lin, Wei-Hsuan Yu, Hing-Chiu Chang, Chin-Kuo Chang, Feng-Nan Hwang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

10 authors at 7 institutions in 4 countries.

Hsin-Chia ChenDepartment of Diagnostic Medical Imaging, Madou Sin-Lau Hospital, Tainan 721, Taiwan.ORCID 0000-0001-9048-3648
Haw-Chiao YangDepartment of Diagnostic Medical Imaging, Madou Sin-Lau Hospital, Tainan 721, Taiwan.
Chih-Ching ChenDepartment of Finance, Chung Yuan Christian University, Chung Li 320, Taiwan.
Seb HarreveltDepartment of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Yu-Chieh ChaoDepartment of Diagnostic Medical Imaging, Madou Sin-Lau Hospital, Tainan 721, Taiwan.
Jyh-Miin LinDevelopment and Alumni Relations, University of Cambridge, Cambridge CB5 8AB, UK.ORCID 0000-0002-2028-2899
Wei-Hsuan YuDepartment of Mathematics, National Central University, Taoyuan City 320, Taiwan.
Hing-Chiu ChangDepartment of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong.
Chin-Kuo ChangGlobal Health Program, College of Public Health, National Taiwan University, Taipei City 100, Taiwan.ORCID 0000-0002-8338-7196
Feng-Nan HwangDepartment of Mathematics, National Central University, Taoyuan City 320, Taiwan.ORCID 0000-0002-2012-3257
Sin-Lau Christian Hospital · TWNational Central University · TWChinese University of Hong Kong · HKChung Yuan Christian University · TWEindhoven University of Technology · NLNational Taiwan University · TWUniversity of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers (the least-squares solver and the L1 total-variation regularized least absolute deviation (L1TV-LAD) solver) on a graphics processing unit (GPU). The BLADE raw data were prospectively acquired and presented in random order before being assessed by two independent radiologists. Evaluation scores were examined for consistency and then by repeated measures analysis of variance (ANOVA) to identify the superior algorithm. The simulation showed the structural similarity index (SSIM) of various tensor solvers ranged between 0.995 and 0.999. Inter-reader reliability was high (Intraclass correlation coefficient (ICC) = 0.845, 95% confidence interval: 0.817, 0.87). The image score of L1TV-LAD was significantly higher than that of vendor-provided image and the least-squares method. The image score of the least-squares method was significantly lower than that of the vendor-provided image. No significance was identified in L1TV-LAD with a regularization strength of λ= 0.4-1.0. The L1TV-LAD with a regularization strength of λ= 0.4-0.7 was found consistently better than least-squares and vendor-provided reconstruction in BLADE MRI with a SENSitivity Encoding (SENSE) factor of 2. This warrants further development of the integrated computing system with the scanner.

Indexed as

AlgorithmsMagnetic Resonance ImagingComputer SimulationLeast-Squares AnalysisReproducibility of ResultsBLADE MRIgraphic processing unit (GPU)least absolute deviationnon-uniform fast Fourier transform (NUFFT)

Identifiers

PMID34698286
PMCPMC8544655
OpenAlexW3204355667

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.