Evidence map›Paper›PMID 32333478›Full record

ArticleMagnetic resonance in medicine2020

Echo planar time-resolved imaging with subspace reconstruction and optimized spatiotemporal encoding.

Zijing Dong, Fuyixue Wang, Timothy G Reese, Berkin Bilgic, Kawin Setsompop

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

  1. Article
  2. TE-aware state analysis and evolution in dynamic network connectivity.bioRxiv : the preprint server for biology · 2026
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  6. Mapping subarachnoid cerebrospinal fluid circulation in the human brain.bioRxiv : the preprint server for biology · 2026
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  9. ArXiv · 2025
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  13. Time-resolved MR fingerprinting for TMagnetic resonance in medicine · 2025
    Article
  14. Article
  15. Article
  16. Article
  17. FastMagnetic resonance letters · 2024
    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

5 authors.

Zijing DongAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.ORCID 0000-0001-9334-0968
Fuyixue WangAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.ORCID 0000-0001-8975-2775
Timothy G ReeseAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.
Berkin BilgicAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.ORCID 0000-0002-9080-7865
Kawin SetsompopAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.

Funding

MRI CORTICOGRAPHY: DEVELOPING NEXT GENERATION MICROSCALE HUMAN CORTEX MRI SCANNERU01EB025162 · NIBIB · UNIVERSITY OF CALIFORNIA BERKELEY · PI FEINBERG, DAVID ALAN, LIU, CHUNLEI · 2017 to 2021
$13.4M
Project 4P41EB015896 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2012 to 2018
$9.8M
Next generation in-vivo diffusion imaging at submillimeter resolutionR01MH116173 · NIMH · STANFORD UNIVERSITY · PI Yogesh Rathi, Kawin Setsompop · 2018 to 2026
$6.2M
fMRI Technologies for Imaging at the Limit of Biological Spatiotemporal Resolution: Administrative SupplementR01EB019437 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI POLIMENI, JONATHAN RIZZO, SETSOMPOP, KAWIN · 2015 to 2024
$5.5M
MRI technology for restoring brain image quality for pediatric patients with metallic orthodontic hardwareR01EB028797 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Berkin Bilgic, Jason P Stockmann · 2020 to 2026
$3.0M
Rapid MRI acquisition for pediatric low-grade gliomasR01EB020613 · NIBIB · STANFORD UNIVERSITY · PI SETSOMPOP, KAWIN · 2016 to 2019
$2.5M
A Storage Area Network for Structural and Functional Image AnalysisS10RR023401 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2009 to 2009
$2.0M
SILICON GRAPHICS PRISM EXTREME 128P/1TBS10RR023043 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI SORENSEN, ALMA GREGORY · 2006 to 2006
$1.5M
Computeserver Structural &Functional Image AnalysisS10RR019307 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2004 to 2004
$496k
NCRR NIH HHS S10 RR019307NCRR NIH HHS S10 RR023043NCRR NIH HHS S10 RR023401NIBIB NIH HHS P41 EB015896NIBIB NIH HHS R01 EB019437NIBIB NIH HHS R01 EB020613NIBIB NIH HHS R01 EB028797NIBIB NIH HHS U01 EB025162NIMH NIH HHS R01 MH116173
6 · The paper itself

Abstract

purposeTo develop new encoding and reconstruction techniques for fast multi-contrast/quantitative imaging.

methodsThe recently proposed Echo Planar Time-resolved Imaging (EPTI) technique can achieve fast distortion- and blurring-free multi-contrast/quantitative imaging. In this work, a subspace reconstruction framework is developed to improve the reconstruction accuracy of EPTI at high encoding accelerations. The number of unknowns in the reconstruction is significantly reduced by modeling the temporal signal evolutions using low-rank subspace. As part of the proposed reconstruction approach, a B

resultsThe effectiveness of the proposed subspace reconstruction was demonstrated first in 2D GESE EPTI, where the reconstruction achieved higher accuracy when compared to conventional B

conclusionThe proposed subspace reconstruction and optimized temporal-variant CAIPI encoding can further improve the performance of EPTI for fast quantitative mapping.

Indexed as

Echo-Planar ImagingImage Processing, Computer-AssistedAlgorithmsArtifactsBrainProspective StudiesRetrospective StudiesEPIEPTIfast imagingquantitative imagingspatiotemporal encodingsubspace

Identifiers

PMID32333478
PMCPMC7402016

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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.