Evidence map›Paper›PMID 31247254›Full record

ArticleMagnetic resonance imaging2019

Applications of a deep learning method for anti-aliasing and super-resolution in MRI.

Can Zhao, Muhan Shao, Aaron Carass, Hao Li, Blake E Dewey, Lotta M Ellingsen, Jonghye Woo, Michael A Guttman, Ari M Blitz, Maureen Stone and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing 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

49 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. ECLARE: efficient cross-planar learning for anisotropic resolution enhancement.Journal of medical imaging (Bellingham, Wash.) · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Review
  17. Review
  18. Article
  19. Self-Supervised Super-Resolution of 2D Pre-clinical MRI Acquisitions.Proceedings of SPIE--the International Society for Optical Engineering · 2024
    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

13 authors.

Can ZhaoDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. Electronic address: czhao20@jhu.edu.
Muhan ShaoDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Aaron CarassDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Hao LiDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Blake E DeweyDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
Lotta M EllingsenDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland.
Jonghye WooDepartment of Radiology, Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA.
Michael A GuttmanJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Ari M BlitzJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Maureen StoneDepartment of Neural and Pain Sciences, University of Maryland, Baltimore, MD, USA.
Peter A CalabresiJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Henry HalperinJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Jerry L PrinceDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

Imaging Neurodegeneration in Multiple SclerosisR01NS082347 · NINDS · JOHNS HOPKINS UNIVERSITY · PI PETER A CALABRESI, Shiv Saidha · 2013 to 2026
$7.0M
NINDS NIH HHS R01 NS082347
6 · The paper itself

Abstract

Magnetic resonance (MR) images with both high resolutions and high signal-to-noise ratios (SNRs) are desired in many clinical and research applications. However, acquiring such images takes a long time, which is both costly and susceptible to motion artifacts. Acquiring MR images with good in-plane resolution and poor through-plane resolution is a common strategy that saves imaging time, preserves SNR, and provides one viewpoint with good resolution in two directions. Unfortunately, this strategy also creates orthogonal viewpoints that have poor resolution in one direction and, for 2D MR acquisition protocols, also creates aliasing artifacts. A deep learning approach called SMORE that carries out both anti-aliasing and super-resolution on these types of acquisitions using no external atlas or exemplars has been previously reported but not extensively validated. This paper reviews the SMORE algorithm and then demonstrates its performance in four applications with the goal to demonstrate its potential for use in both research and clinical scenarios. It is first shown to improve the visualization of brain white matter lesions in FLAIR images acquired from multiple sclerosis patients. Then it is shown to improve the visualization of scarring in cardiac left ventricular remodeling after myocardial infarction. Third, its performance on multi-view images of the tongue is demonstrated and finally it is shown to improve performance in parcellation of the brain ventricular system. Both visual and selected quantitative metrics of resolution enhancement are demonstrated.

Indexed as

AlgorithmsArtifactsBrainDeep LearningHeart VentriclesHumansHydrocephalus, Normal PressureImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMotionMultiple SclerosisMyocardial InfarctionSignal-To-Noise RatioTongueTongue NeoplasmsAliasingDeep learningMRIReconstructionSegmentationSMORESuper-resolution

Identifiers

PMID31247254
PMCPMC7094770

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.