Evidence map›Paper›PMID 37407374›Full record

ArticleAcademic radiology2024

A Physics-Based Algorithm to Universally Standardize Routinely Obtained Clinical T

Nahla M H Elsaid, Hemant D Tagare, Gigi Galiana

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Academic radiology, 2024. 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. Article
  2. Efficient standardization of clinical TMagnetic resonance in medicine · 2024
    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

3 authors.

Nahla M H ElsaidDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 300 Cedar St, New Haven, CT 06519 (N.M.H.E., H.D.T., G.G.). Electronic address: nahla.elsaid@yale.edu.
Hemant D TagareDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 300 Cedar St, New Haven, CT 06519 (N.M.H.E., H.D.T., G.G.); Department of Biomedical Engineering, Yale University, New Haven, Connecticut (H.D.T., G.G.).
Gigi GalianaDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 300 Cedar St, New Haven, CT 06519 (N.M.H.E., H.D.T., G.G.); Department of Biomedical Engineering, Yale University, New Haven, Connecticut (H.D.T., G.G.).

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Practical Nonlinear Gradient Encoding for Enhanced Accelerated ImagingR01EB022030 · NIBIB · YALE UNIVERSITY · PI GALIANA, GIGI · 2017 to 2020
$1.5M
NCATS NIH HHS UL1 TR001863NIBIB NIH HHS R01 EB022030
6 · The paper itself

Abstract

RATIONALE AND

objectivesMR images can be challenging for machine learning and other large-scale analyses because most clinical images, for example, T MATERIALS AND

methodsWhile previous constrained reconstruction methods are unable to reconstruct a T

resultsThis paper introduces the method and demonstrates its performance using simulations, retrospectively undersampled brain images, and prospectively acquired T

conclusionBecause T

Indexed as

AlgorithmsMagnetic Resonance ImagingBrainHeadHumansImage Processing, Computer-AssistedRetrospective StudiesAIMLMRIQuantitative T(2) mappingReconstruction

Identifiers

PMID37407374
PMCPMC10761595

What OpenQuestion holds

Textmetadata
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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.