Evidence map›Paper›PMID 36669874›Full record

ArticleMagnetic resonance in medicine2023

Highly accelerated dynamic acquisition of 3D grid-tagged hyperpolarized-gas lung images using compressed sensing.

William J Garrison, Kun Qing, Sina Tafti, John P Mugler, Y Michael Shim, Jaime F Mata, Gordon D Cates, Eduard E de Lange, Craig H Meyer, Jing Cai and 1 more

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

Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 36% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
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  3. Review
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

11 authors at 3 institutions in 2 countries.

William J GarrisonDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.
Kun QingDepartment of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
Sina TaftiDepartment of Physics, University of Virginia, Charlottesville, Virginia, USA.
John P MuglerDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.
Y Michael ShimDepartment of Medicine, University of Virginia, Charlottesville, Virginia, USA.
Jaime F MataDepartment of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
Gordon D CatesDepartment of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
Eduard E de LangeDepartment of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
Craig H MeyerDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.
Jing CaiDepartment of Health Technology and Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong, People's Republic of China.
G Wilson MillerDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.ORCID 0000-0002-4833-8321
University of Virginia · USCity Of Hope National Medical Center · USHong Kong Polytechnic University · HK

Funding

The integrated Translational Health Research Institute of Virginia (iTHRIV): Using Data to Improve HealthUL1TR003015 · NCATS · UNIVERSITY OF VIRGINIA · PI BROWN, DONALD E, JOHNSTON, KAREN C. · 2019 to 2023
$20.3M
Assessing deformable image registration in the lung using hyperpolarized-gas MRIR21CA195317 · NCI · DUKE UNIVERSITY · PI CAI, JING, MILLER, G. WILSON · 2016 to 2017
$447k
NCATS NIH HHS UL1 TR003015NCI NIH HHS R21 CA195317
6 · The paper itself

Abstract

purposeTo develop and test compressed sensing-based multiframe 3D MRI of grid-tagged hyperpolarized gas in the lung. THEORY AND

methodsApplying grid-tagging RF pulses to inhaled hyperpolarized gas results in images in which signal intensity is predictably and sparsely distributed. In the present work, this phenomenon was used to produce a sampling pattern in which k-space is undersampled by a factor of approximately seven, yet regions of high k-space energy remain densely sampled. Three healthy subjects received multiframe 3D

resultsTags remained clearly resolvable for 4-6 timepoints (5-8 s) in each subject. Displacement maps revealed noteworthy temporal and spatial nonlinearities in lung motion during exhalation. Compressive normal strains occurred along all three principal directions but were primarily oriented in the head-foot direction. Fractional volume changes displayed clear bilateral symmetry, but with the lower lobes displaying slightly higher change than the upper lobes in 2 of the 3 subjects.

conclusionWe developed a compressed sensing-based method for multiframe 3D MRI of grid-tagged hyperpolarized gas in the lung during exhalation. This method successfully overcomes previous challenges for 3D dynamic grid-tagging, allowing time-resolved biomechanical readouts of lung function to be generated.

Indexed as

Data CompressionLungHumansMagnetic Resonance ImagingMaleRespirationcompressed sensinghyperpolarized gaspulmonary biomechanicstagging MRIvariable-density MR sampling

Identifiers

PMID36669874
PMCPMC10760126
OpenAlexW4317581830

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.