Evidence map›Paper›PMID 35696540›Full record

ArticleMagnetic resonance in medicine2022

Accelerating Brain Imaging Using a Silent Spatial Encoding Axis.

Edwin Versteeg, Dennis W J Klomp, Jeroen C W Siero

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Group-Patch Joint Compression: Compressing Dynamic BMagnetic resonance in medicine · 2026
    Article
  3. Article
  4. Review
  5. 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.

Edwin VersteegDepartment of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0003-3235-3970
Dennis W J KlompDepartment of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands.
Jeroen C W SieroDepartment of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0001-5079-2868

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo characterize the acceleration capabilities of a silent head insert gradient axis that operates at the inaudible frequency of 20 kHz and a maximum gradient amplitude of 40 mT/m without inducing peripheral nerve stimulation.

methodsThe silent gradient axis' acquisitions feature an oscillating gradient in the phase-encoding direction that is played out on top of a cartesian readout, similarly as done in Wave-CAIPI. The additional spatial encoding fills k-space in readout lanes allowing for the acquisition of fewer phase-encoding steps without increasing aliasing artifacts. Fully sampled 2D gradient echo datasets were acquired both with and without the silent readout. All scans were retrospectively undersampled (acceleration factors R = 1 to 12) to compare conventional SENSE acceleration and acceleration using the silent gradient. The silent gradient amplitude and the readout bandwidth were varied to investigate the effect on artifacts and g-factor.

resultsThe silent readout reduced the g-factor for all acceleration factors when compared to SENSE acceleration. Increasing the silent gradient amplitude from 31.5 mT/m to 40 mT/m at an acceleration factor of 10 yielded a reduction in the average g-factor (g

conclusionA silent gradient axis enables high acceleration factors up to R = 10 while maintaining a g-factor close to unity (g

Indexed as

AlgorithmsImaging, Three-DimensionalBrainImage Processing, Computer-AssistedMagnetic Resonance ImagingNeuroimagingRetrospective Studiesaccelerationgradient coilgradient insertmagnetic resonance imagingparallel imagingquietsilent

Identifiers

PMID35696540
PMCPMC9544176

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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.