Evidence map›Paper›PMID 26178439›Full record

ArticleMagnetic resonance in medicine2016

Music-based magnetic resonance fingerprinting to improve patient comfort during MRI examinations.

Dan Ma, Eric Y Pierre, Yun Jiang, Mark D Schluchter, Kawin Setsompop, Vikas Gulani, Mark A Griswold

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Singing in the brain.Japanese journal of radiology · 2026
    Review
  3. Review
  4. Advanced MRI techniques in abdominal imaging.Abdominal radiology (New York) · 2024
    Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Magnetic resonance fingerprinting: an overview.European journal of nuclear medicine and molecular imaging · 2021
    Review
  12. Automated design of pulse sequences for magnetic resonance fingerprinting using physics-inspired optimization.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  13. Article
  14. Rapid BRadiology · 2021
    Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Machine Learning for Rapid Magnetic Resonance Fingerprinting Tissue Property Quantification.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020
    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

7 authors.

Dan MaDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
Eric Y PierreDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
Yun JiangDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
Mark D SchluchterDepartment of Epidemiology & Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA.
Kawin SetsompopA. A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Vikas GulaniDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
Mark A GriswoldDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.

Funding

Magnetic Resonance Fingerprinting (MRF) for Improved High Field MRR01EB017219 · NIBIB · CASE WESTERN RESERVE UNIVERSITY · PI GRISWOLD, MARK · 2013 to 2016
$2.5M
Optimization of MR Fingerprinting (MRF) for Quantitative MRIR01EB016728 · NIBIB · CASE WESTERN RESERVE UNIVERSITY · PI GRISWOLD, MARK, GULANI, VIKAS · 2014 to 2017
$2.0M
NIBIB NIH HHS R01 EB016728NIBIB NIH HHS R01 EB017219
6 · The paper itself

Abstract

purposeUnpleasant acoustic noise is a drawback of almost every MRI scan. Instead of reducing acoustic noise to improve patient comfort, we propose a technique for mitigating the noise problem by producing musical sounds directly from the switching magnetic fields while simultaneously quantifying multiple important tissue properties. THEORY AND

methodsMP3 music files were converted to arbitrary encoding gradients, which were then used with varying flip angles and repetition times in a two- and three-dimensional magnetic resonance fingerprinting (MRF) examination. This new acquisition method, named MRF-Music, was used to quantify T1 , T2 , and proton density maps simultaneously while providing pleasing sounds to the patients.

resultsMRF-Music scans improved patient comfort significantly during MRI examinations. The T1 and T2 values measured from phantom are in good agreement with those from the standard spin echo measurements. T1 and T2 values from the brain scan are also close to previously reported values.

conclusionsMRF-Music sequence provides significant improvement in patient comfort compared with the MRF scan and other fast imaging techniques such as echo planar imaging and turbo spin echo scans. It is also a fast and accurate quantitative method that quantifies multiple relaxation parameters simultaneously. Magn Reson Med 75:2303-2314, 2016. © 2015 Wiley Periodicals, Inc.

Indexed as

MusicSignal Processing, Computer-AssistedBrainHumansMagnetic Resonance ImagingPatient Comfortmagnetic resonance fingerprintingmusicquantitative imagingrelaxation time

Identifiers

PMID26178439
PMCPMC4715797

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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.