Evidence map›Paper›PMID 31898839›Full record

ArticleMagnetic resonance in medicine2020

Improved MR fingerprinting for relaxation measurement in the presence of semisolid magnetization transfer.

Yuguang Meng, Jesse Cheung, Phillip Zhe Sun

Open access · greenAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2020. 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
0.6field-weighted citation impact, top 31% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. 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 at 2 institutions in 1 country.

Yuguang MengYerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, Georgia.ORCID 0000-0002-6734-8779
Jesse CheungEmory College of Arts and Sciences, Emory University, Atlanta, Georgia.
Phillip Zhe SunYerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, Georgia.ORCID 0000-0003-4872-1192
Emory University · USFlorida State University · US

Funding

Yerkes National Primate Research Center Role of type-I IFN in regulating COVID-19 induced inflammation and pathogenesisP51OD011132 · OD · EMORY UNIVERSITY · PI Joon Sup Lee · 2012 to 2026
$167.0M
YNPRC NHP CLINICAL MEDICINE RESIDENCY PROGRAM - SUPPLEMENTP51RR000165 · NCRR · EMORY UNIVERSITY · PI RENGARAJAN, JYOTHI · 1985 to 2011
$114.0M
NCRR NIH HHS P51 RR000165NIH HHS P51 OD011132
6 · The paper itself

Abstract

purposeTo characterize and minimize the magnetization transfer (MT) effect in MR fingerprinting (MRF) relaxation measurements with a 2-pool (2P) MT model of multiple tissue types. THEORY AND

methodsSemisolid MT effect in MRF was modeled using 2P Bloch-McConnell equations. The combinations of MT parameters of multiple tissues (white [WM] and gray matter [GM]) were used to build the MRF dictionary. Both 1-pool (1P) and 2P models were simulated to characterize the dependence on MT. Relaxations measured using MRF with spin-echo saturation-recovery (SR) or inversion-recovery preparations were compared with conventional SR-prepared T

resultsThe MRF signal was different from the 1P and 2P models. 1P MRF produced significantly (P < .05) underestimated T

conclusions2P MRF using a combination of MT parameters for multiple tissue types can measure consistent relaxations with conventional methods. With the 2P models, SR-prepared MRF would provide an option for robust relaxation measurement under heterogeneous MT.

Indexed as

BrainMagnetic Resonance ImagingGray MatterPhantoms, ImagingMRFMTsaturation-recovery

Identifiers

PMID31898839
PMCPMC7180097
OpenAlexW2997569837

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.