Evidence map›Paper›PMID 32072677›Full record

ArticleMagnetic resonance in medicine2020

Does the magnetization transfer effect bias chemical exchange saturation transfer effects? Quantifying chemical exchange saturation transfer in the presence of magnetization transfer.

Alex K Smith, Kevin J Ray, James R Larkin, Martin Craig, Seth A Smith, Michael A Chappell

Open access · hybridAbstract 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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.2field-weighted citation impact, top 61% 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

4 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
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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

6 authors at 3 institutions in 2 countries.

Alex K SmithWellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-8005-9982
Kevin J RayWellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.
James R LarkinCancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-4169-8447
Martin CraigInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Seth A SmithVanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.
Michael A ChappellWellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.ORCID 0000-0003-1802-4214
University of Oxford · GBCancer Research UK · GBVanderbilt University · US

Funding

Cancer Research UK C5255/A18085Department of HealthWellcome Trust 203139/Z/16/Z
6 · The paper itself

Abstract

purposeChemical exchange saturation transfer (CEST) is an MRI technique sensitive to the presence of low-concentration solute protons exchanging with water. However, magnetization transfer (MT) effects also arise when large semisolid molecules interact with water, which biases CEST parameter estimates if quantitative models do not account for macromolecular effects. This study establishes under what conditions this bias is significant and demonstrates how using an appropriate model provides more accurate quantitative CEST measurements.

methodsCEST and MT data were acquired in phantoms containing bovine serum albumin and agarose. Several quantitative CEST and MT models were used with the phantom data to demonstrate how underfitting can influence estimates of the CEST effect. CEST and MT data were acquired in healthy volunteers, and a two-pool model was fit in vivo and in vitro, whereas removing increasing amounts of CEST data to show biases in the CEST analysis also corrupts MT parameter estimates.

resultsWhen all significant CEST/MT effects were included, the derived parameter estimates for each CEST/MT pool significantly correlated (P < .05) with bovine serum albumin/agarose concentration; minimal or negative correlations were found with underfitted data. Additionally, a bootstrap analysis demonstrated that significant biases occur in MT parameter estimates (P < .001) when unmodeled CEST data are included in the analysis.

conclusionsThese results indicate that current practices of simultaneously fitting both CEST and MT effects in model-based analyses can lead to significant bias in all parameter estimates unless a sufficiently detailed model is utilized. Therefore, care must be taken when quantifying CEST and MT effects in vivo by properly modeling data to minimize these biases.

Indexed as

Magnetic Resonance ImagingProtonsBiasHumansPhantoms, ImagingProtonsamideCESTMTNOEqMTquantitative

Identifiers

PMID32072677
PMCPMC7317383
OpenAlexW3008678214

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.