Evidence map›Paper›PMID 28275650›Full record

ArticleAnnals of clinical and translational neurology2017

Optimizing image registration and infarct definition in stroke research.

George W J Harston, David Minks, Fintan Sheerin, Stephen J Payne, Michael Chappell, Peter Jezzard, Mark Jenkinson, James Kennedy

Abstract read
In one paragraph

Article in Annals of clinical and translational neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

8 authors.

George W J HarstonAcute Vascular Imaging Centre Radcliffe Department of Medicine University of Oxford Level 2, John Radcliffe Hospital Oxford OX3 9DU United Kingdom.
David MinksDepartment of Neuroradiology Oxford University Hospitals NHS Trust John Radcliffe Hospital Oxford OX3 9DU United Kingdom.
Fintan SheerinDepartment of Neuroradiology Oxford University Hospitals NHS Trust John Radcliffe Hospital Oxford OX3 9DU United Kingdom.
Stephen J PayneDepartment of Engineering Science Institute of Biomedical Engineering University of Oxford Old Road Campus Research Building Oxford OX3 7DQ United Kingdom.
Michael ChappellDepartment of Engineering Science Institute of Biomedical Engineering University of Oxford Old Road Campus Research Building Oxford OX3 7DQ United Kingdom.
Peter JezzardOxford Centre for Functional MRI of the Brain Nuffield Department of Clinical Neurosciences University of Oxford John Radcliffe Hospital Level 6, West Wing Oxford OX3 7DQ United Kingdom.
Mark JenkinsonOxford Centre for Functional MRI of the Brain Nuffield Department of Clinical Neurosciences University of Oxford John Radcliffe Hospital Level 6, West Wing Oxford OX3 7DQ United Kingdom.
James KennedyAcute Vascular Imaging Centre Radcliffe Department of Medicine University of Oxford Level 2, John Radcliffe Hospital Oxford OX3 9DU United Kingdom.

Funding

The Dunhill Medical Trust OSRP1/1006
6 · The paper itself

Abstract

objectiveAccurate representation of final infarct volume is essential for assessing the efficacy of stroke interventions in imaging-based studies. This study defines the impact of image registration methods used at different timepoints following stroke, and the implications for infarct definition in stroke research.

methodsPatients presenting with acute ischemic stroke were imaged serially using magnetic resonance imaging. Infarct volume was defined manually using four metrics: 24-h b1000 imaging; 1-week and 1-month T2-weighted FLAIR; and automatically using predefined thresholds of ADC at 24 h. Infarct overlap statistics and volumes were compared across timepoints following both rigid body and nonlinear image registration to the presenting MRI. The effect of nonlinear registration on a hypothetical trial sample size was calculated.

resultsThirty-seven patients were included. Nonlinear registration improved infarct overlap statistics and consistency of total infarct volumes across timepoints, and reduced infarct volumes by 4.0 mL (13.1%) and 7.1 mL (18.2%) at 24 h and 1 week, respectively, compared to rigid body registration. Infarct volume at 24 h, defined using a predetermined ADC threshold, was less sensitive to infarction than b1000 imaging. 1-week T2-weighted FLAIR imaging was the most accurate representation of final infarct volume. Nonlinear registration reduced hypothetical trial sample size, independent of infarct volume, by an average of 13%.

interpretationNonlinear image registration may offer the opportunity of improving the accuracy of infarct definition in serial imaging studies compared to rigid body registration, helping to overcome the challenges of anatomical distortions at subacute timepoints, and reducing sample size for imaging-based clinical trials.

Identifiers

PMID28275650
PMCPMC5338168

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