Evidence map›Paper›PMID 25783205›Full record

ArticleNeuroImage2015

Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking.

Nick Todd, Oliver Josephs, Martina F Callaghan, Antoine Lutti, Nikolaus Weiskopf

Abstract read
In one paragraph

Article in NeuroImage, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

40 citing papers in PubMed.

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  5. Closed-loop fMRI at the mesoscopic scale of columns and layers: Can we do it and why would we want to?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
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  8. Assessment of Subject Head Motion in Diffusion MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2024
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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

5 authors.

Nick ToddWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK. Electronic address: nicholas.todd@ucl.ac.uk.
Oliver JosephsWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
Martina F CallaghanWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
Antoine LuttiWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK; Laboratoire de Recherche en Neuroimagerie, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Nikolaus WeiskopfWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.

Funding

European Research Council 616905Wellcome Trust 091593Wellcome Trust 091593/Z10/Z
6 · The paper itself

Abstract

We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2 × 2 × 3 factorial design with the following factors: PMC on or off; 3.0mm or 1.5mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies.

Indexed as

Head MovementsAdultAlgorithmsArtifactsEcho-Planar ImagingFemaleHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleProspective StudiesPsychomotor PerformanceSignal-To-Noise RatioVisual PerceptionWavelet Analysis3D imagingEPIFunctional MRIOptical trackingProspective motion correction

Identifiers

PMID25783205
PMCPMC4441089

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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.