Evidence map›Paper›PMID 38860530›Full record

ArticleMagnetic resonance in medicine2024

Sequence-agnostic motion-correction leveraging efficiently calibrated Pilot Tone signals.

Yannick Brackenier, Lucilio Cordero-Grande, Sarah McElroy, Raphael Tomi-Tricot, Hugo Barbaroux, Philippa Bridgen, Shaihan J Malik, Joseph V Hajnal

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2024. 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
–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

2 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.

Yannick BrackenierBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0000-0002-9210-5939
Lucilio Cordero-GrandeBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Sarah McElroyBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0000-0002-4271-5931
Raphael Tomi-TricotBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0000-0002-6149-9154
Hugo BarbarouxBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Philippa BridgenBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Shaihan J MalikBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0000-0001-8925-9032
Joseph V HajnalBiomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Funding

Engineering and Physical Sciences Research Council EP/S022104/1Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033/FEDERWellcome Trust 201526Wellcome Trust 201526/Z/16/ZWellcome Trust 203148Wellcome Trust WT 201526/Z/16/ZWellcome Trust WT203148/Z/16/Z
6 · The paper itself

Abstract

purposeThis study leverages externally generated Pilot Tone (PT) signals to perform motion-corrected brain MRI for sequences with arbitrary k-space sampling and image contrast. THEORY AND

methodsPT signals are promising external motion sensors due to their cost-effectiveness, easy workflow, and consistent performance across contrasts and sampling patterns. However, they lack robust calibration pipelines. This work calibrates PT signal to rigid motion parameters acquired during short blocks (˜4 s) of motion calibration (MC) acquisitions, which are short enough to unobstructively fit between acquisitions. MC acquisitions leverage self-navigated trajectories that enable state-of-the-art motion estimation methods for efficient calibration. To capture the range of patient motion occurring throughout the examination, distributed motion calibration (DMC) uses data acquired from MC scans distributed across the entire examination. After calibration, PT is used to retrospectively motion-correct sequences with arbitrary k-space sampling and image contrast. Additionally, a data-driven calibration refinement is proposed to tailor calibration models to individual acquisitions. In vivo experiments involving 12 healthy volunteers tested the DMC protocol's ability to robustly correct subject motion.

resultsThe proposed calibration pipeline produces pose parameters consistent with reference values, even when distributing only six of these approximately 4-s MC blocks, resulting in a total acquisition time of 22 s. In vivo motion experiments reveal significant (

conclusionsThis study presents a framework for performing motion-corrected brain MRI in sequences with arbitrary k-space encoding and contrast, using externally generated PT signals. The DMC protocol is introduced, promoting observation of patient motion occurring throughout the examination and providing a calibration pipeline suitable for clinical deployment. The method's application is demonstrated in standard volumetric MPRAGE and SPACE sequences.

Indexed as

AlgorithmsBrainImage Processing, Computer-AssistedMagnetic Resonance ImagingMotionAdultArtifactsCalibrationFemaleHealthy VolunteersHumansMalemotion correctionparallel imagingPilot Tone (PT)reconstructionultrahigh field

Identifiers

PMID38860530
PMCPMC7617263

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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.