Evidence map›Paper›PMID 36426774›Full record

ArticleMagnetic resonance in medicine2023

HALO: A software tool for real-time head alignment in the MR scanner.

Zhiying Zhao, Gigi Galiana, Cristofer Zillo, Terry Camarro, Maolin Qiu, Xenophon Papademetris, Michelle Hampson

Open access · greenAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Trial
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

7 authors at 2 institutions in 2 countries.

Zhiying ZhaoDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID 0000-0002-0369-1308
Gigi GalianaDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.
Cristofer ZilloYale College, New Haven, Connecticut, USA.
Terry CamarroDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.
Maolin QiuDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.
Xenophon PapademetrisDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.
Michelle HampsonDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.
Yale University · USUniversity of Macau · MO

Funding

Practical Nonlinear Gradient Encoding for Enhanced Accelerated ImagingR01EB022030 · NIBIB · YALE UNIVERSITY · PI GALIANA, GIGI · 2017 to 2020
$1.5M
Neurofeedback of amygdala activity for PTSDR61MH115110 · NIMH · YALE UNIVERSITY · PI HAMPSON, MICHELLE, HARPAZ-ROTEM, ILAN · 2018 to 2021
$1.2M
NIBIB NIH HHS R01 EB022030NIMH NIH HHS R61 MH115110
6 · The paper itself

Abstract

purposeMRI studies in human subjects often require multiple scanning sessions/visits. Changes in a subject's head position across sessions result in different alignment between brain tissues and the magnetic field which leads to changes in magnetic susceptibility. These changes can have considerable impacts on acquired signals. Head ALignment Optimization (HALO), a software tool was developed by the authors for active head alignment between sessions.

methodsHALO provides real-time visual feedback of a subject's current head position relative to the position in a previous session. The tool was evaluated in a pilot sample of seven healthy human subjects.

resultsHALO was shown to enable subjects to actively align their head positions to the desired position of their initial sessions. The subjects were able to improve their head alignment significantly using HALO and achieved good alignment with their first session meeting stringent criteria similar to that used for within-run head motion (less than 2 mm translation or 2 degrees rotation in any direction from the desired position). Moreover, we found a negative correlation between the post-alignment rotation and similarity in inter-session BOLD patterns around the air-tissue interface near sinus which further highlighted the impact of tissue-field alignment on BOLD data quality.

conclusionUtilization of HALO in longitudinal studies may help to improve data quality by ensuring the consistency of susceptibility gradients in brain tissues across sessions. HALO has been made publicly available.

Indexed as

Magnetic Resonance ImagingSoftwareHumansLongitudinal StudiesRotationBOLD signalhead alignmentmotion correctionreal-time fMRI

Identifiers

PMID36426774
PMCPMC10753491
OpenAlexW4310082111

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.