Evidence map›Paper›PMID 38931494›Full record

ArticleSensors (Basel, Switzerland)2024

Preliminary Experience with Three Alternative Motion Sensors for 0.55 Tesla MR Imaging.

Radhika Tibrewala, Douglas Brantner, Ryan Brown, Leanna Pancoast, Mahesh Keerthivasan, Mary Bruno, Kai Tobias Block, Bruno Madore, Daniel K Sodickson, Christopher M Collins

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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
–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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Radhika TibrewalaBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.ORCID 0000-0002-3837-1144
Douglas BrantnerBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.
Ryan BrownBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.ORCID 0000-0002-2683-712X
Leanna PancoastBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.
Mahesh KeerthivasanSiemens Medical Solutions USA Inc., New York, NY 10016, USA.ORCID 0000-0002-7841-9333
Mary BrunoBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.
Kai Tobias BlockBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.
Bruno MadoreDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Daniel K SodicksonBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.
Christopher M CollinsBernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.

Funding

Ultrasound-based sensors for the fusion and motion correction of MRI and PET/CT dataR01EB030470 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI MADORE, BRUNO · 2021 to 2021
$1.3M
NIBIB NIH HHS R01 EB030470NIH HHS R21CA213169, R01DK106292, R21AG061579, R01DK114428, R01EB030470, R21EB027263
6 · The paper itself

Abstract

Due to limitations in current motion tracking technologies and increasing interest in alternative sensors for motion tracking both inside and outside the MRI system, in this study we share our preliminary experience with three alternative sensors utilizing diverse technologies and interactions with tissue to monitor motion of the body surface, respiratory-related motion of major organs, and non-respiratory motion of deep-seated organs. These consist of (1) a Pilot-Tone RF transmitter combined with deep learning algorithms for tracking liver motion, (2) a single-channel ultrasound transducer with deep learning for monitoring bladder motion, and (3) a 3D Time-of-Flight camera for observing the motion of the anterior torso surface. Additionally, we demonstrate the capability of these sensors to simultaneously capture motion data outside the MRI environment, which is particularly relevant for procedures like radiation therapy, where motion status could be related to previously characterized cyclical anatomical data. Our findings indicate that the ultrasound sensor can track motion in deep-seated organs (bladder) as well as respiratory-related motion. The Time-of-Flight camera offers ease of interpretation and performs well in detecting surface motion (respiration). The Pilot-Tone demonstrates efficacy in tracking bulk respiratory motion and motion of major organs (liver). Simultaneous use of all three sensors could provide complementary motion information outside the MRI bore, providing potential value for motion tracking during position-sensitive treatments such as radiation therapy.

Indexed as

Magnetic Resonance ImagingAlgorithmsDeep LearningHumansLiverMotionMovementRespirationUltrasonographyUrinary Bladderlow-fieldmotionMRIPilot-TonesensorstherapyTime-of-Flightultrasound

Identifiers

PMID38931494
PMCPMC11207459

What OpenQuestion holds

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LicenceCC BY
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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.