Evidence map›Paper›PMID 33999423›Full record

ArticleMedical physics2021

Ultrasound-based sensors to monitor physiological motion.

Bruno Madore, Frank Preiswerk, Jeremy S Bredfeldt, Shenyan Zong, Cheng-Chieh Cheng

Abstract read
In one paragraph

Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Accelerated Abdominal MRI: A Review of Current Methods and Applications.Journal of magnetic resonance imaging : JMRI · 2025
    Review
  5. Review
  6. Article
  7. External Hardware and Sensors, for Improved MRI.Journal of magnetic resonance imaging : JMRI · 2023
    Review
  8. 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

5 authors.

Bruno MadoreDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Frank PreiswerkDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Jeremy S BredfeldtDepartment of Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Shenyan ZongDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Cheng-Chieh ChengDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Funding

Image Guided Therapy Center - Ultrasound-based sensor system for the monitoring of COVID-19 patientsP41EB015898 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI TEMPANY, CLARE M · 2012 to 2021
$18.9M
TRD 3 - Enabling Technologies for Intraprocedural GuidanceP41EB028741 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI Oliver Jonas · 2021 to 2026
$10.7M
HHS | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) P41EB015898NIBIB NIH HHS P41 EB015898NIBIB NIH HHS P41 EB028741
6 · The paper itself

Abstract

purposeMedical procedures can be difficult to perform on anatomy that is constantly moving. Respiration displaces internal organs by up to several centimeters with respect to the surface of the body, and patients often have limited ability to hold their breath. Strategies to compensate for motion during diagnostic and therapeutic procedures require reliable information to be available. However, current devices often monitor respiration indirectly, through changes on the outline of the body, and they may be fixed to floors or ceilings, and thus unable to follow a given patient through different locations. Here we show that small ultrasound-based sensors referred to as "organ configuration motion" (OCM) sensors can be fixed to the abdomen and/or chest and provide information-rich, breathing-related signals.

methodsBy design, the proposed sensors are relatively inexpensive. Breathing waveforms were obtained from tissues at varying depths and/or using different sensor placements. Validation was performed against breathing waveforms derived from magnetic resonance imaging (MRI) and optical tracking signals in five and eight volunteers, respectively.

resultsBreathing waveforms from different modalities were scaled so they could be directly compared. Differences between waveforms were expressed in the form of a percentage, as compared to the amplitude of a typical breath. Expressed in this manner, for shallow tissues, OCM-derived waveforms on average differed from MRI and optical tracking results by 13.1% and 15.5%, respectively.

conclusionThe present results suggest that the proposed sensors provide measurements that properly characterize breathing states. While OCM-based waveforms from shallow tissues proved similar in terms of information content to those derived from MRI or optical tracking, OCM further captured depth-dependent and position-dependent (i.e., chest and abdomen) information. In time, the richer information content of OCM-based waveforms may enable better respiratory gating to be performed, to allow diagnostic and therapeutic equipment to perform at their best.

Indexed as

MovementRespirationHumansMagnetic Resonance ImagingMotionUltrasonographymotion characterizationrespiration monitoringultrasound sensors

Identifiers

PMID33999423
PMCPMC8319119

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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.