Evidence map›Paper›PMID 33194224›Full record

ArticleBiomedical physics & engineering express2020

Evaluation of MRI-derived surrogate signals to model respiratory motion.

Elena H Tran, Björn Eiben, Andreas Wetscherek, Uwe Oelfke, Gustav Meedt, David J Hawkes, Jamie R McClelland

Abstract read
In one paragraph

Article in Biomedical physics & engineering express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Motion modeling from 4D MR images of liver simulating phantom.Journal of applied clinical medical physics · 2022
    Article
  9. Article
  10. Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021
    Review
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.

Elena H TranCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.ORCID 0000-0002-7176-9090
Björn EibenCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.ORCID 0000-0002-5908-251X
Andreas WetscherekJoint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
Uwe OelfkeJoint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
Gustav MeedtElekta, Medical Intelligence Medizintechnik GmbH, Schwabmünchen, Germany.
David J HawkesCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.
Jamie R McClellandCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.ORCID 0000-0002-4922-0093

Funding

Cancer Research UK 19727Cancer Research UK 19908Cancer Research UK 21993Cancer Research UK 28284Cancer Research UK A21993Cancer Research UK C1060/A16464Cancer Research UK C33589/A19727Cancer Research UK C33589/CRC521
6 · The paper itself

Abstract

An MR-Linac can provide motion information of tumour and organs-at-risk before, during, and after beam delivery. However, MR imaging cannot provide real-time high-quality volumetric images which capture breath-to-breath variability of respiratory motion. Surrogate-driven motion models relate the motion of the internal anatomy to surrogate signals, thus can estimate the 3D internal motion from these signals. Internal surrogate signals based on patient anatomy can be extracted from 2D cine-MR images, which can be acquired on an MR-Linac during treatment, to build and drive motion models. In this paper we investigate different MRI-derived surrogate signals, including signals generated by applying principal component analysis to the image intensities, or control point displacements derived from deformable registration of the 2D cine-MR images. We assessed the suitability of the signals to build models that can estimate the motion of the internal anatomy, including sliding motion and breath-to-breath variability. We quantitatively evaluated the models by estimating the 2D motion in sagittal and coronal slices of 8 lung cancer patients, and comparing them to motion measurements obtained from image registration. For sagittal slices, using the first and second principal components on the control point displacements as surrogate signals resulted in the highest model accuracy, with a mean error over patients around 0.80 mm which was lower than the in-plane resolution. For coronal slices, all investigated signals except the skin signal produced mean errors over patients around 1 mm. These results demonstrate that surrogate signals derived from 2D cine-MR images, including those generated by applying principal component analysis to the image intensities or control point displacements, can accurately model the motion of the internal anatomy within a single sagittal or coronal slice. This implies the signals should also be suitable for modelling the 3D respiratory motion of the internal anatomy.

Indexed as

RespirationAgedAlgorithmsDiaphragmFemaleHumansImage Processing, Computer-AssistedImaging, Three-DimensionalLung NeoplasmsMagnetic Resonance ImagingMagnetic Resonance Imaging, CineMaleMiddle AgedMotionPhantoms, ImagingPrincipal Component Analysisimage-derived signalsinternal signalsMRI-guided radiotherapyMR-Linacrespiratory motion modelrespiratory surrogate signalssurrogate-driven motion model

Identifiers

PMID33194224
PMCPMC7655234

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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.