Evidence map›Paper›PMID 28453911›Full record

ArticleMedical physics2017

Validation of biomechanical deformable image registration in the abdomen, thorax, and pelvis in a commercial radiotherapy treatment planning system.

Michael Velec, Joanne L Moseley, Stina Svensson, Björn Hårdemark, David A Jaffray, Kristy K Brock

Open access · bronzeAbstract read
In one paragraph

Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
8.2field-weighted citation impact, top 2% 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

30 citing papers in PubMed, 61 citations in OpenAlex.

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  5. Real-Time Dose-Guided Radiation Therapy.International journal of radiation oncology, biology, physics · 2025
    Review
  6. Multi-contrast 4DMR via MR multitasking: Early clinical experience and implication for liver stereotactic body radiation therapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2025
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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

6 authors at 3 institutions in 3 countries.

Michael VelecTechna Institute and Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 2M9, Canada.
Joanne L MoseleyTechna Institute and Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 2M9, Canada.
Stina SvenssonRaySearch Laboratories AB, Sveavägen 44, SE-103 65, Stockholm, Sweden.
Björn HårdemarkRaySearch Laboratories AB, Sveavägen 44, SE-103 65, Stockholm, Sweden.
David A JaffrayTechna Institute and Princess Margaret Cancer Centre, University Health Network, Toronto, M5G 2M9, Canada.
Kristy K BrockDepartment of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA.
Princess Margaret Cancer Centre · CARaySearch Laboratories (Sweden) · SEUniversity of Michigan–Ann Arbor · US

Funding

Dynamic multi-organ anatomical models for hypofractionated RT design and deliveryR01CA124714 · NCI · UNIVERSITY HEALTH NETWORK · PI BROCK, KRISTY · 2008 to 2011
$797k
NCI NIH HHS R01 CA124714
6 · The paper itself

Abstract

purposeThe accuracy of deformable image registration tools can vary widely between imaging modalities and specific implementations of the same algorithms. A biomechanical model-based algorithm initially developed in-house at an academic institution was translated into a commercial radiotherapy treatment planning system and validated for multiple imaging modalities and anatomic sites.

methodsBiomechanical deformable registration (Morfeus) is a geometry-driven algorithm based on the finite element method. Boundary conditions are derived from the model-based segmentation of controlling structures in each image which establishes a point-to-point surface correspondence. For each controlling structure, material properties and fixed or sliding interfaces are assigned. The displacements of internal volumes for controlling structures and other structures implicitly deformed are solved with finite element analysis. Registration was performed for 74 patients with images (mean vector resolution) of thoracic and abdominal 4DCT (2.8 mm) and MR (5.3 mm), liver CT-MR (4.5 mm), and prostate MR (2.6 mm). Accuracy was quantified between deformed and actual target images using distance-to-agreement (DTA) for structure surfaces and the target registration error (TRE) for internal point landmarks.

resultsThe results of the commercial implementation were as follows. The mean DTA was ≤ 1.0 mm for controlling structures and 1.0-3.5 mm for implicitly deformed structures on average. TRE ranged from 2.0 mm on prostate MR to 5.1 mm on lung MR on average, within 0.1 mm or lower than the image voxel sizes. Accuracy was not overly sensitive to changes in the material properties or variability in structure segmentations, as changing these inputs affected DTA and TRE by ≤ 0.8 mm. Maximum DTA > 5 mm occurred for 88% of the structures evaluated although these were within the inherent segmentation uncertainty for 82% of structures. Differences in accuracy between the commercial and in-house research implementations were ≤ 0.5 mm for mean DTA and ≤ 0.7 mm for mean TRE.

conclusionsAccuracy of biomechanical deformable registration evaluated on a large cohort of images in the thorax, abdomen and prostate was similar to the image voxel resolution on average across multiple modalities. Validation of this treatment planning system implementation supports biomechanical deformable registration as a versatile clinical tool to enable accurate target delineation at planning and treatment adaptation.

Indexed as

AlgorithmsTomography, X-Ray ComputedAbdomenHumansImage Processing, Computer-AssistedMalePelvisProstateThoraxbiomechanical modelsdeformable image registrationmultimodality imaging

Identifiers

PMID28453911
PMCPMC5535790
OpenAlexW2608723843

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.