A hybrid biomechanical intensity based deformable image registration of lung 4DCT

Samavati, Navid; Velec, Michael; Brock, Kristy K.

doi:10.1117/12.2043560

Cited by 8 publications

(9 citation statements)

References 19 publications

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“…DIR5 and DIR6 were developed in turn by the commercial medical imaging software company Mirada Medical (Oxford, UK) and by the Computer Vision Laboratory in ETH Zurich (Zurich, Switzerland) respectively. The different DIR methods are based on the ANACONDA [17], Morfeus [18,19], B-splines, Demons, CT Deformable [20,21], and Total Variation [22] algorithm respectively (Suppl. 1).…”

Section: Deformable Image Registration (Dir) Methods and Derived Defomentioning

confidence: 99%

Assessment of dosimetric errors induced by deformable image registration methods in 4D pencil beam scanned proton treatment planning for liver tumours

Ribeiro

Knopf

Langendijk

et al. 2018

Radiotherapy and Oncology

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Section: Deformable Image Registration (Dir) Methods and Derived Defomentioning

confidence: 99%

Assessment of dosimetric errors induced by deformable image registration methods in 4D pencil beam scanned proton treatment planning for liver tumours

Ribeiro

Knopf

Langendijk

et al. 2018

Radiotherapy and Oncology

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“…Potential advantages over intensity‐based algorithms are its ready applicability to many modalities and its improved accuracy in low contrast image regions . A biomechanical DIR algorithm developed at an academic institution was shown to have an accuracy of 2–3 mm in the liver and lungs on patient images . In studies using deformable dosimeters, it predicted 3D and 4D dose distributions with gamma indices > 90% for 4.7%/2 mm and 3%/3 mm criteria .…”

Section: Introductionmentioning

confidence: 99%

“…In studies using deformable dosimeters, it predicted 3D and 4D dose distributions with gamma indices > 90% for 4.7%/2 mm and 3%/3 mm criteria . Reported computation times range from 1 min for single‐organ to 25 min for multiorgan registrations, excluding the time required for segmentation and certain preprocessing tasks such as surface mesh generation. The promising registration accuracy results however motivated the translation of this algorithm into a commercial treatment planning system (TPS) to facilitate its use in clinic.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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Purpose The 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. Methods Biomechanical 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. Results The 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. Conclusions Accuracy 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.

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“…Previous studies (Li et al, 2008;Han et al, 2014b;Samavati et al, 2015;Hipwell et al, 2016) have shown that a combined method integrating intensity-based registration with biomechanical modelling can compensate physically unrealistic estimated tissue motion (Li et al, 2008), reduce the uncertainty of biomechanical modelling ((Samavati et al, 2015)), compensate displacement residuals ((Han et al, 2014b)) due to the simplification of biomechanical models, and improve the registration performance by increasing image overlap (Han et al, 2014b;Hipwell et al, 2016). Our recent preliminary studies on deformable registration of CT lung images have demonstrated a good registration performance using a combined method (Han et al, 2014a) , in which an intensity-based image registrion process provides a displacement compensation to displacement residues of biomechanical modelling.…”

Section: Biomechanical Model Based Methods For Lung Motion Estimationmentioning

confidence: 92%

A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs

Han

Dong

McClelland

et al. 2017

Medical Image Analysis

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This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson's ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated.

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A hybrid biomechanical intensity based deformable image registration of lung 4DCT

Cited by 8 publications

References 19 publications

Assessment of dosimetric errors induced by deformable image registration methods in 4D pencil beam scanned proton treatment planning for liver tumours

Assessment of dosimetric errors induced by deformable image registration methods in 4D pencil beam scanned proton treatment planning for liver tumours

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

A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs

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