Model Zoo

A Model Zoo for parameter files used for image registration with Elastix, SimpleElastix or ITKElastix in various domains. Parameter files can be uploaded via the GitHub repository.

Par0013

registration of interventional x-ray data to 3D CT for motion estimation, patient positioning or image guidance

Head & Neck 2D 3D CT X-Ray

Par0019

inter patient; rigid and B-spline transformation, advanced mean square and normalized correlation metrics

Head & Neck 3D CT

Par0028

intra patient; rigid + B-spline transformation; several B-pline knot spacings; synthesized head and neck phantoms

Head & Neck 3D CT

Par0023

intrapatient; rigid + B-spline transformation; localized mutual information combined with bending energy penalty

Head & Neck CT MRI PET

Par0058

Head-neck, lung and breast cancer patients acquired between 2016-2018. ...

Head & Neck Chest/Lung CT

Par0027

intra patient; rigid + B-spline transformation; mutual information, multi parametric mutual information

Head & Neck CT MRI PET

Par0060

Intapatient, rigid+ affine+ b-spline transformation, mutual information

Abdomen Head & Neck 2D MRI

Name	Image Properties	Description	Paper
Par0013	Head & Neck 2D 3D CT X-Ray	registration of interventional x-ray data to 3D CT for motion estimation, patient positioning or image guidance	I.M.J. van der Bom, S. Klein, M. Staring, R. Homan, L.W. Bartels, J.P.W. Pluim, "Evaluation of optimization methods for intensity-based 2D-3D registration in X-ray guided interventions", in: SPIE Medical Imaging: Image Processing, SPIE Press, vol. 7962, pp. 796223-1 - 796223-15, 2011.
Par0019	Head & Neck 3D CT	inter patient; rigid and B-spline transformation, advanced mean square and normalized correlation metrics	V. Fortunati, R.F. Verhaart, F. van der Lijn, W.J. Niessen, J.F. Veenland, M.M. Paulides and T. van Walsum, Tissue segmentation of head and neck CT images for treatment planning: A multiatlas approach combined with intensity modeling, Medical Physics 40(7), 071905 (2013)][1]
Par0028	Head & Neck 3D CT	intra patient; rigid + B-spline transformation; several B-pline knot spacings; synthesized head and neck phantoms	Charlotte L. Brouwer, Roel G.J. Kierkels, Aart A. van 't Veld, Nanna M. Sijtsema, and Harm Meertens, The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry, Radiation Oncology 2014, 9:169 ](Brouwer_et_al._-_2014_-_The_effects_of_computed_tomography_image_characteristics_and_knot_spacing_on_the_spatial_accuracy_of_B-pline_def.pdf)
Par0023	Head & Neck CT MRI PET	intrapatient; rigid + B-spline transformation; localized mutual information combined with bending energy penalty	S. Leibfarth , D. Mönnich, S. Welz, C. Siegel, N. Schwenzer, H. Schmidt, D. Zips, D. Thorwarth, A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning, Acta Oncologica 52, 1353-1359 (2013)
Par0058	Head & Neck Chest/Lung CT	Head-neck, lung and breast cancer patients acquired between 2016-2018. ...	Maspero M, Houweling AC, Savenije MH, van Heijst TC, Verhoeff JJ, Kotte AN, van den Berg CA. A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer. Physics and Imaging in Radiation Oncology. 2020 Apr 1;14:24-31. doi:[https://doi.org/10.1016/j.phro.2020.04.002](https://doi.org/10.1016/j.phro.2020.04.002);
Par0027	Head & Neck CT MRI PET	intra patient; rigid + B-spline transformation; mutual information, multi parametric mutual information	V. Fortunati, R.F. Verhaart, F. Angeloni, A. van der Lugt, W.J. Niessen, J.F. Veenland, M.M. Paulides and T. van Walsum, Feasibility of Multimodal Deformable Registration for Head and Neck Tumor Treatment Planning, Int. J. Radiation Oncology Biology and physics 90, 85-93 (2014)
Par0060	Abdomen Head & Neck 2D MRI	Intapatient, rigid+ affine+ b-spline transformation, mutual information	Terpstra ML, Maspero M, D'Agata F, Stemkens B, Intven MP, Lagendijk JJ, Van den Berg CA, Tijssen RH. Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy. Physics in Medicine & Biology. 2020 July 30; 66(15). doi:[https://doi.org/10.1088/1361-6560/ab9358](https://doi.org/10.1088/1361-6560/ab9358).

Name

Image Properties

Description

Paper

Par0013

Head & Neck 2D 3D CT X-Ray

registration of interventional x-ray data to 3D CT for motion estimation, patient positioning or image guidance

I.M.J. van der Bom, S. Klein, M. Staring, R. Homan, L.W. Bartels, J.P.W. Pluim, "Evaluation of optimization methods for intensity-based 2D-3D registration in X-ray guided interventions", in: SPIE Medical Imaging: Image Processing, SPIE Press, vol. 7962, pp. 796223-1 - 796223-15, 2011.

Par0019

Head & Neck 3D CT

inter patient; rigid and B-spline transformation, advanced mean square and normalized correlation metrics

V. Fortunati, R.F. Verhaart, F. van der Lijn, W.J. Niessen, J.F. Veenland, M.M. Paulides and T. van Walsum, Tissue segmentation of head and neck CT images for treatment planning: A multiatlas approach combined with intensity modeling, Medical Physics 40(7), 071905 (2013)][1]

Par0028

Head & Neck 3D CT

intra patient; rigid + B-spline transformation; several B-pline knot spacings; synthesized head and neck phantoms

Charlotte L. Brouwer, Roel G.J. Kierkels, Aart A. van 't Veld, Nanna M. Sijtsema, and Harm Meertens, The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry, Radiation Oncology 2014, 9:169 ](Brouwer_et_al._-_2014_-_The_effects_of_computed_tomography_image_characteristics_and_knot_spacing_on_the_spatial_accuracy_of_B-pline_def.pdf)

Par0023

Head & Neck CT MRI PET

intrapatient; rigid + B-spline transformation; localized mutual information combined with bending energy penalty

S. Leibfarth , D. Mönnich, S. Welz, C. Siegel, N. Schwenzer, H. Schmidt, D. Zips, D. Thorwarth, A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning, Acta Oncologica 52, 1353-1359 (2013)

Par0058

Head & Neck Chest/Lung CT

Head-neck, lung and breast cancer patients acquired between 2016-2018. ...

Maspero M, Houweling AC, Savenije MH, van Heijst TC, Verhoeff JJ, Kotte AN, van den Berg CA. A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer. Physics and Imaging in Radiation Oncology. 2020 Apr 1;14:24-31. doi:[https://doi.org/10.1016/j.phro.2020.04.002](https://doi.org/10.1016/j.phro.2020.04.002);

Par0027

Head & Neck CT MRI PET

intra patient; rigid + B-spline transformation; mutual information, multi parametric mutual information

V. Fortunati, R.F. Verhaart, F. Angeloni, A. van der Lugt, W.J. Niessen, J.F. Veenland, M.M. Paulides and T. van Walsum, Feasibility of Multimodal Deformable Registration for Head and Neck Tumor Treatment Planning, Int. J. Radiation Oncology Biology and physics 90, 85-93 (2014)

Par0060

Abdomen Head & Neck 2D MRI

Intapatient, rigid+ affine+ b-spline transformation, mutual information

Terpstra ML, Maspero M, D'Agata F, Stemkens B, Intven MP, Lagendijk JJ, Van den Berg CA, Tijssen RH. Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy. Physics in Medicine & Biology. 2020 July 30; 66(15). doi:[https://doi.org/10.1088/1361-6560/ab9358](https://doi.org/10.1088/1361-6560/ab9358).