elastixLogo.gif 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
Par0023

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

Head & Neck CT MRI PET
Par0027

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

Head & Neck CT MRI PET
Par0028

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

Head & Neck 3D CT
Par0058

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

Head & Neck Chest/Lung CT
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]
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)
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)
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)
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);
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).

© 2020 Viktor van der Valk