21 March 2016 Deformable image registration with a featurelet algorithm: implementation as a 3D-slicer extension and validation
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Abstract
A radiotherapy (RT) treatment can last for several weeks. In that time organ motion and shape changes introduce uncertainty in dose application. Monitoring and quantifying the change can yield a more precise irradiation margin definition and thereby reduce dose delivery to healthy tissue and adjust tumor targeting. Deformable image registration (DIR) has the potential to fulfill this task by calculating a deformation field (DF) between a planning CT and a repeated CT of the altered anatomy. Application of the DF on the original contours yields new contours that can be used for an adapted treatment plan. DIR is a challenging method and therefore needs careful user interaction. Without a proper graphical user interface (GUI) a misregistration cannot be easily detected by visual inspection and the results cannot be fine-tuned by changing registration parameters. To provide a DIR algorithm with such a GUI available for everyone, we created the extension Featurelet-Registration for the open source software platform 3D Slicer. The registration logic is an upgrade of an in-house-developed DIR method, which is a featurelet-based piecewise rigid registration. The so called "featurelets" are equally sized rectangular subvolumes of the moving image which are rigidly registered to rectangular search regions on the fixed image. The output is a deformed image and a deformation field. Both can be visualized directly in 3D Slicer facilitating the interpretation and quantification of the results. For validation of the registration accuracy two deformable phantoms were used. The performance was benchmarked against a demons algorithm with comparable results.
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A. Renner, A. Renner, H. Furtado, H. Furtado, Y. Seppenwoolde, Y. Seppenwoolde, W. Birkfellner, W. Birkfellner, D. Georg, D. Georg, "Deformable image registration with a featurelet algorithm: implementation as a 3D-slicer extension and validation", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97844B (21 March 2016); doi: 10.1117/12.2216863; https://doi.org/10.1117/12.2216863
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