|Title:||Deformierbare Bildregistrierung mittels Featurelet Algorithmen : Implementierung in die 3D Slicer Software und Validerung||Other Titles:||Featurelet based deformable image registration - implementation to 3D slicer software and validation||Language:||English||Authors:||Renner, Andreas||Qualification level:||Diploma||Advisor:||Georg, Dietmar||Issue Date:||2015||Number of Pages:||78||Qualification level:||Diploma||Abstract:||
The typical course of a radiotherapy (RT) treatment is several weeks. In that time organ motion and shape changes can introduce uncertainties in the application of dose to cancerous tissue and organs at risk. Monitoring and quantifying these changes can yield a more precise treatment 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 between a planning CT and a repeated CT of the altered anatomy. Application of the deformation field on the original contours yields new contours that can be used for an adapted plan. DIR is a challenging method and therefore needs careful user interaction and validation. Without a proper graphical user interface (GUI) a misregistration cannot be easily detected by visual inspection and the result 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 image which are rigidly registered to rectangular search regions on the target image. The output is a deformed image and a deformation field. Both can be visualized directly in 3D Slicer facilitating interpretation and quantification of the results. For validation of the registration accuracy two deformable phantoms are used. The performance is benchmarked against the initial in-house-developed algorithm with comparable or better results.
|Keywords:||Adaptive Radiotherapie; Deformierbare Bildregistrierung; Medizinische Bildverarbeitung
adaptive radiotherapy; deformabel imge registration; medical image processing
|Library ID:||AC12670810||Organisation:||E141 - Atominstitut||Publication Type:||Thesis
|Appears in Collections:||Thesis|
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checked on May 17, 2021
checked on May 17, 2021
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