Fast optimization methods for image registration in adaptive radiation therapy

Leiden Repository

Fast optimization methods for image registration in adaptive radiation therapy

Type: Doctoral Thesis
Title: Fast optimization methods for image registration in adaptive radiation therapy
Author: Qiao, Y.
Issue Date: 2017-11-01
Keywords: Image registration
(stochastic) Optimization
Adaptive radiation therapy
Abstract: In this thesis we developed several stochastic optimization methods for fast image registration, leading to a 5-10 fold speedup over previous approaches. All proposed methods are implemented using C++ and integrated in the open source registration package elastix. We also exploited the usage of high performance computation resources -- the life science grid (lsgrid) to perform over $10^6$ registrations, which significantly reduced computation time for large scale computational tasks. As we have evaluated the proposed method in the application of online adaptive IMPT for prostate cancer, we expect that these methods can achieve the desirable performance for use in clinical practice.
Promotor: Supervisor: Lelieveldt B.P.F. Co-Supervisor: Staring M.
Faculty: Medicine / Leiden University Medical Center (LUMC)
University: Leiden
Uri: urn:isbn:9789462997325
Handle: http://hdl.handle.net/1887/59448
 

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application/pdf Cover 936.7Kb View/Open
application/pdf Title Page_Contents 121.0Kb View/Open
application/pdf Chapter 01 571.9Kb Under embargo until 2019-11-01 Full text at publisher site
application/pdf Chapter 02 860.6Kb Under embargo until 2019-11-01 Full text at publisher site
application/pdf Chapter 03 342.5Kb Under embargo until 2019-11-01
application/pdf Chapter 04 634.1Kb Under embargo until 2019-11-01
application/pdf Chapter 05 800.9Kb Under embargo until 2019-11-01
application/pdf Chapter 06_Summary 181.8Kb View/Open
application/pdf Summary_in Dutch 182.3Kb View/Open
application/pdf Bibliography 193.3Kb View/Open
application/pdf Appendix 142.9Kb View/Open
application/pdf Publications 117.0Kb View/Open
application/pdf Acknowledgements 129.2Kb View/Open
application/pdf Curriculum vitae 94.52Kb View/Open
application/pdf Propositions 136.2Kb View/Open

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