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CT-Deep learning based CBCT image registration for adaptive radio therapy

: Kuckertz, S.; Papenberg, N.; Honegger, J.; Morgas, T.; Haas, B.; Heldmann, S.


Isgum, I. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical Imaging 2020: Image Processing : 17-20 February 2020, Houston, Texas, United States
Bellingham, WA: SPIE, 2020 (Proceedings of SPIE 11313)
ISBN: 978-1-5106-3393-3
ISBN: 978-1-5106-3394-0
Paper 113130Q, 6 S.
Conference "Medical Imaging - Image Processing" <2020, Houston/Tex.>
Fraunhofer MEVIS ()

While deep learning based methods for medical deformable image registration have recently shown significant advances in both speed and accuracy, methods for use in radio therapy are still rarely proposed due to several challenges such as low contrast and artifacts in cone beam CT (CBCT) images or extreme deformations. The aim of image registration in radio therapy is to align a baseline CT and low-dose CBCT images, which allows contours to be propagated and applied doses to be tracked over time. To this end, we present a novel deep learning method for multi-modal deformable CT-CBCT registration. We train a CNN in weakly supervised manner, aiming to optimize an edge-based image similarity and a deformation regularizer including a penalty for local changes of topology and foldings. Additionally, we measure the alignment of given segmentations, facing the problem of extreme deformations. Our method receives only CT and a CBCT images as input and uses ground-truth segmentat ions exclusively during training. Furthermore, our method is not dependent on the availability of difficult to access ground-truth deformation vector fields. We train and evaluate our method on follow-up image pairs of the pelvis and compare our results to conventional iterative registration algorithms. Our experiments show that the registration accuracy of our deep learning based approach is superior to iterative registration without additional guidance by segmentations and nearly as good as iterative structure guided registration that requires ground-truth segmentations. Furthermore, our deep learning based method runs approximately 100 times faster than the iterative methods.