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15 February 2021 4D cone-beam CT deformable registration using unsupervised spatial transformation network
Tonghe Wang, Yang Lei, Zhen Tian, Matt Giles, Jeffrey D. Bradley, Walter J. Curran, Tian Liu, Xiaofeng Yang
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Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116001K (2021) https://doi.org/10.1117/12.2580971
Event: SPIE Medical Imaging, 2021, Online Only
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Abstract
We propose a learning-based method to register 4D CBCT images from different respiratory phases. An unsupervised spatial transformation network (STN)-based deformable registration method is introduced for registering phases of 4D CBCT. Namely, no ground truth deformation vector filed (DVF) is needed during training. To avoid the scatter artifact effect while learning the trainable parameters, a structural similarity-based loss is used for supervision. To evaluate the proposed method, we retrospectively investigate 20 lung 4D CBCT datasets. Five-fold cross-validation was used to evaluate the proposed method. During each experiment, 16 4D CBCT datasets were used for training and the rest 4 4D CBCTs were used as testing. During training, each two phases from one patient were used as moving and fixed images. The average TRE is 1.67 ±3.30 mm. The proposed method has great potential in quantifying tumor trajectory for making clinical decision during radiation therapy.
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Tonghe Wang, Yang Lei, Zhen Tian, Matt Giles, Jeffrey D. Bradley, Walter J. Curran, Tian Liu, and Xiaofeng Yang "4D cone-beam CT deformable registration using unsupervised spatial transformation network", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116001K (15 February 2021); https://doi.org/10.1117/12.2580971
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KEYWORDS
Image registration
Lung
Image resolution
Radiotherapy
Tumors
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