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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