Abdominal CT-CBCT deformable image registration using Deep Neural Network with directional local structural similarity

We proposed an unsupervised deep learning registration network using the combined directional local structural similarity and original images as input for multimodal CT-CBCT image registration. Due to the HU value discrepancy between CT and CBCT, conventional similarity metrics may not provide accurate image similarity measures. Directional local structural similarity measures the image’s self-similarity which reflects the underlying structural similarity regardless of the modality in use. The directional local structural similarity features could be used to drive the image deformation in conventional iterative image registration methods. A total of 30 patients’ datasets was used to train and test the network. For 15 testing cases, our results show that the alignment between the abdominal soft tissues has greatly improved after registration. The average mean-absolute error (MAE), normalized cross correlation (NCC) and target registration error (TRE) were 59.37±11.26 HU, 0.97±0.02 and 1.88±0.74 mm.