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

Yabo Fu; Yang Lei; Tonghe Wang; Jun Zhou; Pretesh Patel; Walter J. Curran; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2581091

15 February 2021 Abdominal CT-CBCT deformable image registration using Deep Neural Network with directional local structural similarity

Yabo Fu, Yang Lei, Tonghe Wang, Jun Zhou, Pretesh Patel, Walter J. Curran, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115981A (2021) https://doi.org/10.1117/12.2581091
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Yabo Fu, Yang Lei, Tonghe Wang, Jun Zhou, Pretesh Patel, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Abdominal CT-CBCT deformable image registration using Deep Neural Network with directional local structural similarity", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115981A (15 February 2021); https://doi.org/10.1117/12.2581091

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