Xinzhi Teng1, Yingxuan Chen2, Yawei Zhang2, Lei Ren2. 1. Duke Kunshan University, Kunshan, China. 2. Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
Abstract
BACKGROUND: To investigate the feasibility of using a supervised convolutional neural network (CNN) to register phase-to-phase deformable vector field of lung 4D-CT/4D-cone beam CT for 4D dose accumulation, contour propagation, motion modeling, or target verification. METHODS: We built a CNN-based deep learning method to register the deformation field directly between phases of patients' 4D-CT or 4D-cone beam CT. The input consists of patch pairs of two phases, while the output is the corresponding deformation field that registers the patch pairs. The centers of the patch pairs were uniformly sampled across the lung, and the size of the patches was chosen to cover the range of the respiratory motion. The network was trained to generate deformation field that matches with the reference deformation field generated by VelocityAI (Varian). The network is structured with four convolutional layers, two average pooling layers, and two fully connected layers. Half mean squared error is applied to guide the study as loss function. Nine patients with eleven sets of 4D-CT/cone beam CT image volumes were used for training and testing. The performance of the network was validated with intra-patient and inter-patient setups. RESULTS: Registered images were generated with Velocity deformation field and the CNN deformation field, respectively. Main anatomic features such as the main vessels and the diaphragm matched well between two deformed images. In the diaphragm region, the coefficients of cross-correlation, root mean squared error, and structural similarity index measure (SSIM) between deformed images registered by CNN and VelocityAI was calculated. The cross-correlation was above 0.9 for all the intra-patient cases. CONCLUSIONS: Patch-based deep learning methods achieved comparable deformable registration accuracy as VelocityAI. Compared to VelocityAI, the deep learning method is fully automatic and faster without user dependency, which makes it more preferable in clinical applications. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: To investigate the feasibility of using a supervised convolutional neural network (CNN) to register phase-to-phase deformable vector field of lung 4D-CT/4D-cone beam CT for 4D dose accumulation, contour propagation, motion modeling, or target verification. METHODS: We built a CNN-based deep learning method to register the deformation field directly between phases of patients' 4D-CT or 4D-cone beam CT. The input consists of patch pairs of two phases, while the output is the corresponding deformation field that registers the patch pairs. The centers of the patch pairs were uniformly sampled across the lung, and the size of the patches was chosen to cover the range of the respiratory motion. The network was trained to generate deformation field that matches with the reference deformation field generated by VelocityAI (Varian). The network is structured with four convolutional layers, two average pooling layers, and two fully connected layers. Half mean squared error is applied to guide the study as loss function. Nine patients with eleven sets of 4D-CT/cone beam CT image volumes were used for training and testing. The performance of the network was validated with intra-patient and inter-patient setups. RESULTS: Registered images were generated with Velocity deformation field and the CNN deformation field, respectively. Main anatomic features such as the main vessels and the diaphragm matched well between two deformed images. In the diaphragm region, the coefficients of cross-correlation, root mean squared error, and structural similarity index measure (SSIM) between deformed images registered by CNN and VelocityAI was calculated. The cross-correlation was above 0.9 for all the intra-patient cases. CONCLUSIONS: Patch-based deep learning methods achieved comparable deformable registration accuracy as VelocityAI. Compared to VelocityAI, the deep learning method is fully automatic and faster without user dependency, which makes it more preferable in clinical applications. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.