D-NAF: Dynamic Neural Attenuation Fields for 4D CBCT Reconstruction in Pulmonary Imaging

Four-dimensional Cone-beam CT (4D-CBCT) has already been integrated into many commercial radiotherapy systems to facilitate image-guided radiotherapy (IGRT). These techniques reconstruct a sequence of three-dimensional (3D) CBCT volumes based on respiratory phases, providing motion-compensated images. Nevertheless, current 4D-CBCT methods still suffer from poor imaging quality due to the limited number of views used for phase-resolved reconstruction, and deep learning-based methods require high-quality training data, which is commonly unavailable. In this paper, we propose a self-supervised 4D-CBCT imaging method based on dynamic neural attenuation fields (D-NAF). The dynamic pulmonary CBCT images are encoded into a 4D implicit representation incorporating both spatial information and temporal information. Moreover, we employed a separable spatial-temporal encoding method to reduce the dimensionality of the solution space, leading to enhanced convergence and reconstruction quality. The results demonstrate that the proposed method yields superior imaging quality in comparison to alternative approaches, with an RMSE value of 59.82 HU, PSNR of 35.33 dB and SSIM of 0.9358. This method is expected to be a new benchmark for self-supervised 4D-CBCT imaging.