Tri-Hash Progressive Sampling Neural Attenuation Field for Sparse-View CBCT Reconstruction

Sparse-view CBCT reconstruction is essential to reduce the X-ray radiation dose in clinical CBCT imaging. However, reducing the number of views often lower the image quality. Existing neural radiance field (NeRF) technologies can achieve the high-quality 3D reconstruction and novel view generation in natural scenes under sparse-view conditions. Nevertheless, applying these technologies to the 3D reconstruction of human tissues in the medical field still has limitations. The recently proposed neural attenuation field (NAF) technique has shown progress in 3D reconstruction of human tissues in medical images. However, in the context of sparse views, there remains the issue of inferior reconstruction quality due to insufficient acquisition of spatial structural information in human tissues. To address these challenges, we propose a novel framework called the Tri-Hash Progressive Sampling Neural Attenuation Field (THP-NAF). First, we introduced an Enhanced Tri-Hash Representation mechanism that enhanced the extraction of 3D spatial information through the 2D plane mapping. This mechanism captures more contextual and spatial information and achieved an optimized balance between the image quality and generation efficiency. Additionally, to mitigate the sampling inefficiency caused by random sampling, we employed a Sobel-based adaptive point–ray sampling strategy. This strategy combined the global and local information for structure-aware ray sampling and could dynamically adjust the number of sampling points, thereby enhancing the sampling flexibility and efficiency. Our method was validated across multiple datasets, demonstrating its ability to improve image quality and its significant potential for clinical applications.