RNAF: Regularization neural attenuation fields for sparse-view CBCT reconstruction.

Abstract

Cone beam computed tomography (CBCT) is increasingly used in clinical settings, with the radiation dose incurred during X-ray acquisition emerging as a critical concern. Traditional algorithms for reconstructing high-quality CBCT images typically necessitate hundreds of X-ray projections, prompting a shift towards sparse-view CBCT reconstruction as a means to minimize radiation exposure. A novel approach, leveraging the Neural Attenuation Field (NAF) based on neural radiation field algorithms, has recently gained traction. This method offers rapid and promising CBCT reconstruction outcomes using a mere 50 views. Nonetheless, NAF tends to overlook the inherent structural properties of projected images, which can lead to shortcomings in accurately capturing the structural essence of the object being imaged. To address these limitations, we introduce an enhanced method: Regularization Neural Attenuation Fields (RNAF). Our approach includes two key innovations. First, we implement a hash coding regularization technique designed to retain low-frequency details within the reconstructed images, thereby preserving essential structural information. Second, we incorporate a Local Patch Global (LPG) sampling strategy. This method focuses on extracting local geometric details from the projection image, ensuring that the intensity variations in randomly sampled X-rays closely mimic those in the actual projection image. Comparative analyses across various body parts (Chest, Jaw, Foot, Abdomen, Knee) reveal that RNAF substantially outperforms existing algorithms. Specifically, its reconstruction quality exceeds that of previous NeRF-based, optimization-based, and analysis algorithms by margins of at least 2.09 dB, 3.09 dB, and 13.84 dB respectively. This significant enhancement in performance underscores the potential of RNAF as a groundbreaking solution in the realm of CBCT imaging, offering a path towards achieving high-quality reconstructions with reduced radiation exposure. Our implementation is publically available at https://github.com/springXIACJ/FRNAF.