Sparse-View X-ray 3D Reconstruction using Hybrid Representation Neural Attenuation Fields

Abstract

X-ray 3D reconstruction has achieved superior performance in medical imaging with traditional and deep learning methods. However, when sparse-view X-ray projections are used to minimize patient exposure to radiation, these methods tend to overfit and produce blurring. To overcome this problem, we propose a novel hybrid feature representation neural attenuation field framework for sparse-view X-ray 3D reconstruction. First, we integrate tri-plane features with hash coding features as the network input, enabling the capture of intricate local details and high-frequency information. Second, we enhance the modeling of radiation attenuation across different organs by designing a specialized attenuation weight estimation network. This network enables the attenuation field estimation network to more accurately focus on the varying attenuation rates of different tissues. Third, we introduce a new multi-skip strategy, which uses the skip connection strategy for each layer of MLPs to the attenuation value and weight prediction network, markedly enhancing the performance of our method. Experiments on public datasets demonstrate the superiority of our proposed method over state-of-the-art methods.