Developing a MAMBA-Neural Radiance Fields Model for Accurate and Efficient 3D X-Ray Image Reconstruction

This research proposes MAMBA-Neural Radiance Fields (MAMBANeRF), a novel model for 3D reconstruction from a single X-ray image in medical applications. By integrating the efficient sequence modeling capabilities of Mamba with the powerful spatial representation of Neural Radiance Fields (NeRF), MAMBANeRF addresses key limitations of traditional 3D reconstruction techniquessuch as Multi-View Stereo and Stereo Matching-which typically require multiple views and high computational resources. While X-ray imaging offers advantages like low radiation exposure, cost-effectiveness, and fast acquisition, single-image 3D reconstruction remains challenging due to limited spatial cues. MAMBANeRF overcomes this challenge by combining Mamba’s sequential reasoning with NeRF’s volumetric rendering, enabling the rapid generation of highprecision 3D models using standard computing hardware. The model supports enhanced visualization of complex anatomical structures to aid diagnostics and enables longitudinal comparisons for treatment evaluation. Furthermore, MAMBANeRF is well-suited for integration with virtual reality and digital twin technologies, paving the way for more accessible, intelligent, and personalized healthcare solutions.