$ρ$-NeRF: Leveraging Attenuation Priors in Neural Radiance Field for 3D Computed Tomography Reconstruction

• URL: http://arxiv.org/abs/2412.05322v1
• Date: Tue, 03 Dec 2024 21:06:26 GMT
• Title: $ρ$-NeRF: Leveraging Attenuation Priors in Neural Radiance Field for 3D Computed Tomography Reconstruction
• Authors: Li Zhou, Changsheng Fang, Bahareh Morovati, Yongtong Liu, Shuo Han, Yongshun Xu, Hengyong Yu,
• Abstract summary: $rho$-NeRF sets a new standard in novel view synthesis (NVS) and computed tomography (CT) reconstruction. The $rho$-NeRF represents a three-dimensional (3D) volume through a fully-connected neural network.
• Score: 4.829520688270679
• License:
• Abstract: This paper introduces $\rho$-NeRF, a self-supervised approach that sets a new standard in novel view synthesis (NVS) and computed tomography (CT) reconstruction by modeling a continuous volumetric radiance field enriched with physics-based attenuation priors. The $\rho$-NeRF represents a three-dimensional (3D) volume through a fully-connected neural network that takes a single continuous four-dimensional (4D) coordinate, spatial location $(x, y, z)$ and an initialized attenuation value ($\rho$), and outputs the attenuation coefficient at that position. By querying these 4D coordinates along X-ray paths, the classic forward projection technique is applied to integrate attenuation data across the 3D space. By matching and refining pre-initialized attenuation values derived from traditional reconstruction algorithms like Feldkamp-Davis-Kress algorithm (FDK) or conjugate gradient least squares (CGLS), the enriched schema delivers superior fidelity in both projection synthesis and image recognition.

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