Low-Dose X-Ray Ct Reconstruction
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Benchmarks
X3D
Most implemented
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Structure-Aware Sparse-View X-ray 3D Reconstruction
TensoRF: Tensorial Radiance Fields
NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction
NeAT: Neural Adaptive Tomography
Papers
Structure-Aware Sparse-View X-ray 3D Reconstruction
X-ray, known for its ability to reveal internal structures of objects, is expected to provide richer information for 3D reconstruction than visible light. Yet, existing neural radiance fields (NeRF) algorithms overlook t…
3D ReconstructionCT ReconstructionLow-Dose X-Ray Ct ReconstructionNeRF+1NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction
This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction (Cone Beam Computed Tomography) that requires no external training data. Specifically, the desired attenuation coefficient…
Low-Dose X-Ray Ct ReconstructionNovel View SynthesisTensoRF: Tensorial Radiance Fields
We present TensoRF, a novel approach to model and reconstruct radiance fields. Unlike NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which represents a 3D voxel grid with per-voxel mul…
Low-Dose X-Ray Ct ReconstructionNeRFNovel View SynthesisNeAT: Neural Adaptive Tomography
In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for multi-view inverse rendering. Through a combination of neural features with an adaptive explicit…
3D ReconstructionInverse RenderingLow-Dose X-Ray Ct ReconstructionNeural RenderingADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography
Advances in multi-spectral detectors are causing a paradigm shift in X-ray Computed Tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the o…
3D ReconstructionComputed Tomography (CT)Image ReconstructionInference Optimization+3IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and Prediction
We propose IntraTomo, a powerful framework that combines the benefits of learning-based and model-based approaches for solving highly ill-posed inverse problems in the Computed Tomography (CT) context. IntraTomo is c…
Computed Tomography (CT)Low-Dose X-Ray Ct ReconstructionNovel View SynthesisSelf-Supervised Learning+1