Low-Dose X-Ray Ct Reconstruction
8 papers with code • 1 benchmarks • 3 datasets
X3D
2DeteCT
A collection of 131 CT datasets of pieces of modeling clay containing stones
Most implemented papers
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
bmild/nerf
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ECCV 2020
Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x, y, z)$ and viewing direction $(\theta, \phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location.
Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally complex because of the repeated use of the forward and backward projection.
TensoRF: Tensorial Radiance Fields
We demonstrate that applying traditional CP decomposition -- that factorizes tensors into rank-one components with compact vectors -- in our framework leads to improvements over vanilla NeRF.
Structure-Aware Sparse-View X-ray 3D Reconstruction
caiyuanhao1998/sax-nerf
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CVPR 2024
In this paper, we propose a framework, Structure-Aware X-ray Neural Radiodensity Fields (SAX-NeRF), for sparse-view X-ray 3D reconstruction.
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and Prediction
After getting estimated through the sinogram prediction module, the density field is consistently refined in the second module using local and non-local geometrical priors.
ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography
However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem.
NeAT: Neural Adaptive Tomography
darglein/NeAT
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Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering methods.
NAF: 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.
