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CT Reconstruction

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Showing 51–75 of 235 papers

TitleStatusHype
Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models—0
X-Recon: Learning-based Patient-specific High-Resolution CT Reconstruction from Orthogonal X-Ray ImagesCode0
DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays—0
Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model—0
Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT ReconstructionCode2
Total Variation Regularization for Tomographic Reconstruction of Cylindrically Symmetric Objects—0
DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction—0
3D CBCT Challenge 2024: Improved Cone Beam CT Reconstruction using SwinIR-Based Sinogram and Image Enhancement—0
XctDiff: Reconstruction of CT Images with Consistent Anatomical Structures from a Single Radiographic Projection ImageCode1
Encoding Semantic Priors into the Weights of Implicit Neural Representation—0
Learning Image Priors through Patch-based Diffusion Models for Solving Inverse ProblemsCode0
MVMS-RCN: A Dual-Domain Unfolding CT Reconstruction with Multi-sparse-view and Multi-scale Refinement-correctionCode0
Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction—0
MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction—0
Continuous Learned Primal Dual—0
Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction—0
DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction—0
Ray-driven Spectral CT Reconstruction Based on Neural Base-Material Fields—0
EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image ReconstructionCode1
Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction—0
Radiative Gaussian Splatting for Efficient X-ray Novel View SynthesisCode4
QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction—0
Low-Dose CT Reconstruction Using Dataset-free LearningCode0
Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss functionCode0
MIRT: a simultaneous reconstruction and affine motion compensation technique for four dimensional computed tomography (4DCT)—0
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