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Papers
arXiv papers
EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction [paper] [Code]
Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction [paper] [Code]
Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction [paper]
CoCoDiff: A Contextual Conditional Diffusion Model for Low-dose CT Image Denoising [paper]
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup [paper]
SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction [paper] [Code]
Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT [paper]
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography [paper]
Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency [paper]
Learned convex regularizers for inverse problems [paper]
Extreme Few-view CT Reconstruction using Deep Inference [paper]
Statistical Image Reconstruction Using Mixed Poisson-Gaussian Noise Model for X-Ray CT [paper]
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network [paper]
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series [paper]
2023
[PINER] PINER: Prior-informed Implicit Neural Representation Learning for Test-time Adaptation in Sparse-view CT Reconstruction (WACV) [paper] [Code]
[IRDS] Iterative reconstruction of low-dose CT based on differential sparse (Biomedical Signal Processing and Control) [paper]
[DADN] Domain-adaptive denoising network for low-dose CT via noise estimation and transfer learning (Medical physics) [paper]
[SPQI] Structure-preserving quality improvement of cone beam CT images using contrastive learning (Computers in Biology and Medicine) [paper]
[3DIP] Solving 3D Inverse Problems Using Pre-Trained 2D Diffusion Models (CVPR 2023) [paper]
[GGLF] Gradient-based geometry learning for fan-beam CT reconstruction (Physics in Medicine & Biology) [paper]
[DGR] An Unsupervised Reconstruction Method For Low-Dose CT Using Deep Generative Regularization Prior (Biomedical Signal Processing and Control) [paper] [Code]
[MALAR] Multiple Adversarial Learning based Angiography Reconstruction for Ultra-low-dose Contrast Medium CT (JBHI) [paper] [Code]
[DESDGAN] A Dual-Encoder-Single-Decoder Based Low-Dose CT Denoising Network (JBHI) [paper] [Code]
[CCN-CL] CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising (Computers in Biology and Medicine) [paper]
[PDF] CT Reconstruction With PDF: Parameter-Dependent Framework for Data From Multiple Geometries and Dose Levels (TMI) [paper]
[DSigNet] Downsampled imaging geometric modeling for accurate ct reconstruction via deep learning (TMI) [paper] [Code]
[MAGIC] MAGIC: Manifold and graph integrative convolutional network for low-dose CT reconstruction (TMI) [paper]
[CasRedSCAN] Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer (TMI) [paper]
[FDM] Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction (Tomography) [paper] [Code]
[MAP-NN] Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction (Nature Machine Intelligence) [paper]
[DeACNN] Learning a Deep CNN Denoising Approach Using Anatomical Prior Information Implemented With Attention Mechanism for Low-Dose CT Imaging on Clinical Patient Data From Multiple Anatomical Sites (JBHI) [paper]
[DCTR] Dynamic CT Reconstruction From Limited Views With Implicit Neural Representations (ICCV 2021) [paper]
2020
[iRadonMAP] Radon Inversion via Deep Learning (TMI) [paper]
[LRTP] Spectral CT reconstruction via low-rank representation and region-specific texture preserving Markov random field regularization (TMI) [paper]
[FSTensor] FSTensorFull-spectrum-knowledge-aware tensor model for energy-resolved CT iterative reconstruction (TMI) [paper]
[MetaInv-Net] MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction (TMI) [paper] [Code]
[SACNN] SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network (TMI) [paper]
[Momentum-Net] Momentum-Net: Fast and convergent iterative neural network for inverse problems (TPAMI) [paper]
[ETEDN] An End-to-End Deep Network for Reconstructing CT Images Directly From Sparse Sinograms (TCI) [paper] [Code]
[DIP] Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods (Inverse Problems) [paper] [Code]
[DGR] An Unsupervised Reconstruction Method For Low-Dose CT Using Deep Generative Regularization Prior (Biomedical Signal Processing and Control) [paper] [Code]
[HD-CNN] Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging (PMB) [paper]
[DLMIR] Deep learning methods for image reconstruction from angularly sparse data for CT and SAR imaging (ASARI) [paper] [Code]
[DEER] Deep efficient end-to-end reconstruction (DEER) network for few-view breast CT image reconstruction (IEEE Access) [paper] [Code]
[TVWFR] Sparse View CT Image Reconstruction Based on Total Variation and Wavelet Frame Regularization (IEEE Access) [paper]
[DL-PICCS] Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS) (Medical physics) [paper]
[SPSS] Sharpness preserved sinogram synthesis using convolutional neural network for sparse-view CT imaging (Medical Imaging) [paper]
[iCTNet] Sinogram interpolation for sparse-view micro-CT with deep learning neural network (Medical Imaging) [paper] [Code]
[PSRV] Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning (Nature biomedical engineering) [paper]
[SUPER] SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction (ICCVW) [paper]