Awesome-CT-Reconstruction

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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]

2022

• [GMM-unNet] Noise Characteristics Modeled Unsupervised Network for Robust CT Image Reconstruction (TMI) [paper]
• [DREAM-Net] DREAM-Net: Deep Residual Error Iterative Minimization Network for Sparse-View CT Reconstruction (TMI) [paper]
• [FONT-SIR] FONT-SIR: Fourth-Order Nonlocal Tensor Decomposition Model for Spectral CT Image Reconstruction (TMI) [paper]
• [DDCL] Dual domain closed-loop learning for sparse-view CT reconstruction (ICIFXCT) [paper]
• [DGR] An Unsupervised Reconstruction Method For Low-Dose CT Using Deep Generative Regularization Prior (Biomedical Signal Processing and Control) [paper] [Code]
• [PLANet] Learning Projection Views for Sparse-View CT Reconstruction (ACM MM) [paper]
• [UNDIP] Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior (CMPB) [paper]
• [ACID-B] Stabilizing deep tomographic reconstruction: Part B. Convergence analysis and adversarial attacks (Patterns) [paper]
• [ACID-A] Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results (Patterns) [paper]
• [DFDLM] A Dataset-free Deep Learning Method for Low-Dose CT Image Reconstruction (Inverse Problems) [paper]
• [EASEL] Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model (TRPMS) [paper] [Code]
• [DuDoTrans] DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction (MLMIR) [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]

2021

• [DRONE] DRONE: Dual-Domain Residual-based Optimization NEtwork for Sparse-View CT Reconstruction (TMI) [paper] [Code]
• [CLEAR] CLEAR: Comprehensive Learning Enabled Adversarial Reconstruction for Subtle Structure Enhanced Low-Dose CT Imaging (TMI) [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]
• [SUPER] Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction (TMI) [paper]
• [Tensor-Net] Learning to Reconstruct CT Images From the VVBP-Tensor (TMI) [paper]
• [FISTA-Net] FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging (TMI) [paper] [Code]
• [IntraTomo] IntraTomo: Self-supervised Learning-based Tomography via Sinogram Synthesis and Prediction (ICCV) [paper] [Code]
• [LMFI] Learnable Multi-scale Fourier Interpolation for Sparse View CT Image Reconstruction (MICCAI) [paper]
• [SinoNet] Noise-Generating-Mechanism-Driven Unsupervised Learning for Low-Dose CT Sinogram Recovery (TRPMS) [paper]
• [N2S] Self-Supervised Training For Low-Dose Ct Reconstruction (ISBI) [paper] [Code]
• [ALPD] Adversarially learned iterative reconstruction for imaging inverse problems (SSVM) [paper] [Code]
• [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]
• [HDNet] Hybrid-domain neural network processing for sparse-view CT reconstruction (TRPMS) [paper]
• [ℓ₀DL] Limited-Angle X-Ray CT Reconstruction Using Image Gradient ℓ₀-Norm With Dictionary Learning (TRPMS) [paper]
• [REDAEP] REDAEP: Robust and Enhanced Denoising Autoencoding Prior for Sparse-View CT Reconstruction (TRPMS) [paper]
• [CADL] Noise and spatial resolution properties of a commercially available deep learning‐based CT reconstruction algorithm (Medical physics) [paper]

2019

• [SISVM] Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions (TMI) [paper]
• [SPULTRA] SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models (TMI) [paper]
• [VVBP-tSVD] VVBP-Tensor in the FBP Algorithm: Its Properties and Application in Low-Dose CT Reconstruction (TMI) [paper]
• [PDE] A regional adaptive variational PDE model for computed tomography image reconstruction (PR) [paper]
• [DNNSS] Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction (TRPMS) [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]

2018

• [LEARN] LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT (TMI) [paper]
• [LPD] Learned Primal-Dual Reconstruction (TMI) [paper]
• [CNNRPGD] CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction (TMI) [paper]
• [RAD] Regularization analysis and design for prior-image-based X-ray CT reconstruction (TMI) [paper]
• [3pADMM] Optimizing a parameterized plug-and-play ADMM for iterative low-dose CT reconstruction (TMI) [paper]
• [DLMI] Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss (TMI) [paper] [Code]
• [DD-Net] A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution (TMI) [paper] [Code]
• [DBSS] Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction (TMI) [paper]
• [TFU-Net] Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT (TMI) [paper]
• [DLCT] Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems (TMI) [paper]
• [PTPN] Intelligent Parameter Tuning in Optimization-Based Iterative CT Reconstruction via Deep Reinforcement Learning (TMI) [paper]
• [DD-Net] A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution (TMI) [paper]
• [NLCTF] Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction (TMI) [paper]
• [WavResNet] Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network (Medical Imaging) [paper]
• [CTNet] Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion (CVPR) [paper]
• [AUTOMAP] Image reconstruction by domain-transform manifold learning (Nature) [paper]
• [DLCT] Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems (TMI) [paper]

2017

• [IMAP-TV] Robust low-dose CT sinogram preprocessing via exploiting noise-generating mechanism (TMI) [paper]
• [KSAE] Iterative Low-dose CT Reconstruction with Priors Trained by Artificial Neural Network (TMI) [paper]
• [RED-CNN] Low-dose CT with a residual encoder-decoder convolutional neural network (TMI) [paper] [Code]
• [DeepCNN] A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction (Medical physics) [paper]
• [SENP] Low‐dose CT reconstruction using spatially encoded nonlocal penalty (Medical physics) [paper]
• [VISS] View-interpolation of sparsely sampled sinogram using convolutional neural network (Medical Imaging) [paper]