Awesome Computed Tomography
经过核验的 CT 重建研究精选列表。每篇论文的链接都经 Crossref、arXiv 或出版方自身记录核验，所有链接每周自动复查。
- 论文
- 678
- 工具包
- 60
- 数据集
- 82
- 基准与挑战赛
- 22
- 学习资源
- 35
- 更新于
- 2026-10-30
默认按最新在前排列，可用“排序”调整。每条条目给出出处与年份；预印本 表示该工作尚未发表于同行评审的会议或期刊，年份取首次公开的时间。鼠标悬停在标题上（手机上轻点标题）可查看简短摘要。
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K-NeAS: Scalable Multi-Material CT Reconstruction Using Neural SDFs
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3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems
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A fast iterative reconstruction method of sparse angle CT for cylindrical lithium battery
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Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction
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AD-GSAnatomical Density-guided 3D Gaussian Splatting for Sparse-View Dental CBCT Reconstruction
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Adaptive Diffusion Models for Sparse-View Motion-Corrected Head Cone-beam CT
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AgenticCTLLM-Orchestrated Diagnose–Plan–Treat for Mixed-Degradation CT Reconstruction
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AllFreqNetA unified frequency-enhanced all-in-one framework for medical image restoration
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AMIFoundAn all-in-one foundation model for multimodal medical image restoration and enhancement
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Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion
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At FullTilt: Real-Time Open-Set 3D Macromolecule Detection Directly from Tilted 2D Projections
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Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction
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Bayesian Joint Reconstruction and Uncertainty Quantification for Limited-Data Multi-Contrast X-Ray CT
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Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction
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CBCT-Based Synthetic CT Generation Using Conditional Flow Matching Model
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CG-GLOREA Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction
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Complex Wavelet-Based Sinogram Segmentation for Metal Artifact Reduction in Cone-Beam CT
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Computed Tomography Reconstruction Algorithm Using Markov Random Field Model
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Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays
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Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
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Cone-beam artifact reduction in Gamma Knife CBCT images using a line-arc-line scan trajectory
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Cone-Beam CT Image Quality Enhancement Using A Latent Diffusion Model Trained with Simulated CBCT Artifacts
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ContiLNNMitigating Slice Sampling Discontinuity with Liquid Neural Networks for Medical Image Restoration
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Continuity-driven Synergistic Diffusion with Neural Priors for Ultra-Sparse-View CBCT Reconstruction
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Convolutional neural network metal artifact reduction in ultra-high resolution photon counting detector CT of middle- and inner-ear implants
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Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT
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Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography
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CryoDeRecA Supervised Multi-task Framework for Joint cryo-ET Restoration Enabled by Generative Physical Simulation
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CT-CLIP/CT-CHATGeneralist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
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CTOResolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT
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CTPretrainDual-Domain Pretraining Foundation Model for CT Image Reconstruction
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Data-driven filter design for flexible and noise-robust tomographic imaging
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DC-PnPDPPlug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
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DDN-FMA deep dictionary network-based foundation model for ultra-low-dose CT denoising
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Deep Pseudo-Proximal Map: A Self-Supervised Data-Fitting Agent for Iterative Reconstruction
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DeepSparseA Foundation Model for Sparse-View CBCT Reconstruction
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Delta-NetDeep Dual-Domain Alternating Optimization Network for High Pitch Helical CT Reconstruction
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DenoMambaA Fused State-Space Model for Low-Dose CT Denoising
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DIAMONDDeep Learning-based Monoenergetic Imaging for Calcified Coronary Stenosis Assessment at Energy-integrating Detector CT
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DiffNAFIterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary Tomography
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DiffNRDiffusion-Enhanced Neural Representation Optimization for Sparse-View 3D Tomographic Reconstruction
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Diffusion model-regularized implicit neural representation for computed tomography metal artifact reduction
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DINRRegularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction of Neutron Computed Tomography Data
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DM4CTBenchmarking Diffusion Models for CT Reconstruction
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Dose-Aware Cold Diffusion with Physics Consistency for Generalizable Low-Dose CT Reconstruction
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Dual-domain U-Nets with embedded back projection operators for motion-resolved 4D CBCT reconstruction
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DuDoSwinSelf-supervised Dual-domain Swin Transformer for Sparse-view CT Reconstruction
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DVG-DiffusionDual-View Guided Diffusion Model for CT Reconstruction from X-Rays
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Dynamic Black-hole Emission Tomography with Physics-Informed Neural Fields
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EDAElucidating the Design Space of Arbitrary-Noise-Based Diffusion Models
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Efficient Unrolled Networks for Large-Scale 3D Inverse Problems
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EFMCTEfficient Flow Matching for Sparse-View CT Reconstruction
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Equivariance2InverseA Practical Self-Supervised CT Reconstruction Method Benchmarked on Real, Limited-Angle, and Blurred Data
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Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT
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Event-Based Sparse-View Background-Oriented Schlieren Tomography
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Exact-GSMathematically Rigorous and Accurate 3D Gaussian Splatting for 3D X-Ray Reconstruction
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FaCT-GSFast and Scalable CT Reconstruction with Gaussian Splatting
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FDA-ReconFaithful and Data-Adaptive Reconstruction for Limited-Angle CBCT
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FID-Netan interpretable fourier and image domain convolutional dictionary network for industrial CT metal artifact reduction
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Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based Refinement
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Filter2Noisea framework for interpretable and zero-shot low-dose CT image denoising
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FORCETomographic Foundation Model—FORCE: Flow-Oriented Reconstruction Conditioning Engine
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Foundation-VAEFoundation VAEs for 3D CT Reconstruction, Augmentation, and Generation
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FoundDiffFoundational Diffusion Model for Generalizable Low-Dose CT Denoising
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From Sparse X-rays to 3D CT: Training-Free Reconstruction with Diffusion Priors
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Gaussian Material Fields for Volumetric Multi-Energy CT Decomposition
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Geometry-Aware Diffusion Approximate Posterior Sampling for Sparse-View and Limited-Angle CT
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GH-NAFGrid-Adaptive Hash-Level-Attended Neural Attenuation Fields for Discrepancy-Aware CBCT
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GR-GaussianGraph-Based Radiative Gaussian Splatting for Sparse-View Tomography
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Gradient-Based Trajectory Optimisation over Continuous Poses for Sparse-View Cone-Beam CT
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H3D-MarNetWavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows
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HARU-NetHybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography
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HeadCT-FM3D foundation model for generalizable disease detection in head computed tomography
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High-Fidelity 3D Tooth Reconstruction by Fusing Intraoral Scans and CBCT Data via a Deep Implicit Representation
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High-Precision Image Reconstruction for Binary-Valued X-Ray CT From Limited-View Projections
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HorusEyeA self-supervised foundation model for generalizable X-ray tomography restoration
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HounsWorldA Multimodal World Model for Hidden Patient-State Readout, Reconstruction, and Simulation
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HR-CNNPushing the limits of spatial resolution in clinical PCD-CT using a dedicated high-resolution convolutional neural network (HR-CNN)
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Human-Guided Shade Artifact Suppression in CBCT-to-MDCT Translation via Schrödinger Bridge with Conditional Diffusion
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ILVIterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction
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Integration of Spectral CT with PET and SPECT: Bringing Tissue Composition Information to Molecular Imaging
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ISCSImproving 2D Diffusion Models for 3D Medical Imaging with Inter-Slice Consistent Stochasticity
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Joint-decoupled iterative CBCT reconstruction with hybrid scatter estimation and voxel-adaptive beam hardening correction
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KLIPLocalized Distribution Shift Detection via KL-Divergence with Diffusion Priors in Inverse Problems
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LamiGaussPitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction
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Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction
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Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction
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LINGOLatent Initialization and Gradient Optimization for Sparse-view X-ray Novel View Synthesis and CT Reconstruction with 3D Gaussian Splatting
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Low-Level Dataset Distillation for Medical Image Enhancement
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LUCIDLearned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction
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Mamba-MoEDeterministic Intermediate Expert Isolation With a Shared Reconstruction Head for All-in-One Medical Image Restoration
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MARRDecoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration
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MedGMAEGaussian Masked Autoencoders for Medical Volumetric Representation Learning
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MerlinA Computed Tomography Vision-Language Foundation Model and Dataset
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MGMARMetal-Guided Metal Artifact Reduction for X-ray Computed Tomography
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MInDI-3DIterative Deep Learning in 3D for Sparse-view Cone Beam Computed Tomography
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Model-Based Iterative Reconstruction with View-Dependent Detector Displacements for Cone-Beam CT
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Model-based motion simulation framework for deep learning-assisted motion correction in dynamic wrist CT
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MVSS-PCRA Multiview Subsampling Approach With Prior-Constrained Reconstruction for Self-Supervised CT Denoising
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NABNeural Adaptive Binning for Sparse-View CT Reconstruction
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NAB-GSZero-shot CT Super-Resolution using Diffusion-based 2D Projection Priors and Signed 3D Gaussians
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Neuromorphic X-Ray Computed Tomography
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NGPSStructure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling
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Noise2MatchSelf-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching
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Non-circular scan trajectories for reducing cone-beam artifacts in Gamma Knife CBCT images: a simulation study
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Nonperiodic dynamic CT reconstruction using backward-warping INR with regularization of diffeomorphism (BIRD)
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OpenProsA Large-Scale Dataset for Limited View Prostate Ultrasound Computed Tomography
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OX-NeRF3D X-ray Tomography Reconstruction from Sparse Views Using Implicit Neural Representation
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PDuMSRNetPrompt Guiding Multi-Scale Adaptive Sparse Representation-driven Network for Low-Dose CT MAR
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PhyDiCTPlug-and-Play CT Reconstruction from Sparse X-Rays via Differentiable Rendering and Strong Priors
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ProCTUniversal Pre-training for Generalizable Incomplete-View CT Reconstruction
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Projection-Volume Fidelity Divergence: Diagnosing and Controlling Optimization Drift in Sparse-View 3D Gaussian Tomography
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Radioactive 3D Gaussian Ray Tracing for Tomographic Reconstruction
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Radon Transforms of Projection Functions and Aleksandrov-Fenchel Inequalities
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RAMReconstruct Anything Model: A Lightweight General Model for Computational Imaging
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RCL-MambaA Dual-domain State Space Model for Measurement-oriented Image Restoration in Rotational Sparse-View Scanning Computed Laminography
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RDDMA Residual-Driven Drifting Model for High-Fidelity Low-Dose CT Denoising
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ReMAR-DSRecalibrated Feature Learning for Metal Artifact Reduction and CT Domain Transformation
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Restore-RWKVEfficient and Effective Medical Image Restoration with RWKV
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Revisiting Pose Sensitivity in Splat-based Computed Tomography under Sparse-view Reconstruction
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RinerUnsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT
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Robustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT under Challenging Acquisition Settings
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Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstruction
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Spectral Consistent Flow for One-Step 3D Medical Image Translation
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Spectral-XLatent prior enhanced spectral CT restoration with mamba-assisted X-net
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Splat-Based Metal Artifact Reduction in Cone-Beam CT via Compact Attenuation Modeling
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Splat-based Metal Artifact Reduction in Cone-Beam CT via Polychromatic Modeling
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STCNetDual-Domain Sparse-View 4D CBCT Reconstruction with Spatial-Temporal Consistency Learning
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TAMPImaging foundation model for universal enhancement of non-ideal measurement CT
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TANZANITECT data harmonization via learned virtual monoenergetic imaging for cross-kV scan translation and radiomics reproducibility
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Texture-preserving implicit neural representation for cone-beam CT truncated reconstruction
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TG-FieldGeometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction
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Tomographic Sparse View Selection Using the View Covariance Loss
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TomoTransformerTowards a Foundation Model for CT Reconstruction
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Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model
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Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation
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Towards reconstructing experimental sparse-view X-ray CT data with diffusion models
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TPAw-LAwTVTruncated projection adaptive weighting combined with adaptive TV for artifact reduction in linear computed laminography
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Truncation artifact reduction in cone-beam computed tomography scans using conditional generative diffusion models
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UniH3UniH³: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
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UniMedIRAll-In-One Medical Image Restoration via Text-Guided Prompting and Dual-Domain Modeling
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Unsupervised Metal Artifact Reduction in Dental CBCT using Fine-tuned Cycle-Consistent Adversarial Networks
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Variational Garrote for Sparse Inverse Problems
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VFMINRSparse-View CT Reconstruction via Implicit Neural Representation Learning Powered by Dual-Domain Vision Foundation Models
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VoDaSuReA Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution
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VoxelSynth3DInterpretable Volumetric Image-Domain Metal Artifact Reduction with a Paired Synthetic CLINIC-Metal Benchmark
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Wavelet-Consistent Diffusion Posterior Sampling for Limited-Angle and Sparse CT
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X-SplatUnified Gaussian Splatting for Joint RGB and X-ray 3D Reconstruction
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XDen-1KA Density Field Dataset of Real-World Objects
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3D CBCT Artefact Removal Using Perpendicular Score-Based Diffusion Models
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4DGS-4DCBCTDynamic Cone Beam CT Reconstruction via Spatiotemporal Gaussian Neural Representation
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A CT Geometry With Multiple Centers Of Rotation For Solving Sparse View Problem
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A Learnt Half-Quadratic Splitting-Based Algorithm for Fast and High-Quality Industrial Cone-beam CT Reconstruction
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AAPM CT metal artifact reduction grand challenge
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Accelerated Optimization of Implicit Neural Representations for CT Reconstruction
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Advancing Limited-Angle CT Reconstruction Through Diffusion-Based Sinogram Completion
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An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of View
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An unsupervised sparse-view CT reconstruction framework using combination of iterative deep image prior and ADMM
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Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?
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CALIMAR-GANAn unpaired mask-guided attention network for metal artifact reduction in CT scans
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CoCo-PnPLearning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Inverse Problems
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Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components
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Coordinate-Based Speed of Sound Recovery for Aberration-Corrected Photoacoustic Computed Tomography
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CT-FMVision Foundation Models for Computed Tomography
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CvG-DiffCross-View Generalized Diffusion Model for Sparse-View CT Reconstruction
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DaPTDegradation-Aware Prompted Transformer for Unified Medical Image Restoration
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Data-Efficient Limited-Angle CT Using Deep Priors and Regularization
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DDSwinIRSwinIR-based Dual-Domain Reconstruction for Sparse-View Computed Tomography
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Deep learning CT image restoration using system blur and noise models
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DGRDiscretized Gaussian Representation for Tomographic Reconstruction
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DiffCodeAll-in-One Medical Image Restoration with Latent Diffusion-Enhanced Vector-Quantized Codebook Prior
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Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
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Diffusion-Based Limited-Angle CT Reconstruction under Noisy Conditions
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DIGSDynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model
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DINRDistributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction
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DPERDisentangled deep learning method for interior tomographic reconstruction of low-dose x-ray CT
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DPKADual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention
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EAGLEAn Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction
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Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration
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Epi-NAFEnhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar Consistency
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Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT: From Simulated to Real Data
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FedM2CTFederated Metadata-Constrained iRadonMAP Framework with Mutual Learning for All-in-One Computed Tomography Imaging
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FIND-Net -- Fourier-Integrated Network with Dictionary Kernels for Metal Artifact Reduction
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Flex-DDDeep denoising model with flexible priors for sparse-view CT reconstruction
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GANeXtA Fully ConvNeXt-Enhanced Generative Adversarial Network for MRI- and CBCT-to-CT Synthesis
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GB-SVFBPGaussian-Based Shift-Variant FBP neural network
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GDP-NetGlobal Dependency-Enhanced Dual-Domain Parallel Network for Ring Artifact Removal
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Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction
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GLFCUnified Global-Local Feature and Contrast Learning with Mamba-Enhanced UNet for Synthetic CT Generation from CBCT
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GLIMPSEGeneralized Locality for Scalable and Robust CT
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ILUMENATELearned high resolution energy-integrating detector CT angiography: Harnessing the power of ultra-high-resolution photon counting detector CT
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InsideOutIntegrated RGB-Radiative Gaussian Splatting for Comprehensive 3D Object Representation
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Investigating the effects of machine learning generalization for enhancing accuracy in fast X-ray computed tomography for industrial metrology
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Joint CT reconstruction of anatomy and implants using a mixed prior model
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KMAR-NetDeep learning-based metal artifact reduction in CT for total knee arthroplasty
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LangMambaA Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
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LARGE-DiffNetCone-Beam Computed Tomography Limited-Angle Image Reconstruction Using Planning CT-Guided Deep Diffusion Model
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Latent Space Consistency for Sparse-View CT Reconstruction
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LCTfoundA lung CT vision foundation model facilitating disease diagnosis and medical imaging
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Learned denoising with simulated and experimental low-dose CT data
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Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction
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Learning Wavelet-Sparse FDK for 3D Cone-Beam CT Reconstruction
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Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models
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Limited-angle SPECT image reconstruction using deep image prior
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Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine
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LWCDNetAn Interpretable Learning Weighted Convolutional Dictionary Network for Metal Artifact Reduction in CT Images
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MAISIMedical AI for Synthetic Imaging
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MedVAEEfficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders
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Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-View CT Reconstruction
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Modeling X-Ray Photon Pile-Up with a Normalizing Flow
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MONSTRModel-Oriented Neutron Strain Tomographic Reconstruction
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Motion-GradA Gradient-Based Approach to Fast and Accurate Head Motion Compensation in Cone-Beam CT
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Multi-stage Deep Learning Artifact Reduction for Parallel-beam CT
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Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation
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Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
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Noisier2InverseSelf-Supervised Learning for Image Reconstruction With Correlated Noise
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Ordered-subsets Multi-diffusion Model for Sparse-view CT Reconstruction
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PALETTEPhysics-informed model-based generative neural network for synthesizing scanner- and algorithm-specific low-dose CT exams
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Physics-Inspired Gaussian Kolmogorov-Arnold Networks for X-ray Scatter Correction in Cone-Beam CT
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PRAISE-NetDeep Projection-Domain Data-Consistent Learning Network for CBCT Metal Artifact Reduction
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Preserving noise texture through training data curation for deep learning denoising of high-resolution cardiac EID-CT
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Prior-Adapted Progressive Time-Resolved CBCT Reconstruction Using a Dynamic Reconstruction and Motion Estimation Method
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Projection Embedded Diffusion Bridge for CT Reconstruction from Incomplete Data
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PWLS-SOMalternative PWLS reconstruction for limited-view CT by strategic optimization of a deep learning model
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Recover Biological Structure from Sparse-View Diffraction Images with Neural Volumetric Prior
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SDBSystem-Embedded Diffusion Bridge Models
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SDFA self-supervised denoiser framework for X-Ray computed tomography
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Semantic contrastive learning for orthogonal X-ray computed tomography reconstruction
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SIRE-NetStructure-Integrated Refinement and Enhancement Network for Limited-Angle CT Reconstruction
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Sparse view tomographic reconstruction of elongated objects using learned primal-dual networks
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Spectral DPSMulti-Material Decomposition Using Spectral Diffusion Posterior Sampling
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SpenerUnsupervised Self-Prior Embedding Neural Representation for Sparse-View CT Reconstruction
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Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction
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Swap-NetA Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction to ICF Applications
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Synthetic CT image generation from CBCT: A Systematic Review
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TAP-CT3D Task-Agnostic Pretraining of Computed Tomography Foundation Models
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TATTask-Adaptive Transformer for All-in-One Medical Image Restoration
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Time-resolved dynamic CBCT reconstruction using prior-model-free spatiotemporal Gaussian representation (PMF-STGR)
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TMGANTexture Matching GAN for CT Image Enhancement
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TomoSelfDEQSelf-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction
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TUFTowards Multi-scenario Generalization: Text-Guided Unified Framework for Low-Dose CT and Total-Body PET Reconstruction
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Ultra-High-Resolution Photon-Counting-Detector CT with a Dedicated Denoising Convolutional Neural Network for Enhanced Temporal Bone Imaging
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Ultrafast Deep Learning-Based Scatter Estimation in Cone-Beam Computed Tomography
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UniSinoPhysics-Driven Foundational Model for Universal CT Sinogram Standardization
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VOI-NetDeep learning-based volume of interest imaging in helical CT for image quality improvement and radiation dose reduction
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Vol3DGSVolumetrically Consistent 3D Gaussian Rasterization
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When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT
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X-FieldA Physically Grounded Representation for 3D X-ray Reconstruction
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X-GRMLarge Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography
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X-ray computed laminography: A brief review of mechanisms, reconstruction, applications and perspectives
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X2-Gaussian4D Radiative Gaussian Splatting for Continuous-Time Tomographic Reconstruction
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XGenReconA New Perspective in Ultrasparse Volumetric CBCT Reconstruction Through Geometry-Controlled X-Ray Projection Generation
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Zero-Shot Low-dose CT Denoising via Sinogram Flicking
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3D Helical CT Reconstruction With a Memory Efficient Learned Primal-Dual Architecture
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A Review of Deep Learning CT Reconstruction From Incomplete Projection Data
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AMIRAll-In-One Medical Image Restoration via Task-Adaptive Routing
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Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review
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ATOMMotion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks
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C2RVCross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction
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Cas-PREDA Limited-Angle Cone Beam CT Reconstruction Research Based on Prior Information
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Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography
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CoreDiffContextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
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Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series
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DCDL-GSDeep convolutional dictionary learning network for sparse view CT reconstruction with a group sparse prior
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DDDMA Dual-Domain Diffusion Model for Sparse-View CT Reconstruction
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DDSDecomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
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Deep-InteriorA new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning
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Deep-silicon photon-counting x-ray projection denoising through reinforcement learning
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DIF-GaussianLearning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction
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DiffReconDiffusion Models for Medical Image Reconstruction
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DiffusionBlendLearning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
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DiffuX2CTDiffusion Learning to Reconstruct CT Images from Biplanar X-Rays
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DPSCT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model
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DuDoDp-MARUnsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains
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GenerateCTText-Conditional Generation of 3D Chest CT Volumes
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Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
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How auto-differentiation can improve CT workflows: classical algorithms in a modern framework
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INCODEImplicit Neural Conditioning with Prior Knowledge Embeddings
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INR-JointImplicit Neural Representations for Robust Joint Sparse-View CT Reconstruction
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LEDALow-dose CT Denoising with Language-engaged Dual-space Alignment
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Machine learning based sinogram interpolation for X-ray computed tomography validated on experimental data
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Machine learning in industrial X-ray computed tomography - a review
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Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework
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MJCNetCoarse-to-Fine Learning for Planning CT-Enhanced Limited-Angle CBCT Reconstruction
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MsDu-NetsMultistage Dual-Domain Networks With Informative Prior and Self-Augmentation for Dual-Energy Limited-Angle CT Reconstruction
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NeRPImplicit Neural Representation Learning With Prior Embedding for Sparsely Sampled Image Reconstruction
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Neural Field Regularization by Denoising for 3D Sparse-View X-Ray Computed Tomography
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PKAID-NetImproved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network
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Plug-and-Play Learned Proximal Trajectory for 3D Sparse-View X-Ray Computed Tomography
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PND-NetPhysics-Inspired Non-Local Dual-Domain Network for Metal Artifact Reduction
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Quad-NetQuad-Domain Network for CT Metal Artifact Reduction
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R2-GaussianRectifying Radiative Gaussian Splatting for Tomographic Reconstruction
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RBP-DIPResidual back projection with deep image prior for ill-posed CT reconstruction
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RegFormerA Local–Nonlocal Regularization-Based Model for Sparse-View CT Reconstruction
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Report on the AAPM deep-learning spectral CT Grand Challenge
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SAX-NeRFStructure-Aware Sparse-View X-Ray 3D Reconstruction
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Scatter-DLScatter Correction Using Deep Learning for Cone-Beam Computed Tomography
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SOUL-NetA Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction
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SWORDStage-by-Stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
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Two-View Industrial CT Reconstruction Based on a Multiscale Conditional Latent Diffusion Network
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Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization
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U-SwinHybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography
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Wavelet-Improved Score-Based Generative Model for Medical Imaging
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X-GaussianRadiative Gaussian Splatting for Efficient X-Ray Novel View Synthesis
-
ZIRCONMultiple Kernel Synthesis of Head CT Using a Task-Based Loss Function
-
3DIPSolving 3D Inverse Problems Using Pre-Trained 2D Diffusion Models
-
ASCONAnatomy-Aware Supervised Contrastive Learning Framework for Low-Dose CT Denoising
-
CROSSCross-Domain Residual-Optimization-Based Structure Strengthening Reconstruction for Limited-Angle CT
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CTformerConvolution-Free Token2Token Dilated Vision Transformer for Low-Dose CT Denoising
-
DADNDomain-adaptive denoising network for low-dose CT via noise estimation and transfer learning
-
DEARDeep Embedding-Attention-Refinement for Sparse-View CT Reconstruction
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Deep-En-ChromaA quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans
-
DIF-NetLearning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction
-
DiffMedDiffusion Models in Medical Imaging: A Comprehensive Survey
-
DiFiR-CTDistance field representation to resolve motion artifacts in computed tomography
-
DL-ReconCombining Physics-Based Models with Deep Learning Image Synthesis and Uncertainty in Intraoperative Cone-Beam CT of the Brain
-
DolceA Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
-
DPSDiffusion Posterior Sampling for General Noisy Inverse Problems
-
DREAM-NetDeep Residual Error Iterative Minimization Network for Sparse-View CT Reconstruction
-
Fast algorithms based on Empirical Interpolation Methods for selecting best projections in Sparse-View X-ray Computed Tomography using a priori information
-
FreeSeedFrequency-Band-Aware and Self-Guided Network for Sparse-View CT Reconstruction
-
GGLFGradient-based geometry learning for fan-beam CT reconstruction
-
GloReDiLearning to Distill Global Representation for Sparse-View CT
-
InDuDoNet+A deep unfolding dual domain network for metal artifact reduction in CT images
-
IRDSIterative reconstruction of low-dose CT based on differential sparse
-
LAMALearned Alternating Minimization Algorithm for Dual-Domain Sparse-View CT Reconstruction
-
LEARN++Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
-
MALARMultiple Adversarial Learning based Angiography Reconstruction for Ultra-low-dose Contrast Medium CT
-
MDSTMulti-domain sparse-view CT reconstruction based on convolution and Swin Transformer
-
MEPNetA Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images
-
Momentum-NetFast and convergent iterative neural network for inverse problems
-
Multislice input for 2D and 3D residual convolutional neural network noise reduction in CT
-
Pie-NetPrior-information-enabled deep learning noise reduction for coronary CT angiography acquired with a photon counting detector CT
-
PINERPrior-Informed Implicit Neural Representation Learning for Test-Time Adaptation in Sparse-View CT Reconstruction
-
PolynerUnsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
-
SCOPESelf-Supervised Coordinate Projection Network for Sparse-View Computed Tomography
-
SEA-NetStructure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data
-
SemiMARSemi-Supervised Learning for CT Metal Artifact Reduction
-
Sparse-View Cone Beam CT Reconstruction Using Data-Consistent Supervised and Adversarial Learning From Scarce Training Data
-
SPEAR-NetSelf-Prior Enhanced Artifact Removal Network for Limited-Angle DECT
-
SPIE-DIRSelf-Prior Information Enhanced Deep Iterative Reconstruction Using Two Complementary Limited-Angle Scans for DECT
-
SPQIStructure-preserving quality improvement of cone beam CT images using contrastive learning
-
SR4ZCTSelf-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap
-
ST_CNNSelf-trained deep convolutional neural network for noise reduction in CT
-
STINRDynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)
-
Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT
-
Technical note: Phantom-based training framework for convolutional neural network CT noise reduction
-
The realisation of fast X-ray computed tomography using a limited number of projection images for dimensional metrology
-
TIME-NetTransformer-Integrated Multi-Encoder Network for limited-angle artifact removal in dual-energy CBCT
-
UDAMARDeep-Learning-Based Metal Artefact Reduction With Unsupervised Domain Adaptation Regularization for Practical CT Images
-
UncertaINRUncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography
-
ACDNetAdaptive Convolutional Dictionary Network for CT Metal Artifact Reduction
-
ACID-AStabilizing deep tomographic reconstruction - Part A. Hybrid framework and experimental results
-
ACID-BStabilizing deep tomographic reconstruction - Part B. Convergence analysis and adversarial attacks
-
AGATEDeep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT
-
An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction
-
An Unsupervised Reconstruction Method For Low-Dose CT Using Deep Generative Regularization Prior
-
Ca-ResUnetLow-dose CT imaging via cascaded ResUnet with spectrum loss
-
CCN-CLA content-noise complementary network with contrastive learning for low-dose computed tomography denoising
-
CoCoDiffA Contextual Conditional Diffusion Model for Low-dose CT Image Denoising
-
DDCLDual domain closed-loop learning for sparse-view CT reconstruction
-
Dedicated convolutional neural network for noise reduction in ultra-high-resolution photon-counting detector computed tomography
-
Deep Learning-Based Dictionary Learning and Tomographic Image Reconstruction
-
Deep learning-based extended field of view computed tomography image reconstruction: influence of network design on image estimation outside the scan field of view
-
Deep microlocal reconstruction for limited-angle tomography
-
DESDGANA Dual-Encoder-Single-Decoder Based Low-Dose CT Denoising Network
-
DFDLMA Dataset-free Deep Learning Method for Low-Dose CT Image Reconstruction
-
DICDNetDeep Interpretable Convolutional Dictionary Network for Metal Artifact Reduction in CT Images
-
DIORDeep Iterative Optimization-Based Residual-Learning for Limited-Angle CT Reconstruction
-
DIRECT-NetA unified mutual-domain material decomposition network for quantitative dual-energy CT imaging
-
DL-VIFReference-Free Learning-Based Similarity Metric for Motion Compensation in Cone-Beam CT
-
DU-GANGenerative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising
-
Dual-AGNetDual-domain attention-guided convolutional neural network for low-dose cone-beam computed tomography reconstruction
-
DuDoDR-NetDual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography
-
DuDoTransDual-Domain Transformer for Sparse-View CT Reconstruction
-
DuDoUFNetDual-domain under-to-fully-complete progressive restoration network for simultaneous metal artifact reduction and low-dose CT reconstruction
-
EASELIterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model
-
End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT
-
FONT-SIRFourth-Order Nonlocal Tensor Decomposition Model for Spectral CT Image Reconstruction
-
GMM-unNetNoise Characteristics Modeled Unsupervised Network for Robust CT Image Reconstruction
-
IDOL-NetAn Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
-
Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior
-
LDM-DNLow-Dimensional Manifold-Constrained Disentanglement Network for Metal Artifact Reduction
-
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup
-
MCGImproving Diffusion Models for Inverse Problems Using Manifold Constraints
-
MedNeRFMedical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray
-
MSM/TomoTxMasked Sinogram Model with Transformer for ill-Posed Computed Tomography Reconstruction: a Preliminary Study
-
NAFNeural Attenuation Fields for Sparse-View CBCT Reconstruction
-
NeATNeural Adaptive Tomography
-
OSCNetOrientation-Shared Convolution Representation for CT Metal Artifact Learning
-
Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction
-
Performance of a deep learning-based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness
-
Photon-Counting CT Reconstruction With a Learned Forward Operator
-
PLANetLearning Projection Views for Sparse-View CT Reconstruction
-
PRIORPrior-Regularized Iterative Optimization Reconstruction For 4D CBCT
-
Report on the AAPM deep-learning sparse-view CT grand challenge
-
Review of high energy x-ray computed tomography for non-destructive dimensional metrology of large metallic advanced manufactured components
-
Sam's NetA Self-Augmented Multistage Deep-Learning Network for End-to-End Reconstruction of Limited Angle CT
-
ScoutCT-NETReconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning
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SinoNetNoise-Generating-Mechanism-Driven Unsupervised Learning for Low-Dose CT Sinogram Recovery
-
SNAFSparse-View CBCT Reconstruction with Neural Attenuation Fields
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Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
-
Trainable joint bilateral filters for enhanced prediction stability in low-dose CT
-
Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography
-
UNDIPSparse-view and limited-angle CT reconstruction with untrained networks and deep image prior
-
AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising
-
ALPDAdversarially learned iterative reconstruction for imaging inverse problems
-
CasRedSCANLimited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer
-
CD-NetComprehensive Domain Network With Spectral Complementary for DECT Sparse-View Reconstruction
-
CLEARComprehensive Learning Enabled Adversarial Reconstruction for Subtle Structure Enhanced Low-Dose CT Imaging
-
CoILCoordinate-Based Internal Learning for Tomographic Imaging
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Cycle-Free CycleGAN Using Invertible Generator for Unsupervised Low-Dose CT Denoising
-
CycleGAN denoising of extreme low-dose cardiac CT using wavelet-assisted noise disentanglement
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DAN-NetDual-Domain Adaptive-Scaling Non-Local Network for CT Metal Artifact Reduction
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DCTRDynamic CT Reconstruction From Limited Views With Implicit Neural Representations
-
DeACNNLearning 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
-
Deep Sinogram Completion With Image Prior for Metal Artifact Reduction in CT Images
-
DL-PICCSAccurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS)
-
Do CNNs Solve the CT Inverse Problem?
-
DRONEDual-Domain Residual-Based Optimization Network for Sparse-View CT Reconstruction
-
DSigNetDownsampled Imaging Geometric Modeling for Accurate CT Reconstruction via Deep Learning
-
Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction
-
FDMDegradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction
-
FISTA-NetLearning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging
-
HDNetHybrid-domain neural network processing for sparse-view CT reconstruction
-
High Pitch Helical CT Reconstruction
-
Incept-netDeep-learning-based direct synthesis of low-energy virtual monoenergetic images with multi-energy CT
-
InDuDoNetAn Interpretable Dual Domain Network for CT Metal Artifact Reduction
-
IntraTomoSelf-supervised Learning-based Tomography via Sinogram Synthesis and Prediction
-
Limited-angle computed tomography with deep image and physics priors
-
Limited-Angle CT Reconstruction via the L1/L2 Minimization
-
LMFILearnable Multi-scale Fourier Interpolation for Sparse View CT Image Reconstruction
-
LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction
-
Low-dose CT image and projection dataset
-
MAGICManifold and graph integrative convolutional network for low-dose CT reconstruction
-
Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach
-
MetaInv-NetMeta Inversion Network for Sparse View CT Image Reconstruction
-
Metal artifact reduction in 2D CT images with self-supervised cross-domain learning
-
Moreau-envelope-enhanced nonlocal shearlet transform and total variation for sparse-view CT reconstruction
-
Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction
-
N2SSelf-Supervised Training For Low-Dose CT Reconstruction
-
PDFCT Reconstruction With PDF: Parameter-Dependent Framework for Data From Multiple Geometries and Dose Levels
-
Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications
-
REDAEPRobust and Enhanced Denoising Autoencoding Prior for Sparse-View CT Reconstruction
-
SPECIALSingle-Shot Projection Error Correction Integrated Adversarial Learning for Limited-Angle CT
-
SUPERUnified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
-
Tensor-NetLearning to Reconstruct CT Images From the VVBP-Tensor
-
To recurse or not to recurse: a low-dose CT study
-
TOD-NetTask-Oriented Low-Dose CT Image Denoising
-
TransCTDual-Path Transformer for Low Dose Computed Tomography
-
UFP-NetDeep learning enabled ultra-fast-pitch acquisition in clinical X-ray computed tomography
-
ULTRADeep learning based spectral CT imaging
-
Unsupervised CT Metal Artifact Learning Using Attention-Guided β-CycleGAN
-
ℓ₀DLLimited-Angle X-Ray CT Reconstruction Using Image Gradient ℓ₀-Norm With Dictionary Learning
-
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network
-
A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT
-
A review on deep learning in medical image reconstruction
-
ADNArtifact Disentanglement Network for Unsupervised Metal Artifact Reduction
-
AirNetFused analytical and iterative reconstruction with deep neural network regularization for sparse-data CT
-
CADLNoise and spatial resolution properties of a commercially available deep learning‐based CT reconstruction algorithm
-
DECT-MULTRADual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
-
Deep learning based image reconstruction algorithm for limited-angle translational computed tomography
-
Deep-MARFast Enhanced CT Metal Artifact Reduction Using Data Domain Deep Learning
-
DEERDeep efficient end-to-end reconstruction (DEER) network for few-view breast CT image reconstruction
-
Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal
-
DIPComputed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods
-
DL-CT-RevDeep Learning for Tomographic Image Reconstruction
-
DL-InvProbDeep Learning Techniques for Inverse Problems in Imaging
-
DLMIRDeep learning methods for image reconstruction from angularly sparse data for CT and SAR imaging
-
EDCNNEdge Enhancement-Based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
-
ETEDNAn End-to-End Deep Network for Reconstructing CT Images Directly From Sparse Sinograms
-
FSTensorFull-spectrum-knowledge-aware tensor model for energy-resolved CT iterative reconstruction
-
GAN-CIRCLECT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)
-
HD-CNNArtifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging
-
Incept-netDeep-learning-based direct inversion for material decomposition
-
iRadonMAPRadon Inversion via Deep Learning
-
Known Operator Learning Enables Constrained Projection Geometry Conversion: Parallel to Cone-Beam for Hybrid MR/X-Ray Imaging
-
Learned convex regularizers for inverse problems
-
Linogram-NetFourier Properties of Symmetric-Geometry Computed Tomography and Its Linogram Reconstruction With Neural Network
-
LRTPSpectral CT reconstruction via low-rank representation and region-specific texture preserving Markov random field regularization
-
MBDLA Model-Based Unsupervised Deep Learning Method for Low-Dose CT Reconstruction
-
Multi-Scale Learned Iterative Reconstruction
-
Noise2InverseSelf-Supervised Deep Convolutional Denoising for Tomography
-
SACNNSelf-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network
-
SISTERSpectral-Image Similarity-Based Tensor With Enhanced-Sparsity Reconstruction for Sparse-View Multi-Energy CT
-
Sparse-view CT reconstruction based on multi-level wavelet convolution neural network
-
Sparsity2DLImage Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning
-
SPULTRALow-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models
-
Synthesizing images from multiple kernels using a deep convolutional neural network
-
TomoGANLow-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks
-
TVWFRSparse View CT Image Reconstruction Based on Total Variation and Wavelet Frame Regularization
-
Virtual Monoenergetic CT Imaging via Deep Learning
-
VVBP-tSVDVVBP-Tensor in the FBP Algorithm: Its Properties and Application in Low-Dose CT Reconstruction
-
WeaveNetInterweaving Network: A Novel Monochromatic Image Synthesis Method for a Photon-Counting Detector CT System
-
3pADMMOptimizing a parameterized plug-and-play ADMM for iterative low-dose CT reconstruction
-
A deep learning reconstruction framework for X-ray computed tomography with incomplete data
-
A dual-stream deep convolutional network for reducing metal streak artifacts in CT images
-
ADMM-based deep reconstruction for limited-angle CT
-
BCD-Netfor Low-Dose CT Reconstruction: Acceleration, Convergence, and Generalization
-
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
-
CT-EvolutionThe Evolution of Image Reconstruction for CT: From Filtered Back Projection to Artificial Intelligence
-
Cycle-Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography
-
DataDrivenSolving Inverse Problems Using Data-Driven Models
-
DIREDeep iterative reconstruction estimation (DIRE): approximate iterative reconstruction estimation for low dose CT imaging
-
DNNSSDeep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction
-
DP-ResNetDomain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging
-
DuDoNetDual Domain Network for CT Metal Artifact Reduction
-
Extreme Few-view CT Reconstruction using Deep Inference
-
iCTNetSinogram interpolation for sparse-view micro-CT with deep learning neural network
-
Laminography in the lab: imaging planar objects using a conventional x-ray CT scanner
-
Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography
-
MAP-NNCompetitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
-
Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning
-
MoDLModel-Based Deep Learning Architecture for Inverse Problems
-
NLCTFNon-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction
-
On-the-Fly Machine Learning for Improving Image Resolution in Tomography
-
One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection
-
PDEA regional adaptive variational PDE model for computed tomography image reconstruction
-
PSRVPatient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning
-
Real-Time Scatter Estimation for Medical CT Using the Deep Scatter Estimation: Method and Robustness Analysis with Respect to Different Anatomies, Dose Levels, Tube Voltages, and Data Truncation
-
SISVMLearning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions
-
Slice-wise reconstruction for low-dose cone-beam CT using a deep residual convolutional neural network
-
SPARESparse-view reconstruction challenge for 4D cone-beam CT from a 1-min scan
-
SPSSSharpness preserved sinogram synthesis using convolutional neural network for sparse-view CT imaging
-
SR-CNNAugmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning
-
SUPER-2SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
-
X2CT-GANReconstructing CT from Biplanar X-Rays with Generative Adversarial Networks
-
3D-DLA3D Deep Learning Angiography (3D-DLA) from C-arm Conebeam CT
-
Adversarial Sparse-View CBCT Artifact Reduction
-
An Image Reconstruction Method Based on Total Variation and Wavelet Tight Frame for Limited-Angle CT
-
AUTOMAPImage reconstruction by domain-transform manifold learning
-
CNN-MARConvolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography
-
CNNRPGDCNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction
-
Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear
-
CPCE3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network
-
CTNetLose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion
-
DD-NetA Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution
-
DLCTDeep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems
-
DSEDeep Scatter Estimation (DSE): Feasibility of Using a Deep Convolutional Neural Network for Scatter Estimation in X-Ray Computed Tomography
-
Image Reconstruction is a New Frontier of Machine Learning
-
LEARNLearned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT
-
Low-dose x-ray tomography through a deep convolutional neural network
-
LPDLearned Primal-Dual Reconstruction
-
MS-DNetA mixed-scale dense convolutional neural network for image analysis
-
MSDNetImproving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks
-
Noise2NoiseLearning Image Restoration without Clean Data
-
Photon-counting CT: Technical Principles and Clinical Prospects
-
PTPNIntelligent Parameter Tuning in Optimization-Based Iterative CT Reconstruction via Deep Reinforcement Learning
-
PWLS-ULTRAAn Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction
-
RADRegularization analysis and design for prior-image-based X-ray CT reconstruction
-
SAGANSharpness-Aware Low Dose CT Denoising Using Conditional Generative Adversarial Network
-
SMGANStructure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
-
Sparse-View CT Reconstruction Using Wasserstein GANs
-
SSDALow-dose CT restoration via stacked sparse denoising autoencoders
-
Statistical Image Reconstruction Using Mixed Poisson-Gaussian Noise Model for X-Ray CT
-
TFU-NetFraming U-Net via Deep Convolutional Framelets - Application to Sparse-view CT
-
WavResNetDeep Convolutional Framelet Denoising for Low-Dose CT via Wavelet Residual Network
-
WGAN-VGGLow Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
-
An improved statistical iterative algorithm for sparse-view and limited-angle CT image reconstruction
-
Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review
-
CNN-InvRevConvolutional Neural Networks for Inverse Problems in Imaging: A Review
-
Coded aperture optimization in compressive X-ray tomography: a gradient descent approach
-
CycleGANUnpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
-
Deep Convolutional Neural Network for Inverse Problems in Imaging
-
DeepCNNA deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction
-
Edge-preserving reconstruction from sparse projections of limited-angle computed tomography using L0-regularized gradient prior
-
Generative Adversarial Networks for Noise Reduction in Low-Dose CT
-
IMAP-TVRobust low-dose CT sinogram preprocessing via exploiting noise-generating mechanism
-
KSAEIterative Low-dose CT Reconstruction with Priors Trained by Artificial Neural Network
-
Low-dose CT for the detection and classification of metastatic liver lesions: Results of the 2016 Low Dose CT Grand Challenge
-
Low-dose CT via convolutional neural network
-
Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization
-
ReATVReweighted Anisotropic Total Variation Minimization for Limited-Angle CT Reconstruction
-
REDThe Little Engine That Could: Regularization by Denoising (RED)
-
RED-CNNLow-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network
-
SENPLow‐dose CT reconstruction using spatially encoded nonlocal penalty
-
VISSView-interpolation of sparsely sampled sinogram using convolutional neural network
-
A Perspective on Deep Imaging
-
Deep-ADMM-NetDeep ADMM-Net for Compressive Sensing MRI
-
Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations
-
MAR-4DecMetal Artifact Reduction in CT: Where Are We After Four Decades?
-
SPIRDual energy CT with one full scan and a second sparse-view scan using structure preserving iterative reconstruction (SPIR)
-
Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications
-
Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty
-
Tensor-based dictionary learning for dynamic tomographic reconstruction
-
Image reconstruction from few views by L0-norm optimization
-
LIVEA limited-angle intrafraction verification (LIVE) system for radiation therapy
-
Low-dose X-ray computed tomography image reconstruction with a combined low-mAs and sparse-view protocol
-
Multi-Material Decomposition Using Statistical Image Reconstruction for Spectral CT
-
PBACPrior-based artifact correction (PBAC) in computed tomography
-
Sparse-view computed laminography with a spherical sinusoidal scan for nondestructive testing
-
TGVSparse-view x-ray CT reconstruction via total generalized variation regularization
-
TV-StokesTotal Variation-Stokes Strategy for Sparse-View X-ray CT Image Reconstruction
-
A technique for estimating 4D-CBCT using prior knowledge and limited-angle projections
-
Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT
-
ATVA limited-angle CT reconstruction method based on anisotropic TV minimization
-
Effects of sparse sampling schemes on image quality in low-dose CT
-
Feature constrained compressed sensing CT image reconstruction from incomplete data via robust principal component analysis of the database
-
Image reconstruction based on total-variation minimization and alternating direction method in linear scan computed tomography
-
Iterative CT Reconstruction Using Shearlet-Based Regularization
-
Iterative Image Reconstruction for Sparse-View CT Using Normal-Dose Image Induced Total Variation Prior
-
Optimization-based image reconstruction from sparse-view data in offset-detector CBCT
-
PnPPlug-and-Play priors for model based reconstruction
-
Sparse Tomography
-
Vision 20/20: Single photon counting x-ray detectors in medical imaging
-
4D Cone Beam CT via Spatiotemporal Tensor Framelet
-
AWTVAdaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction
-
Comparison of image quality in computed laminography and tomography
-
Few-view image reconstruction with dual dictionaries
-
Low-Dose X-ray CT Reconstruction via Dictionary Learning
-
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
-
A General Framework and Review of Scatter Correction Methods in Cone-Beam CT: Part 2 — Scatter Estimation Approaches
-
ADMMDistributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
-
Scatter-RevA General Framework and Review of Scatter Correction Methods in X-Ray Cone-Beam CT: Part 1 — Scatter Compensation Approaches
-
Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers
-
An Investigation of 4D Cone-Beam CT Algorithms for Slowly Rotating Scanners
-
Compressed sensing based cone-beam computed tomography reconstruction with a first-order method
-
Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT
-
NMARNormalized metal artifact reduction (NMAR) in computed tomography
-
Tomographic Reconstruction in the 21st Century
-
Directional View Interpolation for Compensation of Sparse Angular Sampling in Cone-Beam CT
-
Few-View Projection Reconstruction With an Iterative Reconstruction-Reprojection Algorithm and TV Constraint
-
FISTAA Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
-
On-the-Fly Motion-Compensated Cone-Beam CT Using an A Priori Model of the Respiratory Motion
-
The Split Bregman Method for L1-Regularized Problems
-
Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?
-
X-ray cargo container inspection system with few-view projection imaging
-
A novel digital tomosynthesis (DTS) reconstruction method using a deformation field map
-
Experimental Feasibility of Multi-Energy Photon-Counting K-Edge Imaging in Pre-Clinical Computed Tomography
-
High Temporal Resolution and Streak-Free Four-Dimensional Cone-Beam Computed Tomography
-
Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization
-
PICCSPrior Image Constrained Compressed Sensing (PICCS): A Method to Accurately Reconstruct Dynamic CT Images from Highly Undersampled Projection Data Sets
-
Tiny a priori knowledge solves the interior problem in computed tomography
-
A three-dimensional statistical approach to improved image quality for multislice helical CT
-
K-Edge Imaging in X-Ray Computed Tomography Using Multi-Bin Photon Counting Detectors
-
Compressed sensing
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Efficient Monte Carlo Based Scatter Artifact Reduction in Cone-Beam Micro-CT
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K-SVDAn Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
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Scatter Correction Method for X-Ray CT Using Primary Modulation: Theory and Preliminary Results
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Respiratory Correlated Cone Beam CT
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An Algorithm for Total Variation Minimization and Applications
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BPFExact image reconstruction on PI-lines from minimum data in helical cone-beam CT
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DDDistance-driven projection and backprojection in three dimensions
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ISTAAn iterative thresholding algorithm for linear inverse problems with a sparsity constraint
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OS-SARTOrdered-subset simultaneous algebraic reconstruction techniques (OS-SART)
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Statistical Image Reconstruction for Polyenergetic X-Ray Computed Tomography
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Theoretically Exact Filtered Backprojection-Type Inversion Algorithm for Spiral CT
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An Iterative Maximum-Likelihood Polychromatic Algorithm for CT
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Cone-Beam Computed Tomography with a Flat-Panel Imager: Magnitude and Effects of X-Ray Scatter
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Ordered subsets algorithms for transmission tomography
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Spatial resolution properties of penalized-likelihood image reconstruction: space-invariant tomographs
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A cone-beam reconstruction algorithm using shift-variant filtering and cone-beam backprojection
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OS-EMAccelerated image reconstruction using ordered subsets of projection data
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A general cone-beam reconstruction algorithm
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GGMRFA generalized Gaussian image model for edge-preserving MAP estimation
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ICDA local update strategy for iterative reconstruction from projections
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ROFNonlinear total variation based noise removal algorithms
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Mathematical framework of cone beam 3D reconstruction via the first derivative of the Radon transform
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Reduction of CT artifacts caused by metallic implants
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Fast calculation of the exact radiological path for a three-dimensional CT array
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Image Reconstruction from Cone-Beam Projections: Necessary and Sufficient Conditions and Reconstruction Methods
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FDKPractical cone-beam algorithm
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SARTSimultaneous Algebraic Reconstruction Technique (SART): A superior implementation of the ART algorithm
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An Inversion Formula for Cone-Beam Reconstruction
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An Improved Algorithm for Reprojecting Rays through Pixel Images
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MLEMMaximum Likelihood Reconstruction for Emission Tomography
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Optimal short scan convolution reconstruction for fan beam CT
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Correction for beam hardening in computed tomography
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A Method for Correcting Bone Induced Artifacts in Computed Tomography Scanners
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Energy-Selective Reconstructions in X-Ray Computerised Tomography
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Shepp-LoganThe Fourier reconstruction of a head section
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Computerized transverse axial scanning (tomography): Part 1. Description of system
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SIRTIterative methods for the three-dimensional reconstruction of an object from projections
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Three-dimensional Reconstruction from Radiographs and Electron Micrographs: Application of Convolutions instead of Fourier Transforms
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ARTAlgebraic Reconstruction Techniques (ART) for three-dimensional electron microscopy and X-ray photography
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Representation of a Function by Its Line Integrals, with Some Radiological Applications
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Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten (Johann Radon)
没有论文符合当前筛选条件。
已知时，每条说明开头会注明该发布包含什么：Projection + image（投影域 + 图像域）表示既有原始投影又有参考体，因而可以对前向模型做端到端评分；Projection only（仅投影域）表示只有原始数据；Image only（仅图像域）表示只有图像域数据，无法用来检验投影模型。
本库所收录论文、工具与数据的研究团队。每张卡片给出实验室主页，以及该团队已被本库收录的代表作。
北美
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WANG-AXIS Lab / Biomedical Imaging Center (BIC) WANG-AXIS 实验室 / 生物医学成像中心
伦斯勒理工学院的 WANG-AXIS 实验室与生物医学成像中心（BIC）由 Ge Wang 领导，研究方向涵盖 X 射线 CT 成像理论、系统与重建。该团队早期的工作 包括螺旋锥束 CT 重建与内层析成像（interior tomography），近年的工作将 深度学习应用于断层重建，其中包括 FORCE 框架（2025），该框架将泊松流 （Poisson flow）生成先验与数据保真相结合，用于低剂量、稀疏视角和金属 伪影条件下的重建。该中心还运行包括光子计数（MARS）光谱显微 CT 扫描仪 和基于光栅的 X 射线相位衬度成像平台在内的 CT 实验设备。
本库收录： Tomographic Foundation Model—FORCE: Flow-Oriented Reconstruction Conditioning Engine
Code in the lab's GitHub organization (github.com/WANG-AXIS): FORCE, RDDM (low-dose CT denoising), LEARN (sparse-data CT reconstruction network), M3FM.
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X-ray Tomographic Imaging Group X 射线断层成像课题组
芝加哥大学放射学系的 X 射线断层成像课题组由 Xiaochuan Pan 与 Emil Y. Sidky 领导，为临床、临床前、安检和同步辐射 CT 开发重建算法与 成像系统。该团队以约束总变分（TV）最小化重建著称——即 Sidky 与 Pan 于 2008 年提出的 ASD-POCS 算法：它在满足数据约束的条件下最小化图像的 总变分，可从稀疏或有限角度数据重建锥束 CT 图像。该团队近年的工作包括 方向性 TV 算法、一步法光谱 CT 重建，以及截断投影数据的重建。
本库收录： Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization · leehoy/CTReconstruction
CTReconstruction (GitHub: leehoy/CTReconstruction) — reconstruction code associated with the group.
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I-STAR Lab (Imaging for Surgery, Therapy, and Radiology) I-STAR 实验室
约翰斯·霍普金斯大学的 I-STAR 实验室（手术、治疗与放射成像实验室）由 Jeffrey Siewerdsen、J. Webster Stayman 与 Wojciech Zbijewski 领导， 研究面向图像引导介入的锥束 CT（CBCT）系统与三维重建。其研究包括惩罚 似然（基于模型的）重建、用于在植入物等已知物体存在时成像的已知组件 重建（known-component reconstruction），以及对 X 射线散射、探测器迟滞 和系统几何等物理效应的建模。该实验室成员的早期工作量化了平板探测器 CBCT 中 X 射线散射的强度与影响（Siewerdsen 与 Jaffray，2001），以及 锥束显微 CT 中基于蒙特卡罗的散射校正（Zbijewski 与 Beekman，2006）。
本库收录： Cone-Beam Computed Tomography with a Flat-Panel Imager: Magnitude and Effects of X-Ray Scatter · Efficient Monte Carlo Based Scatter Artifact Reduction in Cone-Beam Micro-CT
The lab site (istar.jhu.edu) maintains a Downloads section for lab software and data; no single flagship public toolkit was identified.
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Fessler Group Fessler 课题组
密歇根大学 Jeffrey Fessler 的课题组研究统计（基于模型的）图像重建 及其背后的优化算法，应用对象包括 CT、PET、SPECT 与 MRI。Fessler 与 Rogers（1996）分析了空间不变断层成像中惩罚似然重建的空间分辨率特性， 这项工作是后续设计具有可控分辨率特性的正则化方法的基础。该课题组维护 着密歇根图像重建工具箱（MIRT），提供 MATLAB 与 Julia（MIRT.jl）两个 版本，以及基于 PyTorch 的可微重建工具箱 MIRTorch。
本库收录： Spatial resolution properties of penalized-likelihood image reconstruction: space-invariant tomographs · MIRT · MIRTorch
MIRT — Michigan Image Reconstruction Toolbox (MATLAB; Julia port MIRT.jl under active development). MIRTorch — PyTorch-based differentiable reconstruction toolbox (PyPI: MIRTorch).
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CT Clinical Innovation Center CT 临床创新中心
梅奥诊所的 CT 临床创新中心成立于 2004 年，负责开发和评估新的 CT 技术、 临床应用与剂量降低技术，核心成员包括 Cynthia McCollough 与 Lifeng Yu。 其工作涵盖双能与多能量 CT 以及光子计数探测器 CT，McCollough 等人 （2015）对该领域作过综述。该中心还为 CT 研究界建立了公开数据资源： 2016 年 AAPM 低剂量 CT 大挑战赛（Low-Dose CT Grand Challenge），以及 托管于癌症影像档案库（TCIA）的低剂量 CT 图像与投影数据集 （LDCT-and-Projection-data），该数据集提供 299 例头、胸、腹患者检查的 常规剂量与模拟低剂量投影数据及全剂量图像。
本库收录： Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications · LDCT-and-Projection-data · AAPM Low-Dose CT Grand Challenge
LDCT-and-Projection-data library (TCIA) — 299 patient exams with routine-dose and simulated low-dose projection data. DICOM-CT-PD — open, vendor-neutral data format for CT projection data developed by the center.
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CT Research Group (UW CT Lab) UW CT 实验室
威斯康星大学麦迪逊分校 Guang-Hong Chen 的 CT 研究组从事低剂量与欠采样 CT 的重建方法研究。Chen 提出了先验图像约束压缩感知（PICCS）方法，利用 一幅先验图像作为约束，从高度欠采样的投影数据重建动态 CT 图像；PICCS 已被应用于心脏成像、灌注成像、双能 CT 与 C 臂 CT。该组还从事能谱与 光子计数探测器 CT 研究；威斯康星大学麦迪逊分校是 GE 医疗硅基光子计数 CT 系统在美国的首个临床评估站点，并于 2022 年 11 月完成首批人体扫描。
本库收录： Prior Image Constrained Compressed Sensing (PICCS): A Method to Accurately Reconstruct Dynamic CT Images from Highly Undersampled Projection Data Sets · Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS)
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Bouman Group Bouman 课题组
普渡大学 Charles Bouman 课题组从事 X 射线 CT 的基于模型的迭代重建 （MBIR）研究，将重建表述为带有显式前向模型和马尔可夫随机场先验的统计 估计问题。该组 20 世纪 90 年代与 Ken Sauer（圣母大学）合作的 MBIR 统计重建工作，后来被 GE 医疗商业化为用于低剂量临床 CT 的 Veo MBIR 产品。该组维护开源软件 SVMBIR——一个面向平行束与扇束 CT 的 MBIR 实现，并已将基于模型的方法扩展到分布式与多节点重建。
本库收录： A generalized Gaussian image model for edge-preserving MAP estimation · A local update strategy for iterative reconstruction from projections · svmbir
SVMBIR (GitHub: cabouman/svmbir) — supervised MBIR for parallel/fan-beam CT.
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LEAP Project, Lawrence Livermore National Laboratory 劳伦斯利弗莫尔国家实验室 LEAP 项目
劳伦斯利弗莫尔国家实验室的 LEAP 项目提供一套三维透射 CT 算法库，包含 预处理、正投影/反投影算子以及解析（FBP/FDK）与迭代重建算法，提供 Python 与 C++ 接口，并带有 PyTorch 绑定，使投影算子可微、可嵌入深度 学习流程。投影算子支持多种几何，并在多 GPU 与多核 CPU 硬件上运行， 面向工业与实验室 CT。基于物理的建模与校正（散射、束硬化、双能分解） 由配套软件 XrayPhysics 提供——一个带 Python 绑定的 C/C++ 库，用于 X 射线截面与源谱建模。
本库收录： LEAP · XrayPhysics
LEAP — LivermorE AI Projector for Computed Tomography (GitHub: LLNL/LEAP). XrayPhysics (GitHub: kylechampley/XrayPhysics) — cross sections, spectra modeling, multi-material beam-hardening correction, dual-energy decomposition.
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APS Tomography Group 先进光子源断层成像团队
阿贡国家实验室先进光子源的断层成像团队开发同步辐射微米与纳米断层 成像光束线所使用的重建与数据处理软件。其软件成果包括 TomoPy——一个 用于同步辐射断层数据预处理与重建的 Python 框架（Gürsoy、De Carlo 等， 2014），以及 TomocuPy——Viktor Nikitin 开发的以并发分块方式重建数据的 GPU 流水线。该团队还维护 TomoBank，一个收录实验与模拟断层数据集及 其描述信息的仓库，供研究者在共享的光束线数据上开发和比较重建方法。
TomoPy (GitHub: tomopy/tomopy) — tomographic reconstruction in Python. TomocuPy — GPU-accelerated reconstruction pipeline (Nikitin, Journal of Synchrotron Radiation, 2023). TomoBank — open repository of synchrotron tomography datasets.
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Lei Xing's group, Medical Physics Division 斯坦福大学 Lei Xing 课题组（医学物理部）
Lei Xing 主持斯坦福大学放射肿瘤学系医学物理部，其课题组研究面向放射 治疗的人工智能与成像。其 CT 重建工作包括由单个投影视角重建患者特异的 容积图像（Nature Biomedical Engineering，2019）、用于稀疏采样的带先验 嵌入的隐式神经表示（NeRP、PINER）、压缩感知与四维锥束 CT、低剂量网络 TransCT，以及基于深度学习的金属伪影校正。
本库收录： Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning · NeRP: Implicit Neural Representation Learning With Prior Embedding for Sparsely Sampled Image Reconstruction · PINER: Prior-Informed Implicit Neural Representation Learning for Test-Time Adaptation in Sparse-View CT Reconstruction · TransCT: Dual-Path Transformer for Low Dose Computed Tomography · Deep Sinogram Completion With Image Prior for Metal Artifact Reduction in CT Images · Metal artifact reduction in 2D CT images with self-supervised cross-domain learning · Compressed sensing based cone-beam computed tomography reconstruction with a first-order method · 4D Cone Beam CT via Spatiotemporal Tensor Framelet
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Imaging and Informatics Lab (CT Lab) 麻省大学洛厄尔分校成像与信息学实验室（Hengyong Yu）
麻省大学洛厄尔分校电气与计算机工程系的成像与信息学实验室由 Hengyong Yu 主持，研究计算机断层成像与医学图像处理，从基于压缩感知的内部重建 到基于机器学习的层析成像。其收录于本目录的论文包括基于 CNN 的金属伪影 校正（CNN-MAR，IEEE TMI 2018）、用于低剂量 CT 的 Transformer 去噪网络 CTformer、对抗式重建（CLEAR）、用于动态与能谱 CT 的张量方法，以及有限角 心脏 CT。
本库收录： Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography · CTformer: Convolution-Free Token2Token Dilated Vision Transformer for Low-Dose CT Denoising · CLEAR: Comprehensive Learning Enabled Adversarial Reconstruction for Subtle Structure Enhanced Low-Dose CT Imaging · Tensor-based dictionary learning for dynamic tomographic reconstruction · Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction · Hybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography
欧洲
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Pattern Recognition Lab (LME) 模式识别实验室
埃尔朗根-纽伦堡大学（FAU）模式识别实验室自 2015 年起由 Andreas Maier 担任负责人，将机器学习应用于医学成像，其 CT 研究主线是"已知算子" （known operator）方法——把投影与重建的物理过程作为可微层嵌入神经 网络。其软件成果包括 PYRO-NN，一个面向平行束、扇束与锥束几何的可微 CT 算子库，以及 diffct，一个 CUDA 加速的可微 CT 库，其后续版本支持 非圆与任意采集轨迹。德国研究联合会（DFG）资助的项目 "Deep-Learning-basierte CBCT-Rekonstruktion beliebiger Trajektorien" （周期 36 个月，经费 324,459 欧元，2025 年 9 月公布）与代根多夫应用 技术大学及斯坦福大学合作，开发面向任意轨迹的基于深度学习的 CBCT 重建方法，并使用 FAU 区域计算中心（RRZE）的算力设施。
diffct (GitHub: sypsyp97/diffct) — CUDA-accelerated differentiable CT library. PYRO-NN (GitHub: csyben/PYRO-NN) — differentiable CT reconstruction operators with PyTorch support and native CUDA kernels.
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Computational Image Analysis (CIA) Group 剑桥大学计算图像分析组
剑桥大学计算图像分析组从事数学成像与反问题研究，其 CT 方向致力于 弥合数学方法进展与临床落地之间的鸿沟。研究涵盖数据驱动重建方法 （包括迭代展开方法、学习正则项、即插即用方法），以及变分正则化、 Krylov 子空间求解器和四维成像。团队开发了开源软件包 LION（Learned Iterative Optimization Networks），收录后处理、展开、学习正则项、 即插即用与自监督 CT 重建方法，并提供可复现的训练流程与 CT 数据集。
本库收录： LION
LION (Learned Iterative Optimization Networks) — data-driven CT reconstruction methods with reproducible training pipelines; the group also co-maintains the TIGRE toolbox for cone-beam reconstruction.
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Computational Imaging Group 阿姆斯特丹 CWI 计算成像组
CWI 计算成像组开发以数学与算法驱动的三维成像方法，服务于科学、医学 与工业，结合先进采集、参数估计与离散层析成像，包括通过 RECAST3D 实现实时重建，以及 FleX-ray 实验室将定制 CT 扫描仪与先进处理和 AI 算法相连。该组与安特卫普大学联合开发了 ASTRA Toolbox——面向二维与 三维层析成像的高性能 GPU 基元 MATLAB/Python 平台，被科研人员、工业 伙伴及大型同步辐射装置（ESRF、APS）采用；还开发了其 Python 封装 tomosipo，用于复杂几何的三维层析，旨在把快速 GPU 计算与主流深度 学习工具结合。
本库收录： ASTRA Toolbox · tomosipo · FleX-ray Walnut CBCT Collection (CWI)
ASTRA Toolbox (astra-toolbox.com) and tomosipo; RECAST3D for real-time visualization of tomographic imaging.
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imec-Vision Lab (ASTRA Group) 安特卫普大学 imec 视觉实验室（ASTRA 组）
imec 视觉实验室下属的 ASTRA 研究组面向电子、X 射线、中子与磁共振 成像开发层析重建算法，聚焦跨应用领域的通用重建问题。核心方向包括 相衬 X 射线 CT、非标准采集几何的 X 射线层析（例如层析合成 laminography、体层合成 tomosynthesis）、动态（4D）层析与离散层析。 该组与 CWI 计算成像组联合开发了 ASTRA Toolbox——开源 GPU 加速的 层析重建方法研发平台。
本库收录： ASTRA Toolbox
ASTRA Toolbox (developed jointly with CWI); DART-related discrete tomography methods.
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Mathematical Imaging Group (Ozan Öktem) 瑞典皇家理工学院 Öktem 课题组
Öktem 在 KTH 的研究将反问题、凸优化与机器学习结合用于医学成像， 方向之一是学习式迭代（展开）重建方法。他与 Jonas Adler 提出了 Learned Primal-Dual 重建方法（IEEE T-MI 2018），并开发了反问题 Python 库 ODL（Operator Discretization Library），提供正向算子、几何 与解析方法，被后续众多 CT 重建研究采用。learned_gradient_tomography 代码库实现了早期的迭代深度网络工作（Adler & Öktem，《用迭代深度神经 网络求解不适定反问题》，Inverse Problems 2017）。
本库收录： ODL · Learned Primal-Dual · Learned Gradient Tomography
ODL (odlgroup/odl) — operator discretization library for inverse problems; learned primal-dual and learned gradient tomography implementations.
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CREATIS Tomographic Reconstruction Group 里昂 CREATIS 层析重建组
Simon Rit 在 CREATIS 的团队从事层析重建研究，聚焦放疗图像引导的锥束 CT，包括正则化与稀疏（压缩感知）CT 方法，以及针对心脏与呼吸运动的 运动补偿 X 射线 CT（其方法已转移到荷兰的临床方案）。团队开发了开源 锥束 CT 重建工具包 RTK（openrtk.org），基于 ITK 构建、采用 Plastimatch 的 GPU 代码，按 Apache 2.0 许可发布，既提供 C++ 类与命令行工具，也 通过 PyPI 发布 Python 包（itkrtk）。RTK 支持 Elekta、Varian 与 IBA 扫描仪输入、CPU/GPU 的 Feldkamp-David-Kress 重建、4D ROOSTER 等四维 算法，以及诊断 CT 扫描仪的柱面探测器几何。
本库收录： RTK
RTK (Reconstruction Toolkit; GitHub: RTKConsortium/RTK, PyPI: itkrtk) — open-source cone-beam CT reconstruction toolkit built on ITK.
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Tomographic Imaging / Core Imaging Library (CIL) Group
英国科学与技术设施委员会（STFC）的层析成像团队与曼彻斯特大学等伙伴 通过层析计算成像协作计算项目（CCPi）合作，开发用于层析图像重建的 开源软件。其核心成像库（CIL）是一个 Python 框架，用于对滤波反投影 不足以处理的数据进行迭代正则化重建，这类数据包括带噪、不完整、非 标准几何或多通道（动态与能谱）数据。该团队还研究协同成像，将层析 成像与其他模态和数据源结合进行联合重建。
本库收录： Core Imaging Library (CIL) · CCPi Regularisation Toolkit · TomoPhantom
Core Imaging Library (CIL) — open-source Python reconstruction framework (GitHub: TomographicImaging/CIL), Apache-2.0. CCPi Regularisation Toolkit. TomoPhantom — 2D–4D analytical phantoms for reconstruction benchmarking.
亚洲
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BISPL (Bio Imaging & Signal Processing Lab)
韩国科学技术院（KAIST）的生物成像与信号处理实验室（BISPL）由 Jong Chul Ye 教授领导，研究面向生物医学成像的信号处理与机器学习 方法。其核心研究方向之一是将扩散模型与基于分数的生成模型用作先验 来求解成像逆问题，包括稀疏视角与有限角 CT；其 DiffusionMBIR 方法将 预训练的二维扩散模型与基于模型的迭代重建相结合，并沿层间方向施加 全变分先验，用于求解稀疏视角和有限角层析等三维重建问题。
本库收录： Solving 3D Inverse Problems Using Pre-Trained 2D Diffusion Models
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CT Imaging Group, Department of Engineering Physics 清华大学工程物理系 CT 成像组
清华大学工程物理系 CT 成像组由邢宇翔、张丽两位教师带领，研究 X 射线 CT 成像系统与图像重建方法。研究内容包括层析重建算法、光子计数与 能谱（多能）CT，以及以射线源阵列替代机架旋转的固定式多源 CT 系统 设计。该组依托系内的粒子技术与辐射成像教育部重点实验室开展工作。
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CT Reconstruction Groups (Chen / Luo) 东南大学 CT 重建组（陈阳 / 罗守华）
本条目涵盖东南大学位于南京的两个 CT 重建研究组：计算机科学与工程 学院（影像科学与技术实验室）的陈阳教授组，以及生物科学与医学工程 学院的罗守华教授组。他们的研究面向低剂量 CT 重建，以及稀疏视角、 有限角等非理想测量条件下的图像增强。2026 年，由计算机学院团队主导 的 TAMP 发表于 Nature Communications，该工作是一个在物理仿真数据上 预训练、用于非理想测量 CT 通用增强的成像基础模型。
本库收录： Imaging foundation model for universal enhancement of non-ideal measurement CT
TAMP model code (announced in the paper for release at github.com/YutingHelist/TAMP); SimNICT — the physics-simulated pre-training dataset built for TAMP (announced via Hugging Face, YutingHelist/SimNICT).
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XMed Lab XMed 实验室
香港科技大学 XMed 实验室由电子与计算机工程学系的李小萌领导，研究 医学人工智能与计算机视觉。其 DeepSparse 模型（IEEE Transactions on Medical Imaging，2026）是一个用于稀疏视角锥束 CT 重建的基础模型，在 大规模腹部 CT 数据集（AtlasAbdomenMini，约 7k 个体数据）上预训练， 并可用少量标注样本微调到不同解剖部位。
本库收录： DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction
DeepSparse code (GitHub: xmed-lab/DeepSparse); pretrained and fine-tuned model weights released on Hugging Face.
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MIAH Lab 复旦大学 MIAH 实验室（单洪明）
复旦大学类脑智能科学与技术研究院的 MIAH 实验室由单洪明领导，研究面向 医学影像的机器学习，侧重生成式人工智能与图像重建。其 CT 工作以低剂量 CT 为中心：单洪明早年与 Ge Wang 课题组合作的 MAP-NN（Nature Machine Intelligence，2019）和三维 CPCE 网络，之后在复旦发展为基于扩散模型的 去噪，如 CoreDiff（IEEE TMI，2024）与 FoundDiff。课题组还研究稀疏视角 与不完整视角重建（FreeSeed、GloReDi、ProCT）以及金属伪影校正（Quad-Net）。
本库收录： CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization · FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising · FreeSeed: Frequency-Band-Aware and Self-Guided Network for Sparse-View CT Reconstruction · Learning to Distill Global Representation for Sparse-View CT · Universal Pre-training for Generalizable Incomplete-View CT Reconstruction · Quad-Net: Quad-Domain Network for CT Metal Artifact Reduction · ASCON: Anatomy-Aware Supervised Contrastive Learning Framework for Low-Dose CT Denoising · DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising · Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction · 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network
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Deep Imaging Group (DIG) 四川大学深度成像课题组（张意）
深度成像课题组（DIG）由四川大学网络空间安全学院的张意教授领导，研究 基于深度学习的医学图像处理。其 CT 重建工作涵盖低剂量 CT（MAGIC，IEEE TMI 2021）、跨扫描几何的重建（PDF）、金属伪影校正（DAN-Net、IDOL-Net、 SemiMAR）、基于展开网络的稀疏视角与能谱 CT（RegFormer、SOUL-Net），以及 一篇 2024 年关于不完整投影数据深度学习 CT 重建的综述。
本库收录： MAGIC: Manifold and graph integrative convolutional network for low-dose CT reconstruction · CT Reconstruction With PDF: Parameter-Dependent Framework for Data From Multiple Geometries and Dose Levels · Dual-Domain Adaptive-Scaling Non-Local Network for CT Metal Artifact Reduction · IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction · SemiMAR: Semi-Supervised Learning for CT Metal Artifact Reduction · RegFormer: A Local–Nonlocal Regularization-Based Model for Sparse-View CT Reconstruction · SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction · A Review of Deep Learning CT Reconstruction From Incomplete Projection Data
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Imaging and Visual Representation Laboratory 南昌大学成像与视觉表示实验室（刘且根）
刘且根领导的南昌大学信息工程学院成像与视觉表示实验室，研究 MRI 与 CT 图像重建中的生成模型。其 CT 论文使用学习得到的先验与分数先验：用于低剂量 CT 的深度梯度先验（EASEL，IEEE TRPMS 2022）、用于稀疏视角 CT 的去噪自编码 先验（REDAEP）、正弦图域生成建模（IEEE TRPMS 2024），以及用于稀疏视角 CT 的小波细化扩散模型（SWORD，IEEE TMI 2024）。
本库收录： Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model · REDAEP: Robust and Enhanced Denoising Autoencoding Prior for Sparse-View CT Reconstruction · Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction · Stage-by-Stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction · Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary Tomography
本目录中一切条目的连接方式，拆成一组各自独立加载的小图：由团队、主题与工具包构成的骨架，数据集与使用它们的论文，手工策展的谱系树，以及每个主题一张图。选择一个分类即可探索。
总览
团队骨架
知识图谱的骨架：本 CT 重建目录中的研究团队、主题、工具包与指南，以及它们之间的连接。
87 个节点 · 209 条关系数据集与使用它们的论文
本 CT 重建目录中的数据集关系：与论文有关联记录的数据集与基准，以及使用它们的论文。
86 个节点 · 336 条关系CT 重建谱系树：从 FBP 到基础模型
一张可缩放画布上的 CT 重建谱系树：30 篇手工策展的论文，从解析重建的根出发，分叉出双域网络、隐式与显式表示、生成先验，最终汇入基础模型。
30 个里程碑 · 1984–2026按主题
综述与回顾
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
61 个节点 · 133 条关系经典与迭代重建
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
148 个节点 · 535 条关系稀疏视角 CT
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
250 个节点 · 1143 条关系有限角层析
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
112 个节点 · 506 条关系内部、感兴趣区与截断重建
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
16 个节点 · 26 条关系低剂量 CT 去噪
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
250 个节点 · 1517 条关系金属伪影校正
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
117 个节点 · 673 条关系能谱与双能 CT
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
97 个节点 · 445 条关系锥束 CT
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
176 个节点 · 580 条关系螺旋与多层 CT
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
10 个节点 · 16 条关系动态与 4D CT
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
64 个节点 · 183 条关系散射校正
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
18 个节点 · 40 条关系几何标定与运动补偿
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
54 个节点 · 109 条关系深度展开与基于模型的学习
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
161 个节点 · 808 条关系自监督与免训练方法
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
91 个节点 · 235 条关系扩散与生成式先验
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
124 个节点 · 372 条关系隐式神经表示
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
48 个节点 · 92 条关系高斯溅射
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
41 个节点 · 66 条关系其他主题
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
73 个节点 · 106 条关系相关工作：重建之外
本 CT 重建目录中单个主题的知识图谱：它的论文、论文使用的数据集、聚焦该主题的团队与最高产的作者。
34 个节点 · 57 条关系