Awesome Computed Tomography
A curated list of CT reconstruction resources — 653 papers (updated 2026-10-25), 52 toolkits, 79 datasets, 19 benchmarks, 29 learning resources. Every paper link resolved against Crossref, arXiv or the publisher’s own record.
- Papers
- 653
- Toolkits
- 52
- Datasets
- 79
- Benchmarks
- 19
- Updated
- 2026-10-25
Newest first within each topic. Each entry gives its venue and year in italics; _preprint_ marks work not (yet) published at a peer-reviewed venue, dated by its first public posting.
-
K-NeAS: Scalable Multi-Material CT Reconstruction Using Neural SDFs
-
3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems
-
A fast iterative reconstruction method of sparse angle CT for cylindrical lithium battery
-
Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction
-
AD-GSAnatomical Density-guided 3D Gaussian Splatting for Sparse-View Dental CBCT Reconstruction
-
AgenticCTLLM-Orchestrated Diagnose–Plan–Treat for Mixed-Degradation CT Reconstruction
-
AllFreqNetA unified frequency-enhanced all-in-one framework for medical image restoration
-
AMIFoundAn all-in-one foundation model for multimodal medical image restoration and enhancement
-
Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion
-
At FullTilt: Real-Time Open-Set 3D Macromolecule Detection Directly from Tilted 2D Projections
-
Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction
-
Bayesian Joint Reconstruction and Uncertainty Quantification for Limited-Data Multi-Contrast X-Ray CT
-
CAREA clinically validated CT foundation model with large-scale vision-language pretraining
-
CBCT-Based Synthetic CT Generation Using Conditional Flow Matching Model
-
CG-GLOREA Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction
-
Complex Wavelet-Based Sinogram Segmentation for Metal Artifact Reduction in Cone-Beam CT
-
Computed Tomography Reconstruction Algorithm Using Markov Random Field Model
-
Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays
-
Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
-
Cone-beam artifact reduction in Gamma Knife CBCT images using a line-arc-line scan trajectory
-
Cone-Beam CT Image Quality Enhancement Using A Latent Diffusion Model Trained with Simulated CBCT Artifacts
-
ContiLNNMitigating Slice Sampling Discontinuity with Liquid Neural Networks for Medical Image Restoration
-
Continuity-driven Synergistic Diffusion with Neural Priors for Ultra-Sparse-View CBCT Reconstruction
-
Convolutional neural network metal artifact reduction in ultra-high resolution photon counting detector CT of middle- and inner-ear implants
-
Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT
-
Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography
-
CryoDeRecA Supervised Multi-task Framework for Joint cryo-ET Restoration Enabled by Generative Physical Simulation
-
CT-CLIP/CT-CHATGeneralist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
-
CTOResolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT
-
CTPretrainDual-Domain Pretraining Foundation Model for CT Image Reconstruction
-
Data-driven filter design for flexible and noise-robust tomographic imaging
-
DC-PnPDPPlug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
-
DDN-FMA deep dictionary network-based foundation model for ultra-low-dose CT denoising
-
Deep Pseudo-Proximal Map: A Self-Supervised Data-Fitting Agent for Iterative Reconstruction
-
DeepSparseA Foundation Model for Sparse-View CBCT Reconstruction
-
Delta-NetDeep Dual-Domain Alternating Optimization Network for High Pitch Helical CT Reconstruction
-
DenoMambaA Fused State-Space Model for Low-Dose CT Denoising
-
DIAMONDDeep Learning-based Monoenergetic Imaging for Calcified Coronary Stenosis Assessment at Energy-integrating Detector CT
-
DiffNRDiffusion-Enhanced Neural Representation Optimization for Sparse-View 3D Tomographic Reconstruction
-
Diffusion model-regularized implicit neural representation for computed tomography metal artifact reduction
-
DINRRegularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction of Neutron Computed Tomography Data
-
DM4CTBenchmarking Diffusion Models for CT Reconstruction
-
Dose-Aware Cold Diffusion with Physics Consistency for Generalizable Low-Dose CT Reconstruction
-
Dual-domain U-Nets with embedded back projection operators for motion-resolved 4D CBCT reconstruction
-
DuDoSwinSelf-supervised Dual-domain Swin Transformer for Sparse-view CT Reconstruction
-
Dynamic Black-hole Emission Tomography with Physics-Informed Neural Fields
-
Efficient Unrolled Networks for Large-Scale 3D Inverse Problems
-
Equivariance2InverseA Practical Self-Supervised CT Reconstruction Method Benchmarked on Real, Limited-Angle, and Blurred Data
-
Event-Based Sparse-View Background-Oriented Schlieren Tomography
-
Exact-GSMathematically Rigorous and Accurate 3D Gaussian Splatting for 3D X-Ray Reconstruction
-
FaCT-GSFast and Scalable CT Reconstruction with Gaussian Splatting
-
FDA-ReconFaithful and Data-Adaptive Reconstruction for Limited-Angle CBCT
-
FID-Netan interpretable fourier and image domain convolutional dictionary network for industrial CT metal artifact reduction
-
Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based Refinement
-
Filter2Noisea framework for interpretable and zero-shot low-dose CT image denoising
-
Foundation-VAEFoundation VAEs for 3D CT Reconstruction, Augmentation, and Generation
-
From Sparse X-rays to 3D CT: Training-Free Reconstruction with Diffusion Priors
-
Gaussian Material Fields for Volumetric Multi-Energy CT Decomposition
-
GB-SVFBPGaussian-Based Shift-Variant FBP neural network
-
Geometry-Aware Diffusion Approximate Posterior Sampling for Sparse-View and Limited-Angle CT
-
GH-NAFGrid-Adaptive Hash-Level-Attended Neural Attenuation Fields for Discrepancy-Aware CBCT
-
GR-GaussianGraph-Based Radiative Gaussian Splatting for Sparse-View Tomography
-
Gradient-Based Trajectory Optimisation over Continuous Poses for Sparse-View Cone-Beam CT
-
H3D-MarNetWavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows
-
HARU-NetHybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography
-
HeadCT-FM3D foundation model for generalizable disease detection in head computed tomography
-
High-Fidelity 3D Tooth Reconstruction by Fusing Intraoral Scans and CBCT Data via a Deep Implicit Representation
-
High-Precision Image Reconstruction for Binary-Valued X-Ray CT From Limited-View Projections
-
HorusEyeA self-supervised foundation model for generalizable X-ray tomography restoration
-
HounsWorldA Multimodal World Model for Hidden Patient-State Readout, Reconstruction, and Simulation
-
HR-CNNPushing the limits of spatial resolution in clinical PCD-CT using a dedicated high-resolution convolutional neural network (HR-CNN)
-
Human-Guided Shade Artifact Suppression in CBCT-to-MDCT Translation via Schrödinger Bridge with Conditional Diffusion
-
ILVIterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction
-
Integration of Spectral CT with PET and SPECT: Bringing Tissue Composition Information to Molecular Imaging
-
ISCSImproving 2D Diffusion Models for 3D Medical Imaging with Inter-Slice Consistent Stochasticity
-
Joint-decoupled iterative CBCT reconstruction with hybrid scatter estimation and voxel-adaptive beam hardening correction
-
KLIPLocalized Distribution Shift Detection via KL-Divergence with Diffusion Priors in Inverse Problems
-
LamiGaussPitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction
-
Learned denoising with simulated and experimental low-dose CT data
-
Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction
-
Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction
-
LINGOLatent Initialization and Gradient Optimization for Sparse-view X-ray Novel View Synthesis and CT Reconstruction with 3D Gaussian Splatting
-
Low-Level Dataset Distillation for Medical Image Enhancement
-
LUCIDLearned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction
-
Mamba-MoEDeterministic Intermediate Expert Isolation With a Shared Reconstruction Head for All-in-One Medical Image Restoration
-
MARRDecoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration
-
MedGMAEGaussian Masked Autoencoders for Medical Volumetric Representation Learning
-
MerlinA Computed Tomography Vision-Language Foundation Model and Dataset
-
MGMARMetal-Guided Metal Artifact Reduction for X-ray Computed Tomography
-
Model-Based Iterative Reconstruction with View-Dependent Detector Displacements for Cone-Beam CT
-
Model-based motion simulation framework for deep learning-assisted motion correction in dynamic wrist CT
-
MVSS-PCRA Multiview Subsampling Approach With Prior-Constrained Reconstruction for Self-Supervised CT Denoising
-
NABNeural Adaptive Binning for Sparse-View CT Reconstruction
-
NAB-GSZero-shot CT Super-Resolution using Diffusion-based 2D Projection Priors and Signed 3D Gaussians
-
Neuromorphic X-Ray Computed Tomography
-
NGPSStructure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling
-
Noise2MatchSelf-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching
-
Non-circular scan trajectories for reducing cone-beam artifacts in Gamma Knife CBCT images: a simulation study
-
OpenProsA Large-Scale Dataset for Limited View Prostate Ultrasound Computed Tomography
-
OX-NeRF3D X-ray Tomography Reconstruction from Sparse Views Using Implicit Neural Representation
-
Per-Bundle, Pre-Reconstruction Inversion for Temporal CT: Flux-Compensation Design and Multi-Patient Generalization
-
PhyDiCTPlug-and-Play CT Reconstruction from Sparse X-Rays via Differentiable Rendering and Strong Priors
-
ProCTPrompted Contextual Transformer for Incomplete-View CT Reconstruction
-
Projection-Volume Fidelity Divergence: Diagnosing and Controlling Optimization Drift in Sparse-View 3D Gaussian Tomography
-
Radioactive 3D Gaussian Ray Tracing for Tomographic Reconstruction
-
Radon Transforms of Projection Functions and Aleksandrov-Fenchel Inequalities
-
RAMReconstruct Anything Model: A Lightweight General Model for Computational Imaging
-
RCL-MambaA Dual-domain State Space Model for Measurement-oriented Image Restoration in Rotational Sparse-View Scanning Computed Laminography
-
RDDMA Residual-Driven Drifting Model for High-Fidelity Low-Dose CT Denoising
-
Restore-RWKVEfficient and Effective Medical Image Restoration with RWKV
-
Revisiting Pose Sensitivity in Splat-based Computed Tomography under Sparse-view Reconstruction
-
RinerUnsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT
-
Robustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT under Challenging Acquisition Settings
-
Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstruction
-
Spectral Consistent Flow for One-Step 3D Medical Image Translation
-
Spectral-XLatent prior enhanced spectral CT restoration with mamba-assisted X-net
-
Splat-Based Metal Artifact Reduction in Cone-Beam CT via Compact Attenuation Modeling
-
Splat-based Metal Artifact Reduction in Cone-Beam CT via Polychromatic Modeling
-
STCNetDual-Domain Sparse-View 4D CBCT Reconstruction with Spatial-Temporal Consistency Learning
-
TAMPImaging foundation model for universal enhancement of non-ideal measurement CT
-
TANZANITECT data harmonization via learned virtual monoenergetic imaging for cross-kV scan translation and radiomics reproducibility
-
Texture-preserving implicit neural representation for cone-beam CT truncated reconstruction
-
TG-FieldGeometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction
-
Tomographic Sparse View Selection Using the View Covariance Loss
-
TomoTransformerTowards a Foundation Model for CT Reconstruction
-
Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model
-
Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation
-
Towards reconstructing experimental sparse-view X-ray CT data with diffusion models
-
TPAw-LAwTVTruncated projection adaptive weighting combined with adaptive TV for artifact reduction in linear computed laminography
-
Truncation artifact reduction in cone-beam computed tomography scans using conditional generative diffusion models
-
UniH3UniH³: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
-
UniMedIRAll-In-One Medical Image Restoration via Text-Guided Prompting and Dual-Domain Modeling
-
Unsupervised Metal Artifact Reduction in Dental CBCT using Fine-tuned Cycle-Consistent Adversarial Networks
-
Variational Garrote for Sparse Inverse Problems
-
VFMINRSparse-View CT Reconstruction via Implicit Neural Representation Learning Powered by Dual-Domain Vision Foundation Models
-
VoDaSuReA Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution
-
VoxelSynth3DInterpretable Volumetric Image-Domain Metal Artifact Reduction with a Paired Synthetic CLINIC-Metal Benchmark
-
Wavelet-Consistent Diffusion Posterior Sampling for Limited-Angle and Sparse CT
-
X-SplatUnified Gaussian Splatting for Joint RGB and X-ray 3D Reconstruction
-
XDen-1KA Density Field Dataset of Real-World Objects
-
3D CBCT Artefact Removal Using Perpendicular Score-Based Diffusion Models
-
4DGS-4DCBCTSpatiotemporal Gaussian Optimization for 4D Cone Beam CT Reconstruction from Sparsely Sampled Projections
-
A CT Geometry With Multiple Centers Of Rotation For Solving Sparse View Problem
-
A Learnt Half-Quadratic Splitting-Based Algorithm for Fast and High-Quality Industrial Cone-beam CT Reconstruction
-
Accelerated Optimization of Implicit Neural Representations for CT Reconstruction
-
Adaptive Diffusion Models for Sparse-View Motion-Corrected Head Cone-beam CT
-
Advancing Limited-Angle CT Reconstruction Through Diffusion-Based Sinogram Completion
-
An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of View
-
An unsupervised sparse-view CT reconstruction framework using combination of iterative deep image prior and ADMM
-
Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?
-
Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction
-
CALIMAR-GANAn unpaired mask-guided attention network for metal artifact reduction in CT scans
-
CoCo-PnPLearning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Inverse Problems
-
Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components
-
Coordinate-Based Speed of Sound Recovery for Aberration-Corrected Photoacoustic Computed Tomography
-
CT-FMVision Foundation Models for Computed Tomography
-
CvG-DiffCross-View Generalized Diffusion Model for Sparse-View CT Reconstruction
-
DaPTDegradation-Aware Prompted Transformer for Unified Medical Image Restoration
-
Data-Efficient Limited-Angle CT Using Deep Priors and Regularization
-
DDSwinIRSwinIR-based Dual-Domain Reconstruction for Sparse-View Computed Tomography
-
Deep learning CT image restoration using system blur and noise models
-
DGRDiscretized Gaussian Representation for Tomographic Reconstruction
-
DiffCodeAll-in-One Medical Image Restoration with Latent Diffusion-Enhanced Vector-Quantized Codebook Prior
-
DiffNAFIterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary Tomography
-
Diffusion-Based Limited-Angle CT Reconstruction under Noisy Conditions
-
DIGSDynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model
-
DINRDistributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction
-
DPERDisentangled deep learning method for interior tomographic reconstruction of low-dose x-ray CT
-
DPKADual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention
-
DVG-DiffusionDual-View Guided Diffusion Model for CT Reconstruction from X-Rays
-
EAGLEAn Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction
-
EDAElucidating the Design Space of Arbitrary-Noise-Based Diffusion Models
-
Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration
-
Epi-NAFEnhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar Consistency
-
Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT
-
Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT: From Simulated to Real Data
-
FedM2CTFederated Metadata-Constrained iRadonMAP Framework with Mutual Learning for All-in-One Computed Tomography Imaging
-
FIND-Net -- Fourier-Integrated Network with Dictionary Kernels for Metal Artifact Reduction
-
Flex-DDDeep denoising model with flexible priors for sparse-view CT reconstruction
-
FORCETomographic Foundation Model—FORCE: Flow-Oriented Reconstruction Conditioning Engine
-
FoundDiffFoundational Diffusion Model for Generalizable Low-Dose CT Denoising
-
GANeXtA Fully ConvNeXt-Enhanced Generative Adversarial Network for MRI- and CBCT-to-CT Synthesis
-
GDP-NetGlobal Dependency-Enhanced Dual-Domain Parallel Network for Ring Artifact Removal
-
GLFCUnified Global-Local Feature and Contrast Learning with Mamba-Enhanced UNet for Synthetic CT Generation from CBCT
-
GLIMPSEGeneralized Locality for Scalable and Robust CT
-
ILUMENATELearned high resolution energy-integrating detector CT angiography: Harnessing the power of ultra-high-resolution photon counting detector CT
-
InsideOutIntegrated RGB-Radiative Gaussian Splatting for Comprehensive 3D Object Representation
-
Investigating the effects of machine learning generalization for enhancing accuracy in fast X-ray computed tomography for industrial metrology
-
Joint CT reconstruction of anatomy and implants using a mixed prior model
-
KMAR-NetDeep learning-based metal artifact reduction in CT for total knee arthroplasty
-
LangMambaA Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
-
LARGE-DiffNetCone-Beam Computed Tomography Limited-Angle Image Reconstruction Using Planning CT-Guided Deep Diffusion Model
-
Latent Space Consistency for Sparse-View CT Reconstruction
-
LCTfoundA lung CT vision foundation model facilitating disease diagnosis and medical imaging
-
Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction
-
Learning Wavelet-Sparse FDK for 3D Cone-Beam CT Reconstruction
-
Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models
-
Limited-angle SPECT image reconstruction using deep image prior
-
Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine
-
LWCDNetAn Interpretable Learning Weighted Convolutional Dictionary Network for Metal Artifact Reduction in CT Images
-
MedVAEEfficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders
-
MInDI-3DIterative Deep Learning in 3D for Sparse-view Cone Beam Computed Tomography
-
Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-View CT Reconstruction
-
Modeling X-Ray Photon Pile-Up with a Normalizing Flow
-
MONSTRModel-Oriented Neutron Strain Tomographic Reconstruction
-
Motion-GradA Gradient-Based Approach to Fast and Accurate Head Motion Compensation in Cone-Beam CT
-
Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation
-
Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
-
Noisier2InverseSelf-Supervised Learning for Image Reconstruction With Correlated Noise
-
Nonperiodic dynamic CT reconstruction using backward-warping INR with regularization of diffeomorphism (BIRD)
-
Ordered-subsets Multi-diffusion Model for Sparse-view CT Reconstruction
-
PALETTEPhysics-informed model-based generative neural network for synthesizing scanner- and algorithm-specific low-dose CT exams
-
PDuMSRNetPrompt Guiding Multi-Scale Adaptive Sparse Representation-driven Network for Low-Dose CT MAR
-
Physics-Inspired Gaussian Kolmogorov-Arnold Networks for X-ray Scatter Correction in Cone-Beam CT
-
PRAISE-NetDeep Projection-Domain Data-Consistent Learning Network for CBCT Metal Artifact Reduction
-
Preserving noise texture through training data curation for deep learning denoising of high-resolution cardiac EID-CT
-
Prior-Adapted Progressive Time-Resolved CBCT Reconstruction Using a Dynamic Reconstruction and Motion Estimation Method
-
Projection Embedded Diffusion Bridge for CT Reconstruction from Incomplete Data
-
PWLS-SOMalternative PWLS reconstruction for limited-view CT by strategic optimization of a deep learning model
-
Recover Biological Structure from Sparse-View Diffraction Images with Neural Volumetric Prior
-
ReMAR-DSRecalibrated Feature Learning for Metal Artifact Reduction and CT Domain Transformation
-
SDBSystem-Embedded Diffusion Bridge Models
-
SDFA self-supervised denoiser framework for X-Ray computed tomography
-
Semantic contrastive learning for orthogonal X-ray computed tomography reconstruction
-
SIRE-NetStructure-Integrated Refinement and Enhancement Network for Limited-Angle CT Reconstruction
-
Sparse view tomographic reconstruction of elongated objects using learned primal-dual networks
-
Spectral DPSMulti-Material Decomposition Using Spectral Diffusion Posterior Sampling
-
SpenerUnsupervised Self-Prior Embedding Neural Representation for Sparse-View CT Reconstruction
-
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction
-
Swap-NetA Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction to ICF Applications
-
Synthetic CT image generation from CBCT: A Systematic Review
-
TAP-CT3D Task-Agnostic Pretraining of Computed Tomography Foundation Models
-
TATTask-Adaptive Transformer for All-in-One Medical Image Restoration
-
Time-resolved dynamic CBCT reconstruction using prior-model-free spatiotemporal Gaussian representation (PMF-STGR)
-
TMGANTexture Matching GAN for CT Image Enhancement
-
TomoSelfDEQSelf-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction
-
TUFTowards Multi-scenario Generalization: Text-Guided Unified Framework for Low-Dose CT and Total-Body PET Reconstruction
-
Ultra-High-Resolution Photon-Counting-Detector CT with a Dedicated Denoising Convolutional Neural Network for Enhanced Temporal Bone Imaging
-
Ultrafast Deep Learning-Based Scatter Estimation in Cone-Beam Computed Tomography
-
UniSinoPhysics-Driven Foundational Model for Universal CT Sinogram Standardization
-
VOI-NetDeep learning-based volume of interest imaging in helical CT for image quality improvement and radiation dose reduction
-
Vol3DGSVolumetrically Consistent 3D Gaussian Rasterization
-
When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT
-
X-FieldA Physically Grounded Representation for 3D X-ray Reconstruction
-
X-GRMLarge Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography
-
X-ray computed laminography: A brief review of mechanisms, reconstruction, applications and perspectives
-
X2-Gaussian4D Radiative Gaussian Splatting for Continuous-Time Tomographic Reconstruction
-
XGenReconA New Perspective in Ultrasparse Volumetric CBCT Reconstruction Through Geometry-Controlled X-Ray Projection Generation
-
Zero-Shot Low-dose CT Denoising via Sinogram Flicking
-
3D Helical CT Reconstruction With a Memory Efficient Learned Primal-Dual Architecture
-
A Review of Deep Learning CT Reconstruction From Incomplete Projection Data
-
AMIRAll-In-One Medical Image Restoration via Task-Adaptive Routing
-
Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review
-
ATOMMotion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks
-
BaggageCTFan-Beam CT Reconstruction for Unaligned Sparse-View X-Ray Baggage Data
-
C2RVCross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction
-
Cas-PREDA Limited-Angle Cone Beam CT Reconstruction Research Based on Prior Information
-
Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography
-
CoreDiffContextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
-
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series
-
DCDL-GSDeep convolutional dictionary learning network for sparse view CT reconstruction with a group sparse prior
-
DDDMDual-Domain Diffusion Model for Sparse-View CT Reconstruction
-
Deep-InteriorA new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning
-
Deep-silicon photon-counting x-ray projection denoising through reinforcement learning
-
DIF-GaussianLearning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction
-
DiffReconDiffusion Models for Medical Image Reconstruction
-
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
-
DiffusionBlendLearning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
-
DiffuX2CTDiffusion Learning to Reconstruct CT Images from Biplanar X-Rays
-
DPSCT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model
-
DuDoDp-MARUnsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains
-
Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
-
Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction
-
How auto-differentiation can improve CT workflows: classical algorithms in a modern framework
-
INCODEImplicit Neural Conditioning with Prior Knowledge Embeddings
-
INR-JointImplicit Neural Representations for Robust Joint Sparse-View CT Reconstruction
-
LEDALow-dose CT Denoising with Language-engaged Dual-space Alignment
-
Machine learning based sinogram interpolation for X-ray computed tomography validated on experimental data
-
Machine learning in industrial X-ray computed tomography - a review
-
MAISIMedical AI for Synthetic Imaging
-
Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework
-
MJCNetCoarse-to-Fine Learning for Planning CT-Enhanced Limited-Angle CBCT Reconstruction
-
MsDu-NetsMultistage Dual-Domain Networks With Informative Prior and Self-Augmentation for Dual-Energy Limited-Angle CT Reconstruction
-
NeRPImplicit Neural Representation Learning With Prior Embedding for Sparsely Sampled Image Reconstruction
-
Neural Field Regularization by Denoising for 3D Sparse-View X-Ray Computed Tomography
-
PKAID-NetImproved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network
-
Plug-and-Play Learned Proximal Trajectory for 3D Sparse-View X-Ray Computed Tomography
-
PND-NetPhysics-Inspired Non-Local Dual-Domain Network for Metal Artifact Reduction
-
Quad-NetQuad-Domain Network for CT Metal Artifact Reduction
-
R2-GaussianRectifying Radiative Gaussian Splatting for Tomographic Reconstruction
-
RBP-DIPResidual back projection with deep image prior for ill-posed CT reconstruction
-
SAX-NeRFStructure-Aware Sparse-View X-Ray 3D Reconstruction
-
Scatter-DLScatter Correction Using Deep Learning for Cone-Beam Computed Tomography
-
SOUL-NetA Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction
-
SWORDStage-by-Stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
-
Two-View Industrial CT Reconstruction Based on a Multiscale Conditional Latent Diffusion Network
-
Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization
-
U-SwinHybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography
-
Wavelet-Improved Score-Based Generative Model for Medical Imaging
-
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
-
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
-
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
-
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
-
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
-
GenerateCTText-Conditional Generation of 3D Chest CT Volumes
-
GGLFGradient-based geometry learning for fan-beam CT reconstruction
-
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
-
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
-
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
-
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
-
DiffMedDiffusion Models for Medical Image Analysis: A Comprehensive Survey
-
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
-
MALARMultiple Adversarial Learning based Angiography Reconstruction for Ultra-low-dose Contrast Medium CT
-
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
-
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
-
SNAFSparse-View CBCT Reconstruction with Neural Attenuation Fields
-
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
-
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
-
DAN-NetDual-Domain Adaptive-Scaling Non-Local Network for CT Metal Artifact Reduction
-
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
-
DICDNetDeep Interpretable Convolutional Dictionary Network 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
-
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
-
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
-
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
-
SinoNetNoise-Generating-Mechanism-Driven Unsupervised Learning for Low-Dose CT Sinogram Recovery
-
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
-
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
-
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
-
HD-CNNArtifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging
-
HDNetHybrid-domain neural network processing for sparse-view CT reconstruction
-
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
-
Momentum-NetFast and convergent iterative neural network for inverse problems
-
Moreau-envelope-enhanced nonlocal shearlet transform and total variation for sparse-view CT reconstruction
-
Multi-Scale Learned Iterative Reconstruction
-
Noise2InverseSelf-Supervised Deep Convolutional Denoising for Tomography
-
REDAEPRobust and Enhanced Denoising Autoencoding Prior for Sparse-View CT Reconstruction
-
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
-
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
-
WeaveNetInterweaving Network: A Novel Monochromatic Image Synthesis Method for a Photon-Counting Detector CT System
-
ℓ₀DLLimited-Angle X-Ray CT Reconstruction Using Image Gradient ℓ₀-Norm With Dictionary Learning
-
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
-
Deep-MARFast Enhanced CT Metal Artifact Reduction Using Data Domain Deep Learning
-
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
-
GAN-CIRCLECT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)
-
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
-
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
-
SPULTRALow-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models
-
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
-
VVBP-tSVDVVBP-Tensor in the FBP Algorithm: Its Properties and Application in Low-Dose CT Reconstruction
-
X2CT-GANReconstructing CT from Biplanar X-Rays with Generative Adversarial Networks
-
3D-DLA3D Deep Learning Angiography (3D-DLA) from C-arm Conebeam CT
-
3pADMMOptimizing a parameterized plug-and-play ADMM for iterative low-dose CT reconstruction
-
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
-
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
-
NLCTFNon-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction
-
Noise2NoiseLearning Image Restoration without Clean Data
-
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 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
-
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
-
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
-
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
-
K-Edge Imaging in X-Ray Computed Tomography Using Multi-Bin Photon Counting Detectors
-
Compressed sensing
-
Efficient Monte Carlo Based Scatter Artifact Reduction in Cone-Beam Micro-CT
-
K-SVDAn Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
-
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
-
Scatter Correction Method for X-Ray CT Using Primary Modulation: Theory and Preliminary Results
-
Respiratory Correlated Cone Beam CT
-
An Algorithm for Total Variation Minimization and Applications
-
ISTAAn iterative thresholding algorithm for linear inverse problems with a sparsity constraint
-
OS-SARTOrdered-subset simultaneous algebraic reconstruction techniques (OS-SART)
-
Statistical Image Reconstruction for Polyenergetic X-Ray Computed Tomography
-
Theoretically Exact Filtered Backprojection-Type Inversion Algorithm for Spiral CT
-
An Iterative Maximum-Likelihood Polychromatic Algorithm for CT
-
Cone-Beam Computed Tomography with a Flat-Panel Imager: Magnitude and Effects of X-Ray Scatter
-
Spatial resolution properties of penalized-likelihood image reconstruction: space-invariant tomographs
-
A cone-beam reconstruction algorithm using shift-variant filtering and cone-beam backprojection
-
A general cone-beam reconstruction algorithm
-
GGMRFA generalized Gaussian image model for edge-preserving MAP estimation
-
ICDA local update strategy for iterative reconstruction from projections
-
ROFNonlinear total variation based noise removal algorithms
-
Mathematical framework of cone beam 3D reconstruction via the first derivative of the Radon transform
-
Reduction of CT artifacts caused by metallic implants
-
Image Reconstruction from Cone-Beam Projections: Necessary and Sufficient Conditions and Reconstruction Methods
-
FDKPractical cone-beam algorithm
-
SARTSimultaneous Algebraic Reconstruction Technique (SART): A superior implementation of the ART algorithm
-
An Inversion Formula for Cone-Beam Reconstruction
-
MLEMMaximum Likelihood Reconstruction for Emission Tomography
-
Optimal short scan convolution reconstruction for fan beam CT
-
Correction for beam hardening in computed tomography
-
A Method for Correcting Bone Induced Artifacts in Computed Tomography Scanners
-
Energy-Selective Reconstructions in X-Ray Computerised Tomography
-
Shepp-LoganThe Fourier reconstruction of a head section
-
SIRTIterative methods for the three-dimensional reconstruction of an object from projections
-
Three-dimensional Reconstruction from Radiographs and Electron Micrographs: Application of Convolutions instead of Fourier Transforms
-
ARTAlgebraic Reconstruction Techniques (ART) for three-dimensional electron microscopy and X-ray photography
-
Representation of a Function by Its Line Integrals, with Some Radiological Applications
-
Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten (Johann Radon)
No paper matches these filters.
Data says what a release contains: _proj + image_ has raw projections and a reference volume, so a forward model can be scored end to end; _projections_ has raw data only; _image_ is image-domain only and cannot test a projection model.
Research groups and labs whose papers, software or data appear in this directory. Each card links the group's homepage and the works of theirs already in the list.
North America
-
WANG-AXIS Lab / Biomedical Imaging Center (BIC) WANG-AXIS 实验室 / 生物医学成像中心
The WANG-AXIS Lab and the Biomedical Imaging Center (BIC) at Rensselaer Polytechnic Institute, directed by Ge Wang, work on x-ray CT imaging theory, systems, and reconstruction. The group's earlier work includes spiral cone-beam CT reconstruction and interior tomography, and its recent work applies deep learning to tomographic reconstruction, including the FORCE framework (2025), which combines a Poisson flow generative prior with data fidelity for reconstruction under low-dose, sparse-view, and metal-artifact conditions. The center also operates CT hardware that includes a photon-counting (MARS) spectral micro-CT scanner and a grating-based x-ray phase-contrast imaging platform.
In this library: 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.
-
X-ray Tomographic Imaging Group X 射线断层成像课题组
The X-ray tomographic imaging group in the Department of Radiology at the University of Chicago, led by Xiaochuan Pan and Emil Y. Sidky, develops reconstruction algorithms and imaging systems for clinical, preclinical, security, and synchrotron CT. The group is known for constrained total-variation (TV) minimization reconstruction — the ASD-POCS algorithm of Sidky & Pan (2008) — which minimizes image TV subject to data constraints and reconstructs cone-beam CT images from sparse or limited-angle data. Recent work from the group includes directional TV algorithms, one-step spectral CT reconstruction, and reconstruction from truncated projection data.
In this library: 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.
-
I-STAR Lab (Imaging for Surgery, Therapy, and Radiology) I-STAR 实验室
The I-STAR Lab (Imaging for Surgery, Therapy, and Radiology) at Johns Hopkins University, led by Jeffrey Siewerdsen, J. Webster Stayman, and Wojciech Zbijewski, works on cone-beam CT (CBCT) systems and 3D reconstruction for image-guided interventions. Its research includes penalized-likelihood (model-based) reconstruction, known-component reconstruction for imaging in the presence of implants and other known objects, and physics modeling of effects such as x-ray scatter, detector lag, and system geometry. Early work from lab members quantified the magnitude and effects of x-ray scatter in flat-panel CBCT (Siewerdsen & Jaffray, 2001) and Monte Carlo-based scatter correction in cone-beam micro-CT (Zbijewski & Beekman, 2006).
In this library: 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.
-
Fessler Group Fessler 课题组
Jeffrey Fessler's group at the University of Michigan works on statistical (model-based) image reconstruction and the optimization algorithms behind it, for CT as well as PET, SPECT, and MRI. Fessler & Rogers (1996) analyzed the spatial resolution properties of penalized-likelihood reconstruction for space-invariant tomographs, work that underlies later methods for designing regularizers with controlled resolution properties. The group maintains the Michigan Image Reconstruction Toolbox (MIRT) in MATLAB and Julia (MIRT.jl) versions, and the PyTorch-based MIRTorch toolbox for differentiable image reconstruction.
In this library: 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).
-
CT Clinical Innovation Center CT 临床创新中心
The CT Clinical Innovation Center at Mayo Clinic, founded in 2004, develops and evaluates new CT technology, clinical applications, and dose-reduction techniques, with core faculty including Cynthia McCollough and Lifeng Yu. Its work covers dual- and multi-energy CT and photon-counting-detector CT, reviewed in McCollough et al. (2015). The center also created public data resources for the CT research community: the 2016 AAPM Low-Dose CT Grand Challenge, and the Low Dose CT Image and Projection Data library (LDCT-and-Projection-data) hosted at The Cancer Imaging Archive, which provides routine-dose and simulated low-dose projection data together with full-dose images for 299 patient exams of the head, chest, and abdomen.
In this library: 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.
-
CT Research Group (UW CT Lab) UW CT 实验室
Guang-Hong Chen's CT research group at the University of Wisconsin–Madison works on reconstruction methods for low-dose and undersampled CT. Chen introduced Prior Image Constrained Compressed Sensing (PICCS), which uses a prior image as a constraint to reconstruct dynamic CT images from highly undersampled projection data, and PICCS has been applied to cardiac imaging, perfusion imaging, dual-energy CT, and C-arm CT. The group also works on spectral and photon-counting detector CT; UW–Madison was the first U.S. clinical evaluation site for GE Healthcare's silicon-based photon-counting CT system, with first human scanning in November 2022.
In this library: 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)
-
Bouman Group Bouman 课题组
Charles Bouman's group at Purdue University works on model-based iterative reconstruction (MBIR) for X-ray CT, formulating reconstruction as statistical estimation with explicit forward models and Markov-random-field priors. The MBIR statistical reconstruction work carried out with Ken Sauer (University of Notre Dame) in the 1990s was later commercialized by GE Healthcare as the Veo MBIR product for low-dose clinical CT. The group maintains SVMBIR, an open-source implementation of MBIR for parallel- and fan-beam CT, and has extended model-based methods to distributed and multi-node reconstruction.
In this library: 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.
-
LEAP Project, Lawrence Livermore National Laboratory 劳伦斯利弗莫尔国家实验室 LEAP 项目
The LEAP project at Lawrence Livermore National Laboratory provides a library of 3D transmission CT algorithms — pre-processing, forward/back projectors, and analytic (FBP/FDK) and iterative reconstruction — with Python and C++ APIs and PyTorch bindings that make the projectors differentiable for integration into deep-learning pipelines. The projectors support multiple geometries on multi-GPU and multi-core CPU hardware and are aimed at industrial and laboratory CT. Physics-based modeling and correction (scatter, beam hardening, dual-energy decomposition) is provided through the companion XrayPhysics package, a C/C++ library with Python bindings for X-ray cross sections and source-spectra modeling.
In this library: 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.
-
APS Tomography Group 先进光子源断层成像团队
The tomography group at the Advanced Photon Source of Argonne National Laboratory develops the reconstruction and data-handling software used at synchrotron micro- and nano-tomography beamlines. Its software output includes TomoPy, a Python framework for pre-processing and reconstructing synchrotron tomographic data (Gürsoy, De Carlo et al., 2014), and TomocuPy, Viktor Nikitin's GPU pipeline that reconstructs data in concurrent chunks. The group also curates TomoBank, a repository of experimental and simulated tomography datasets with descriptors, provided so that reconstruction methods can be developed and compared on shared beamline data.
In this library: TomoPy · TomoBank
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.
Europe
-
Pattern Recognition Lab (LME) 模式识别实验室
The Pattern Recognition Lab at FAU Erlangen-Nürnberg, headed by Andreas Maier since 2015, applies machine learning to medical imaging, with a CT line built around known operators — embedding the physics of projection and reconstruction as differentiable layers inside neural networks. Its software output includes PYRO-NN, a library of differentiable CT operators for parallel, fan, and cone-beam geometries, and diffct, a CUDA-accelerated differentiable CT library whose later versions support non-circular and arbitrary acquisition trajectories. A DFG-funded project, "Deep-Learning-basierte CBCT-Rekonstruktion beliebiger Trajektorien" (36 months, 324,459 EUR, announced September 2025), develops deep-learning-based CBCT reconstruction for arbitrary trajectories with partners at TH Deggendorf and Stanford University, using FAU's RRZE computing infrastructure.
In this library: diffct · PYRO-NN
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.
-
Computational Image Analysis (CIA) Group 剑桥大学计算图像分析组
The Computational Image Analysis group at Cambridge works on mathematical imaging and inverse problems, with a dedicated CT remit on closing the gap between mathematical advances and clinical practice. Its tomographic work covers data-driven reconstruction methods — including iterative unrolled methods, learned regularizers, and Plug-and-Play approaches — alongside variational regularization, Krylov subspace solvers, and 4D imaging. The group develops LION (Learned Iterative Optimization Networks), an open software package that collects post-processing, unrolled, learned-regularizer, Plug-and-Play and self-supervised CT reconstruction methods with reproducible training pipelines and CT datasets.
In this library: 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.
-
Computational Imaging Group 阿姆斯特丹 CWI 计算成像组
The Computational Imaging group at CWI develops mathematics- and algorithm-driven methods for 3D imaging across science, medicine and industry, combining advanced acquisition, parameter estimation and discrete tomography — including real-time reconstruction via RECAST3D and the in-house FleX-ray lab that couples a custom CT scanner to advanced processing and AI algorithms. It jointly developed the ASTRA Toolbox with the University of Antwerp — a MATLAB/Python platform of high-performance GPU primitives for 2D and 3D tomography used by researchers, industrial partners, and large synchrotrons (ESRF, APS) — and tomosipo, its Pythonic wrapper for 3D tomography with complex geometries, aimed at pairing fast GPU compute with modern deep learning tools.
In this library: ASTRA Toolbox · tomosipo · FleX-ray Walnut CBCT Collection (CWI)
ASTRA Toolbox (astra-toolbox.com) and tomosipo; RECAST3D for real-time visualization of tomographic imaging.
-
imec-Vision Lab (ASTRA Group) 安特卫普大学 imec 视觉实验室（ASTRA 组）
The ASTRA research group of imec-Vision Lab develops tomographic reconstruction algorithms for electron, X-ray, neutron and magnetic resonance imaging, focusing on the general reconstruction problems shared across application fields rather than any single application. Key expertises include phase-contrast X-ray CT, X-ray tomography for non-standard acquisition geometries (e.g., laminography, tomosynthesis), dynamic (4D) tomography and discrete tomography. The group developed the ASTRA Toolbox — an open-source, GPU-accelerated software platform for developing novel tomographic reconstruction methods — jointly with CWI's Computational Imaging group.
In this library: ASTRA Toolbox
ASTRA Toolbox (developed jointly with CWI); DART-related discrete tomography methods.
-
Mathematical Imaging Group (Ozan Öktem) 瑞典皇家理工学院 Öktem 课题组
Ozan Öktem's research at KTH combines inverse problems, convex optimization and machine learning for medical imaging, with a line of work on learned iterative (unrolled) reconstruction schemes. Together with Jonas Adler he introduced the Learned Primal-Dual reconstruction method (IEEE T-MI 2018) and the ODL (Operator Discretization Library) Python library for inverse problems, which provides forward operators, geometries and analytical methods used by many subsequent CT reconstruction studies. The learned gradient tomography codebase implements the earlier iterative deep network work (Adler & Öktem, "Solving ill-posed inverse problems using iterative deep neural networks", Inverse Problems 2017).
In this library: ODL · Learned Primal-Dual · Learned Gradient Tomography
ODL (odlgroup/odl) — operator discretization library for inverse problems; learned primal-dual and learned gradient tomography implementations.
-
CREATIS Tomographic Reconstruction Group 里昂 CREATIS 层析重建组
Simon Rit's group at CREATIS works on tomographic reconstruction with a focus on cone-beam CT for image-guided radiotherapy, including regularized and sparse (compressive-sensing) CT methods and motion-compensated X-ray CT for cardiac and respiratory motion — methods that were transferred to a clinical solution in the Netherlands. The group develops the Reconstruction Toolkit (RTK, openrtk.org), an open-source cone-beam CT reconstruction toolkit built on ITK with GPU code from Plastimatch, released under Apache 2.0 and distributed both as C++ classes/command-line tools and as Python packages (itkrtk) via PyPI. RTK supports Elekta, Varian and IBA scanner inputs, CPU/GPU Feldkamp-David-Kress reconstruction, 4D algorithms such as 4D ROOSTER, and cylindrical-detector geometries used by diagnostic CT scanners.
In this library: RTK
RTK (Reconstruction Toolkit; GitHub: RTKConsortium/RTK, PyPI: itkrtk) — open-source cone-beam CT reconstruction toolkit built on ITK.
-
Tomographic Imaging / Core Imaging Library (CIL) Group
The Tomographic Imaging group at the UK Science and Technology Facilities Council (STFC), working with partners including the University of Manchester through the Collaborative Computational Project in Tomographic Imaging (CCPi), develops open-source software for tomographic image reconstruction. Its Core Imaging Library (CIL) is a Python framework for iterative, regularised reconstruction of datasets where filtered back-projection is insufficient, such as noisy, incomplete, non-standard-geometry or multichannel (dynamic and spectral) data. The group also works on synergistic imaging, combining tomography with other modalities and data sources in joint reconstruction problems.
In this library: 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.
Asia
-
BISPL (Bio Imaging & Signal Processing Lab)
The Bio Imaging & Signal Processing Lab (BISPL) at KAIST, led by Jong Chul Ye, works on signal processing and machine learning methods for biomedical imaging. A central research line uses diffusion and score-based generative models as priors for imaging inverse problems, including sparse-view and limited-angle CT; its DiffusionMBIR method combines a pre-trained 2D diffusion model with model-based iterative reconstruction and a total-variation prior along the slice direction to solve 3D reconstruction problems such as sparse-view and limited-angle tomography.
In this library: Solving 3D Inverse Problems Using Pre-Trained 2D Diffusion Models
-
CT Imaging Group, Department of Engineering Physics 清华大学工程物理系 CT 成像组
The CT imaging group in Tsinghua University's Department of Engineering Physics, led by Yuxiang Xing and Li Zhang, studies X-ray CT imaging systems and image reconstruction methods. Its research covers tomographic reconstruction algorithms, photon-counting and spectral (multi-energy) CT, and fixed multi-source CT system designs in which source arrays replace gantry rotation. The group is based in the department's Key Laboratory of Particle & Radiation Imaging (Ministry of Education).
-
CT Reconstruction Groups (Chen / Luo) 东南大学 CT 重建组（陈阳 / 罗守华）
This entry covers two Southeast University groups working on CT reconstruction in Nanjing: Yang Chen's group at the School of Computer Science and Engineering (Laboratory of Image Science and Technology) and Shouhua Luo's group at the School of Biological Science and Medical Engineering. Their research addresses low-dose CT reconstruction and image enhancement under non-ideal measurement conditions such as sparse views and limited angles. In 2026 the CSE-led team published TAMP, an imaging foundation model pre-trained on physics-simulated data for universal enhancement of non-ideal measurement CT, in Nature Communications.
In this library: 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).
-
XMed Lab XMed 实验室
The XMed Lab at the Hong Kong University of Science and Technology, led by Xiaomeng Li in the Department of Electronic and Computer Engineering, works on medical AI and computer vision. Its DeepSparse model (IEEE Transactions on Medical Imaging, 2026) is a foundation model for sparse-view cone-beam CT reconstruction: it is pre-trained on a large-scale abdominal CT dataset (AtlasAbdomenMini, ~7k volumes) and can be fine-tuned to diverse anatomical targets with few labelled examples.
In this library: 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.