Sparse-View CT Reconstruction

CT: Computed Tomography

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Overview

Sparse-view CT reconstruction studies how to recover a three-dimensional image from far fewer X-ray projections than conventional tomography uses, reducing scan time or radiation dose. Approaches range from iterative and unrolled methods that enforce the imaging physics to neural fields, Gaussian splatting, and generative priors such as diffusion and flow matching; self-supervised learning and adaptive view selection can help when ground truth or projection budgets are limited. Methods are evaluated on simulated and clinical data, including sparse-view and low-dose benchmarks, with image quality, projection consistency, and robustness to noise and acquisition geometry all important. A central tension is that strong learned priors can fill in missing detail but may invent anatomy, so current work focuses on balancing plausible structure with measurement consistency, while improving efficiency and generalization across scanners and sampling patterns.

Latest papers

CardsList

48 papers9 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

  1. Dual-Domain Sparse-View 4D CBCT Reconstruction with Spatial-Temporal Consistency Learning

    Oct 13, 2026Qi Lai, YuTong HeSparse-View 4D ReconstructionImage Reconstruction

  2. AD-GS: Anatomical Density-guided 3D Gaussian Splatting for Sparse-View Dental CBCT Reconstruction

    Oct 10, 2026Zonglin Li, Kexian Xie, Feiran Li +5Sparse-View CT Reconstruction3D Gaussian Splatting

    Beijing University of Posts and Telecommunications · Department of Stomatology, Peking University Third Hospital · Independent Researcher +2

  3. RePrior: Measurement-Reconciled Prior Correction for Sparse-View CBCT Reconstruction

    Oct 8, 2026Mengrui Yin, Tianxingjian Ding, Wufan Zhao +2Image ReconstructionSparse-View CT Reconstruction

    University of Central Florida · The Hong Kong University of Science and Technology (Guangzhou) · Tampere University +1

  4. OX-NeRF: 3D X-ray Tomography Reconstruction from Sparse Views Using Implicit Neural Representation

    Oct 8, 2026Thomas Welsch, Min-Hsin Tu, David J. Chapman +1Sparse-View CT ReconstructionNeural Radiance Fields

    Department of Engineering Science University of Oxford Oxford, United Kingdom

  5. Gradient-Based Trajectory Optimisation over Continuous Poses for Sparse-View Cone-Beam CT

    Oct 7, 2026Linda-Sophie Schneider, Simon Wittl, Gabriel Herl +1Trajectory OptimizationCone-Beam CT

    Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany · Deggendorf Institute of Technology, Germany

  6. PhyDiCT: Plug-and-Play CT Reconstruction from Sparse X-Rays via Differentiable Rendering and Strong Priors

    Oct 7, 2026Weicheng Dai, Shantanu Ghosh, Kayhan BatmanghelichSparse-View CT Reconstruction3D Medical Imaging

    Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA

  7. Geometry-Aware Diffusion Approximate Posterior Sampling for Sparse-View and Limited-Angle CT

    Oct 5, 2026Honglei Brinkmann, Carola-Bibiane Schönlieb, Ander BiguriSparse-View CT ReconstructionComputed Tomography

    Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

  8. Consecutive Posterior Fusion for Diffusive Recovery of Unobservable Image Structures

    Oct 2, 2026Elena Morotti, Davide Evangelista, Elena Loli PiccolominiDiffusion-Based Inverse ProblemsImage Inverse Problems

    Department of Computer Science and Engineerin University of Bologna Bologna, 40126, Italy

  9. TomoTransformer: Towards a Foundation Model for CT Reconstruction

    Sep 29, 2026AmirEhsan Khorashadizadeh, Benjamín BéjarMedical Imaging Foundation ModelsImage Reconstruction

    Swiss Data Science Center (SDSC) in Paul Scherrer Institute (PSI), Villigen, Switzerland.

  10. LINGO: Latent Initialization and Gradient Optimization for Sparse-view X-ray Novel View Synthesis and CT Reconstruction with 3D Gaussian Splatting

    Sep 19, 2026Lifeng Xing, Dequan Jin, Kunpeng Bu +2Sparse-View 3D ReconstructionNovel View Synthesis

    School of Mathematics and Center for Applied Mathematics of Guangxi, Guangxi University, Nanning 530004, China · Department of Endocrinology, Metabolism and Nephrology, Guangxi Medical University Cancer Hospital, Nanning 530021, China · School of Mechanics and Engineering Science and Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai 200072, China

  11. SONAR: A Structure-Consistent Neural Operator for Null-Space-Aware Sparse View CT Reconstruction

    Sep 12, 2026Song Ni, Haijun Yu, Haodong Li +4Sparse-View CT ReconstructionImage Inverse Problems

    Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854

  12. Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstruction

    Sep 9, 2026Pranav Poudel, Florence Dell'Aniello Picard, Nairouz Shehata +23D Gaussian Splatting3D Medical Imaging

    Polytechnique Montréal, Canada · Mila - Quebec AI Institute, Canada · CHUM - University of Montreal Hospital, Canada

  13. Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction

    Aug 18, 2026Yifei Wu, Yicheng Wu, Qiang Ma +5Medical Image AnalysisComputed Tomography

    School of Computer Science, Northwestern Polytechnical University, Xi’an, China · Imperial College London, London, UK · Department of Brain Sciences, Imperial College London, London, UK +3

  14. Shared-Structure 4D Spectral Gaussian Representation for Sparse-View Spectral CT Reconstruction

    Aug 17, 2026Jiancheng Fang, Shaoyu Wang, Wenjun Xia +2Sparse-View CT ReconstructionComputed Tomography

    School of Information Engineering, Nanchang University, Nanchang 330031, China · School of Computer Science and Engineering, Southeast University, Nanjing 210096, China · Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Laboratory of Image Science and Technology, and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education

  15. TR-GS: High-Fidelity Sparse-View CT Volumetric Rendering via t-Distribution Gaussian Splatting and Ray-Confidence Modeling

    Aug 17, 2026Zedong Xiao, Yiren Wang, Zhou Liu +23D Medical ImagingSparse-View CT Reconstruction

    Shenzhen University Shenzhen, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen, China

  16. Feed-Forward Hierarchical Gaussian Diffusion for Extreme CT Reconstruction

    Aug 15, 2026Yuezhe Yang, Li ChengComputed TomographySparse-View CT Reconstruction

    Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada · School of Computer Science, University of Sydney, Sydney, New South Wales, 2006, Australia

  17. CG-GLORE: A Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction

    Aug 15, 2026Tran Xuan Hieu Le, Doanh C. Bui, Vu Trung Duong Le +5Sparse-View CT ReconstructionComputed Tomography

    Nara Institute of Science and Technology Nara, Japan · University of Information Technology Ho Chi Minh City, Vietnam · Vietnam National University Ho Chi Minh City, Vietnam +2

  18. Revisiting Pose Sensitivity in Splat-based Computed Tomography under Sparse-view Reconstruction

    Aug 5, 2026Kiseok Choi, Hyeongjun Cho, Inchul Kim +13D Gaussian SplattingCone-Beam CT

    KAIST

  19. Agentic Autoresearch for CT Reconstruction

    Jul 24, 2026Andreas Maier, Lucas Kachelriess, Siming Bayer +4AI for ScienceSparse-View CT Reconstruction

    Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany · Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany · Universitätsklinikum Erlangen (UKER), Erlangen, Germany +2

  20. KK-NeAS: Scalable Multi-Material CT Reconstruction Using Neural SDFs

    Jul 15, 2026Daksh K. Shah, Emmanouil Nikolakakis, Razvan MarinescuCone-Beam CTImplicit Neural Representations

    University of California, Santa Cruz

  21. Posterior Variance Is a Constraint Map, Not an Error Map: Closed-Form Uncertainty for Radiative Gaussian Splatting in Sparse-View CT

    Jul 15, 2026Chulin Zhao, Yiran Xu, Shu Liu3D Gaussian SplattingUncertainty Quantification

    1Dundee International Institute, Central South University · 2Central South University

  22. Exact and Calibrated Diffusion Reconstruction for Digital Breast Tomosynthesis

    Jul 14, 2026Imade BouftiniImage ReconstructionSparse-View CT Reconstruction

  23. Design Choices in Splitting-Based Self-Supervised Sparse-View CT Reconstruction

    Jul 12, 2026Nadja Gruber, Lukas Neumann, Ander Biguri +3Self-Supervised LearningSparse-View CT Reconstruction

    Department of Computer Science, University of Innsbruck, Austria. · Institute of Basic Sciences in Engineering Science, University of Innsbruck, Austria. · Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK. +3

  24. Quantum Compressed Sensing CT Reconstruction Algorithm Based on Penalized Weighted Least Squares and Guided Total Variation

    Jul 12, 2026Yuwen Zhang, Yujie Liu, Ao Wang +4Sparse-View CT ReconstructionQuadratic Unconstrained Binary Optimization

    Department of Medical Imaging Technology, Peking University Health Science Center, Beijing, China

  25. Robustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT under Challenging Acquisition Settings

    Jul 10, 2026Chengze Ye, Linda-Sophie Schneider, Yipeng Sun +5Cone-Beam CTImage Reconstruction

    Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

  26. SAVER: Stochastic Adaptive Variance-Driven Exploration and Reconstruction for Low-Dose Computed Tomography

    Jul 4, 2026Shunta Nonaga, Koji Tabata, Junya Honda +3Sparse-View CT ReconstructionAdaptive Sampling

    Graduate School of Chemical Sciences and Engineering, Hokkaido University, Kita 13 Nishi 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan. · Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Kita 20 Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan · Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan. +7

  27. A Dual-domain Refinement Network with FBP-based Jacobian Learning for Sparse-view Dual-Energy CT Material Decomposition

    Jun 29, 2026Qian Liu, Xiaohong Fan, Ke Chen +3Sparse-View CT ReconstructionOperator Learning

    School of Mathematics and Computational Science, Xiangtan University, National Center for Applied Mathematics in Hunan, Key Laboratory for Intelligent Computing and Information Processing of the Ministry of Education, Xiangtan 411105, China · College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China · Department of Mathematics and Statistics, University of Strathclyde, G1 1XH Glasgow, U.K. +1

  28. Enabling self-supervised learned primal dual with Noise2Inverse

    Jun 25, 2026Antti Sällinen, Siiri Rautio, Santeri Kaupinmäki +1Self-Supervised LearningImage Reconstruction

    Research Unit of Mathematical Sciences, University of Oulu, Finland · Department of Mathematics and Information Science, Josai University, Japan · Department of Computer Science, University College London, United Kingdom

  29. Multilevel Stochastic Plug-and-Play for Sparse-View CT Reconstruction

    Jun 23, 2026Antoine De Paepe, Alexandre Bousse, Dimitris VisvikisImage ReconstructionSparse-View CT Reconstruction

    LaTIM, Inserm, UMR 1101, Université de Bretagne Occidentale, Brest, France

  30. Quantum CT via Dynamic Interval Encoding and Prior-Balanced QUBO Reconstruction

    Jun 23, 2026Ao Wang, Yikuang Yuluo, Yujie Liu +7Image ReconstructionSparse-View CT Reconstruction

    Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China · Key Lab of Optoelectronic Technology and Systems and the Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China · Pattern Recognition Lab (LME), Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Martensstraße 3, 91058 Erlangen, Germany