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Implicit Neural Representations

Implicit neural representations (INRs) represent the unknown CT volume as a coordinate-based neural network that maps a 3D (or 3D plus time) position to an attenuation value, and fit that network so that its simulated projections match the measured ones. The network is the image; reconstruction is per-scan optimisation rather than inference with a pre-trained model.

Because the representation is continuous and fitted to each scan, INRs need no training data and handle sparse-view, limited-angle and dynamic acquisitions naturally; multiresolution hash encodings make them fast enough for cone-beam volumes. Methods in this family are often called neural attenuation fields or NeRF-style X-ray methods.

Current work focuses on encodings and architectures that reduce over-smoothing or noise fitting, priors from pre-trained or diffusion models, motion fields for 4D-CT, polychromatic forward models for metal artifacts, and uncertainty quantification. Gaussian splatting is a closely related explicit alternative and has its own topic page.

Browse all 25 Implicit Neural Representations papers in the searchable directory

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Papers

All 25 papers, newest first.

PaperVenueYearLinks
DiffNR: Diffusion-Enhanced Neural Representation Optimization for Sparse-View 3D Tomographic ReconstructionAAAI2026paper · project
Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction of Neutron Computed Tomography DataICASSP2026paper
Dynamic Black-hole Emission Tomography with Physics-Informed Neural FieldsCVPR2026paper
Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based RefinementMICAD2026paper · code
GH-NAF: Grid-Adaptive Hash-Level-Attended Neural Attenuation Fields for Discrepancy-Aware CBCTCVPR2026paper · code
NAB: Neural Adaptive Binning for Sparse-View CT ReconstructionICLR2026paper · code
OX-NeRF: 3D X-ray Tomography Reconstruction from Sparse Views Using Implicit Neural Representationpreprint2026paper
Texture-preserving implicit neural representation for cone-beam CT truncated reconstructionpreprint2026paper
Accelerated Optimization of Implicit Neural Representations for CT ReconstructionISBI2025paper
Coordinate-Based Speed of Sound Recovery for Aberration-Corrected Photoacoustic Computed TomographyICCV2025paper · project
Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary TomographyTCI2025paper
Epi-NAF: Enhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar ConsistencyISBI2025paper
Nonperiodic dynamic CT reconstruction using backward-warping INR with regularization of diffeomorphism (BIRD)preprint2025paper
Recover Biological Structure from Sparse-View Diffraction Images with Neural Volumetric PriorICCV2025paper
Unsupervised Self-Prior Embedding Neural Representation for Sparse-View CT ReconstructionAAAI2025paper · code
INCODE: Implicit Neural Conditioning with Prior Knowledge EmbeddingsWACV2024paper · code
Implicit Neural Representations for Robust Joint Sparse-View CT ReconstructionTMLR2024paper · code
Neural Field Regularization by Denoising for 3D Sparse-View X-Ray Computed Tomography3DV2024paper
Structure-Aware Sparse-View X-Ray 3D ReconstructionCVPR2024paper · code
PINER: Prior-Informed Implicit Neural Representation Learning for Test-Time Adaptation in Sparse-View CT ReconstructionWACV2023paper · code
Unsupervised Polychromatic Neural Representation for CT Metal Artifact ReductionNeurIPS2023paper · code
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed TomographyTMLR2023paper
NAF: Neural Attenuation Fields for Sparse-View CBCT ReconstructionMICCAI2022paper · code
SNAF: Sparse-View CBCT Reconstruction with Neural Attenuation Fieldspreprint2022paper
Dynamic CT Reconstruction From Limited Views With Implicit Neural RepresentationsICCV2021paper

Browse all 25 Implicit Neural Representations papers in the searchable directory

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