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

• URL: http://arxiv.org/abs/2608.16463v1
• Date: Mon, 17 Aug 2026 12:00:11 GMT
• Title: Shared-Structure 4D Spectral Gaussian Representation for Sparse-View Spectral CT Reconstruction
• Abstract summary: Sparse-view spectral computed tomography (CT) reconstructs energy-resolved attenuation volumes from limited projection views.<n>We propose a SharedStructure 4D Spectral Gaussian Representation (4D-SG) that learns shared Gaussian geometry from full spectrum structural projections.<n>Experiments on six synthesized, simulated projection, and real projection datasets with 50 views demonstrate the best average performance.
• Score: 15.681197404294762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse-view spectral computed tomography (CT) reconstructs energy-resolved attenuation volumes from limited projection views, requiring simultaneous handling of angular undersampling and spectral coupling. We propose a SharedStructure 4D Spectral Gaussian Representation (4D-SG) that learns shared Gaussian geometry from full spectrum structural projections and uses a Gaussian-wise Spectral Density Curve Network (GSC-Net) to predict Gaussian raw density transformations. This factorization separates shared spatial structure from spectral attenuation variation, avoids independent channel geometry optimization, and establishes a continuous 4D-SG representation from discrete spectral measurements for unobserved spectral channel queries. Experiments on six synthesized, simulated projection, and real projection datasets with 50 views demonstrate the best average performance. Compared with the strongest Gaussian baseline, 4D-SG improves PSNR from 35.56 dB to 36.61 dB, increases SSIM from 0.909 to 0.914, and reduces LPIPS from 0.208 to 0.194, demonstrating its effectiveness for sparse-view spectral CT reconstruction.

Related papers

• Phy-CoSF: Physics-Guided Continuous Spectral Fields Reconstruction and Super-Resolution for Snapshot Compressive Imaging [79.35520607451187]
  We propose Phy-CoSF, which synergizes deep unfolding networks with implicit neural representations.<n>Specifically, we propose a two-phase architecture that bridges discrete-wavelength training with continuous spectral rendering.
  arXiv  Detail & Related papers  (2026-05-13T14:17:02Z)
• Residual Gaussian Splatting for Ultra Sparse-View CBCT Reconstruction [14.00351191217297]
  This work integrates wavelet multi-resolution analysis with 3DGS to enhance sparse reconstruction.<n>We introduce a spectrally-decoupled Gaussian representation that stratifies the volumetric field into a geometric base component and a residual detail component.<n>Experiments on clinical datasets demonstrate that RGS enables the reconstructed images to capture highly refined geometric textures.
  arXiv  Detail & Related papers  (2026-04-30T07:59:18Z)
• MSGS: Multispectral 3D Gaussian Splatting [46.61006851229471]
  We present a multispectral extension to 3D Gaussian Splatting (3DGS) for wavelength-aware view synthesis.<n>Each Gaussian is augmented with spectral radiance, represented via per-band spherical harmonics, and optimized under a dual-loss supervision scheme combining RGB and multispectral signals.<n>Our method is evaluated on both public and self-captured real-world datasets, demonstrating consistent improvements over the RGB-only 3DGS baseline in terms of image quality and spectral consistency.
  arXiv  Detail & Related papers  (2026-04-14T23:03:38Z)
• Accelerating 4D Hyperspectral Imaging through Physics-Informed Neural Representation and Adaptive Sampling [4.125456588936474]
  We introduce a physics-informed neural representation approach that efficiently reconstructs dense spatially-resolved 2DIR hyperspectral images from sparse experimental measurements.<n>The proposed approach achieves high-fidelity spectral recovery using only $1/32$ of the sampling budget, as opposed to exhaustive sampling, effectively reducing total experiment time by up to 32-fold.
  arXiv  Detail & Related papers  (2026-04-08T01:22:09Z)
• Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition [51.56484100374058]
  This paper formulates pilot-limited channel estimation as low-rank tensor completion from sparse observations.<n>Experiments on synthetic channels demonstrate 10-20,dB normalized mean-square error (NMSE) improvement over least-squares (LS)<n> evaluations on DeepMIMO ray-tracing channels show 24-44% additional NMSE reduction over pure tensor-based methods.
  arXiv  Detail & Related papers  (2026-02-03T23:38:05Z)
• Calculating Vibronic Spectra with a linear algorithm based on Gaussian Boson Sampling [0.0]
  We implement the algorithm for simulating the pentacene molecule through three distinct approaches.<n>High fidelity $(F>0.999)$ was achieved between the simulated Franck-Condon profiles and analytically calculated profiles.
  arXiv  Detail & Related papers  (2025-08-05T22:18:47Z)
• CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis [69.02751635551724]
  Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding.<n> variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies.<n>We introduce CARL, a model for Camera-Agnostic Representation Learning across RGB, multispectral, and hyperspectral imaging modalities.
  arXiv  Detail & Related papers  (2025-04-27T13:06:40Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction [127.20208645280438]
  Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement. Modeling the inter-spectra interactions is beneficial for HSI reconstruction. Mask-guided Spectral-wise Transformer (MST) proposes a novel framework for HSI reconstruction.
  arXiv  Detail & Related papers  (2021-11-15T16:59:48Z)
• Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition [78.37855832568569]
  We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function. We show numerical and experimental results for spectral CT materials decomposition.
  arXiv  Detail & Related papers  (2021-03-25T15:20:10Z)
• Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data [0.0]
  This framework can generate swept-source OCT images using undersampled spectral data, without any spatial aliasing artifacts. It can be easily integrated with existing swept-source or spectral domain OCT systems. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral domain OCT systems.
  arXiv  Detail & Related papers  (2021-03-04T22:30:31Z)
• Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography [20.03088101097943]
  Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs) Due to the limited photons and counting rate in the corresponding spectral fraction, the reconstructed spectral images usually suffer from severe noise. In this paper, a fourth-order nonlocal tensor decomposition model for spectral CT image reconstruction (FONT-SIR) is proposed.
  arXiv  Detail & Related papers  (2020-10-27T15:14:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.