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SpectralCTGaussians introduces a novel spectral CT reconstruction and basis material decomposition technique that employs 3D Gaussian splatting with per‑Gaussian material fractions and energy‑dependent basis functions. By jointly optimizing these parameters across all energy channels via a differentiable polychromatic forward model, the method achieves superior novel view synthesis and higher PSNR for spectral CT volume reconstruction compared to traditional and learning‑based baselines. It also provides one‑step material decomposition with direct RGB segmentation and recovers the photoelectric basis more accurately than existing pipelines.
The paper introduces a method for extremely sparse‑view computed tomography (xSV‑CT) that learns a pose‑aware diffusion prior from a single 2D projection per training sample. During inference, the diffusion process is guided by measured‑view fidelity, shared‑volume reprojection, and projection‑physics consistency to reconstruct 3D volumes from only 1, 4, or 8 views. Experiments on two public CT datasets show that this approach outperforms or matches baselines that rely on dense‑volume supervision, achieving superior novel‑view synthesis and competitive reconstruction quality.
Sparse-view computed tomography is a severely ill-posed inverse problem, where recent 3D Gaussian Splatting methods offer an efficient explicit representation for tomographic reconstruction. However, we find that projection-domain optimization can be misleading in this setting: the rendered projections may continue to improve while the reconstructed volume deteriorates.
arXiv:2610.09492v1 Announce Type: new Abstract: Volumetric material decomposition in multi-energy computed tomography requires a representation that organizes multiple three-dimensional material fiel...
arXiv:2606. 16212v1 Announce Type: cross Abstract: Sparse-view CT reduces radiation dose and scanning time by acquiring fewer projection views, but angular undersampling makes reconstruction severely ill-posed, causing streak artifacts, structural blurring, and loss of fine details.
arXiv:2607. 25576v1 Announce Type: new Abstract: Photoacoustic tomography (PAT) combines the optical absorption contrast of biological tissue with the spatial resolution of ultrasound, yet recovering the initial pressure distribution from sparse-view sensor measurements remains an ill-posed inverse problem.