FaCT-GS is a new framework that accelerates Gaussian Splatting (GS) for X‑ray Computed Tomography (CT) reconstruction. By optimizing voxelization and rasterization pipelines, it achieves speeds more than four times faster than the current state‑of‑the‑art GS methods on 512×512 projections and over thirteen times faster on 2k projections. The improved voxelization also allows quick fitting of Gaussians to existing volumes, providing a compressed representation or a warm‑start prior for reconstruction.
arXiv:2609.10376v1 Announce Type: new Abstract: Sparse-view X-ray 3D reconstruction is essential for reducing radiation exposure, but recovering a density field from a handful of X-ray projections is...
arXiv:2609.22849v1 Announce Type: new Abstract: In novel view synthesis and Computed Tomography (CT) reconstruction with sparse-view X-ray imaging, insufficient angular coverage leads to structural a...
Sparse-view X-ray 3D reconstruction is essential for reducing radiation exposure, but recovering a density field from a handful of X-ray projections is severely ill-posed. Recently, 3D Gaussian Splatt...
arXiv:2610.09253v1 Announce Type: new Abstract: Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric info...
Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric information. We propose PhyDiCT, a training-free fram...
SparseTokenGS is a Gaussian-based, feed-forward method for sparse-view cone-beam CT reconstruction that uses a reusable token pool to maintain scan-wide context during decoding. Anchor Token Fusion and Point-Token Attention query this shared pool at anchors and query coordinates, preserving local Gaussian priors while enabling reconstruction in a single forward pass without per-scan optimization. Experiments on chest, dental, volcanic rock, and chicken datasets show that SparseTokenGS achieves superior reconstruction quality, achieving 29.51 dB PSNR and 0.842 SSIM from only six dental projections and maintaining separable tooth-and-pulp structures for root‑canal localization.
arXiv:2609.23380v1 Announce Type: new Abstract: Gaussian Splatting has enabled real-time novel view synthesis, but its tightly coupled geometry and appearance representation often require a large num...
Cone-beam computed tomography (CBCT) enables volumetric reconstruction from X-ray projections, but suffers from severe artifacts--especially beam hardening--when imaging materials with high attenuation such as metals. These artifacts arise from the polychromatic nature of X-rays and are not properly addressed by conventional monochromatic reconstruction algorithms.
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.
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.
Sparse-view cone-beam computed tomography is widely used for fast, low-dose volumetric imaging and lesion localization in dentistry and image-guided interventions, but it introduces severe artifacts....