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.
By Reinout Vos, Saptarshi Neil Sinha, Michael Weinmann
The paper introduces $K$-NeAS, a scalable neural architecture for multi-material CT reconstruction that replaces separate material networks with a shared latent backbone and a differentiable $K$-material soft selector. It automates attenuation bounds using a Gaussian Mixture Model and adds a scheduled auxiliary floater loss to reduce geometric hallucinations in sparse-view settings. Evaluated on four clinical CBCT datasets, $K$-NeAS achieves higher 3D volumetric fidelity—up to a 1.88 dB PSNR gain over a single-material baseline—and shows improved robustness under extreme sparsity, outperforming baselines by up to 1.17 dB.
By Daksh K. Shah, Emmanouil Nikolakakis, Razvan Marinescu
arXiv:2505.08123v2 Announce Type: replace-cross
Abstract: Multi-material decomposition (MMD) enables quantitative reconstruction of tissue compositions in the human body, supporting a wide range of c...
By Qing Wu, Hongjiang Wei, Jingyi Yu, S. Kevin Zhou, Yuyao Zhang
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: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...
By Lifeng Xing, Dequan Jin, Kunpeng Bu, Peigeng He, Shihui Ying
arXiv:2609.24468v2 Announce Type: replace
Abstract: Three-dimensional (3D) multi-contrast magnetic resonance imaging (MCMRI) provides rich anatomical and quantitative information but requires long ac...
By Jingran Xu, Dong Liang, Hairong Zheng, Yuanyuan Liu, Yanjie Zhu
Sparse-view spectral computed tomography (CT) reconstructs energy-resolved attenuation volumes from limited projection views, requiring simultaneous handling of angular undersampling and spectral coup...
arXiv:2505. 17338v3 Announce Type: replace-cross Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings.
By Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri, Van Nguyen Nguyen, Terrence Chen, Ziyan Wu
arXiv:2610.24858v2 Announce Type: replace
Abstract: Reducing MRI acquisition time improves patient comfort, reduces motion artefacts, and increases scanner throughput. Clinical protocols often shorte...
By Aditya Rastogi, Ramon Leiser, Prateek Singh, Martha Foltyn-Dumitru, Martin Bendszus, Fabian Bamberg, Klaus Maier-Hein, Kai Schlamp, Alexander Radbruch, Gianluca Brugnara, Philipp Vollmuth
Dual-energy CT (DECT) exploits attenuation differences across different X-ray spectra to provide richer material information and has been widely used in medical imaging. While sparse-view acquisition can lower radiation exposure, it makes DECT material decomposition even more challenging, as the problem is nonlinear and ill-posed.
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.
By Pawel Tomasz Pieta, Rasmus Juul Pedersen, Sina Borgi, Jakob Sauer J{\o}rgensen, Jens Wenzel Andreasen, Vedrana Andersen Dahl
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...
By Pranav Poudel, Florence Dell'Aniello Picard, Nairouz Shehata, Fr\'ed\'eric Lavoie, Herve Lombaert