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.
Sparse-view computed tomography (CT) reduces radiation dose and acquisition time but remains severely ill-posed because incomplete projections poorly constrain null-space information. Existing learnin...
arXiv:2609.13688v1 Announce Type: cross
Abstract: Sparse-view computed tomography (CT) reduces radiation dose and acquisition time but remains severely ill-posed because incomplete projections poorly...
By Song Ni, Haijun Yu, Haodong Li, Changsheng Fang, Shuyi Fan, Yixing Huang, Hengyong Yu
The paper presents a Deep Operator Network that reconstructs full‑field 4D mitral regurgitation hemodynamics from sparse planar velocity data and boundary pressure traces. The network is pretrained on a URANS database of eleven orifice phantoms and then fine‑tuned on new cases, achieving rapid predictions in minutes. While the adaptation improves flow topology in the observed plane, reconstruction error increases sharply with distance from that plane, limiting physical consistency in the surrounding volume.
By Jakob Marcel Hoffmann, Yosuke Hasegawa, Alexander Stroh
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.
By Jigang Duan, Jiayi Wang, Heran Wang, Ping Yang, Genwei Ma, Xing Zhao
arXiv:2609.15550v1 Announce Type: cross
Abstract: X-ray coronary angiography is the clinical gold standard for coronary artery disease during real-time cardiac interventions, but provides only 2D pro...
By Deyu Meng, Mojtaba Lashgari, Yiying Wang, Abhirup Banerjee
X-ray coronary angiography is the clinical gold standard for coronary artery disease during real-time cardiac interventions, but provides only 2D projections of inherently 3D vessels. Existing learnin...
arXiv:2609.37605v1 Announce Type: cross
Abstract: Supervised deep learning has advanced sparse-view tomographic reconstruction. However, conventional models, which typically map filtered back-project...
By AmirEhsan Khorashadizadeh, Benjam\'in B\'ejar
The paper introduces Anatomy-Informed Neural Networks (AINN), which embed soft and hard anatomical priors into the loss function and network architecture to prevent anatomically impossible predictions. AINN is applied to a clinical scenario of aortoiliac deformation caused by a guidewire, modeling vessel and wire dynamics in SE(3) and training with a Wasserstein-2 loss from 2D angiograms. The study verifies the kinematics and loss against ground truth but does not yet train a network, outlining future work to apply the model to real CT data.
By David P. Stonko
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.
By Linrui Dai, Jinxiu Liang, Chu Zhou, Mae Yamaguchi, Ryuya Takahashi, Imari Sato
arXiv:2610.20509v1 Announce Type: cross
Abstract: Generative super-resolution (SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing...
By Hairong Ni, Jiahe Huang, Minghao Guo, Wojciech Matusik, Rose Yu
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.
By Zihan Deng, Zhenzhen Gong, Yujie Liu, Jiaxin Gong, Dingkun Liu, Kexian Xie, Yixing Huang, Peng Jin, Tianye Niu, Zhisheng Wang, Fenglin Liu, Hongming Shan, Lequan Yu