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.
VoxelSynth3D is a training‑free 3D image‑domain framework that reduces metal artifacts in postoperative musculoskeletal CT by combining support masking, normalized tissue synthesis, deviation gating, and restricted edge refinement. The method preserves implant voxels while correcting surrounding tissue artifacts and was evaluated on a newly constructed Synthetic CLINIC‑Metal benchmark, showing a reduction in RMSE from 801.48 to 786.18 HU on 40 held‑out cases. Compared to a 3D Gaussian smoother, VoxelSynth3D achieved a 13.58 HU improvement and maintained clean-edge agreement beyond 5 mm from metal.
By Amritesh Banerjee, Abdul Basit, Renil Renji Joseph, Nouhaila Innan, Muhammad Shafique
The paper introduces a high‑resolution Rectified Flow Matching model trained on 256×256 chest CT images to serve as a generative prior for CT reconstruction. A two‑stage training strategy—initial strong anatomically informed augmentation followed by fine‑tuning—helps mitigate overfitting and improve structural fidelity. When evaluated on various CT inverse problems, Flow Matching‑based reconstruction methods outperform diffusion‑based algorithms in PSNR, SSIM, and perceptual quality while requiring fewer sampling steps.
By Davide Evangelista
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
The paper proposes a single diffusion model trained across multiple imaging domains to act as a reusable prior for computed tomography (CT) reconstruction. By keeping the model frozen, it is applied to three distinct datasets—flaw analysis in additively manufactured metal parts using cone‑beam X‑ray CT, and concrete microstructure imaging with parallel‑beam neutron CT—each differing in modality, geometry, material, and degradation. In all cases, the method outperforms analytic reconstructions, demonstrating its potential as a foundation plug‑and‑play prior for heterogeneous CT problems.
By Haley Duba-Sullivan, Patxi Fernandez-Zelaia, Obaidullah Rahman, Amirkoushyar Ziabari
arXiv:2603.26509v2 Announce Type: replace
Abstract: Computed tomography (CT) provides rich 3D anatomical detail but is often constrained by high radiation exposure, substantial costs, and limited ava...
By Martin Rath, Morteza Ghahremani, Yitong Li, Ashkan Taghipour, Marcus Makowski, Christian Wachinger