arXiv AI
Sep 24

Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based Refinement

The paper presents a three‑stage framework to extend the field of view in dental cone‑beam CT scans that are limited by truncated detector configurations. First, an implicit neural representation estimates missing projection data; second, an iterative reconstruction generates a secondary volumetric image with better anatomical consistency; third, a fast diffusion model refines the image for enhanced quality. Experiments show reduced truncation artifacts and improved reconstruction of structures beyond the original field of view.

By Susanne Schaub, Florentin Bieder, Matheus L. Oliveira, Yulan Wang, Buyanbileg Sodnom-ish, Dorothea Dagassan-Berndt, Michael M. Bornstein, Philippe C. Cattin
arXiv Machine Learning
Sep 3

Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction

The paper introduces Conditional Diffusion Posterior Alignment (CDPA), a method that scales diffusion-based sparse‑view CT reconstruction to large 3D volumes by conditioning a 2D U‑Net diffusion model on an initial 3D reconstruction and enforcing data‑consistency alignment. CDPA addresses high memory demands, limited 3D training data, and slice‑wise inconsistencies, achieving state‑of‑the‑art performance on synthetic and real Cone Beam CT data. The authors also demonstrate that the same approach improves fast denoising U‑Nets, delivering near‑diffusion quality at a fraction of the computational cost.

By Luis Barba, Johannes Kirschner, Benjamin Bejar
arXiv AI
Aug 19

Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction

The paper introduces LiftXR, a geometry‑guided framework that first reconstructs a 3D anatomical layout from bi‑planar X‑ray images and then uses this layout to guide CT volume reconstruction. An anatomical parser refines the layout by analyzing the reconstructed CT, enabling region‑specific intensity calibration. Experiments on two public datasets show LiftXR surpasses recent X‑ray‑to‑CT methods and improves downstream segmentation performance.

By Yifei Wu, Yicheng Wu, Qiang Ma, Qi Chen, Renyang Gu, Xinyu Liu, Yongsheng Pan, Yong Xia