发表机构
University of Basel; University of Campinas; Wuhan University（巴塞尔大学; 坎皮纳斯大学; 武汉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对牙科CBCT视野截断问题，提出结合隐式神经表示、迭代重建和扩散模型的三阶段框架，有效减少伪影并扩展视野，提升图像质量。
AI 中文摘要
牙科锥形束计算机断层扫描（CBCT）系统通常采用探测器配置，提供截断的视野（FOV），仅能捕获患者解剖结构的一小部分。在本工作中，我们旨在利用截断视野扫描的投影数据重建扩展视野。为此，我们提出一个三阶段框架，包括：（1）隐式神经表示（INR），用于估计截断投影数据中缺失的部分；（2）迭代重建，用于生成具有改进解剖一致性的次级体积图像；（3）快速扩散模型，用于图像增强。所提出的方法在统一的截断CBCT成像流程中结合了连续表示、基于物理的重建和生成式细化的优势。实验结果表明，该方法有效减少了截断伪影，改善了超出原始视野结构的重建，并生成了质量更高的图像。我们的代码公开于https://github.com/SusanneSchaub/CBCT-FOV-Extension。
英文摘要
Dental cone-beam computed tomography (CBCT) systems often employ detector configurations that provide a truncated field of view (FOV) that only captures a small part of the patient's anatomy. In this work, we aim to reconstruct an extended FOV using projections of truncated FOV scans. To this end, we propose a three-stage framework that consists of (1) an implicit neural representation (INR) for estimating missing parts of the truncated projection data, (2) an iterative reconstruction for generating a secondary volumetric image with improved anatomical consistency and (3) a fast diffusion model for image enhancement. The proposed approach combines the strengths of continuous representations, physics-based reconstruction and generative refinement within a unified pipeline for truncated CBCT imaging. Experimental results demonstrate that the method effectively reduces truncation artifacts, improves the reconstruction of structures extending beyond the original FOV and produces images with enhanced quality. Our code is publicly available at https://github.com/SusanneSchaub/CBCT-FOV-Extension.
CommentsAccepted at MICAD 2026
