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

2026-09-23

AI Summary

本文针对牙科锥形束CT视场局限问题，通过隐式神经表示、迭代重建及扩散模型增强三阶段方法扩展视场，减少截断伪影，提高图像质量。

Abstract

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.

Problem

• Dental CBCT
• Field-of-View (FOV)
• Truncated Projections
• Image Reconstruction
• Anatomical Consistency

Innovation

• Implicit Neural Representations
• Iterative Reconstruction
• Diffusion Model
• Field-of-View Extension
• Truncated CBCT