Du Xin, Chen Yi, Zhao Jun, Xi Yan
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2615-2618. doi: 10.1109/EMBC.2018.8512732.
Dental panoramic radiography (DPR), a widely used medical examination method, has its intrinsic weakness in high requirement to the positioning of patient. Although positioning devices like chin support can provide a relatively stable and guaranteed environment for exposure, problems including morphological differences of jaw between patients and their improper standing postures still put the reconstructed image at high risk of getting blurred, especially in the anterior segment of dental arch. This paper proposes a novel method based on convolutional neural network (CNN) to estimate the positioning error of patient's dental arch, and thereby reconstruct the panoramic image with the corrected dental curvature, so that the blur gets reduced. Experiment results demonstrate the method's effectiveness in providing reconstructed images of stable quality for further diagnosis.
牙科全景X线摄影（DPR）是一种广泛应用的医学检查方法，但其对患者定位要求较高，存在固有缺陷。尽管像下巴支撑等定位装置可为曝光提供相对稳定且有保障的环境，但患者之间颌骨形态差异以及站立姿势不当等问题，仍使重建图像极易模糊，尤其是在牙弓前段。本文提出一种基于卷积神经网络（CNN）的新方法，用于估计患者牙弓的定位误差，进而通过校正牙齿曲率来重建全景图像，以减少模糊。实验结果表明，该方法能有效提供质量稳定的重建图像用于进一步诊断。
