González-López Antonio, Campos-Morcillo Pedro
Hospital Universitario Virgen de la Arrixaca, ctra. Madrid-Cartagena s/n, 30120 El Palmar (Murcia), Spain.
Hospital Universitario Virgen de la Arrixaca, ctra. Madrid-Cartagena s/n, 30120 El Palmar (Murcia), Spain.
Phys Med. 2017 Jun;38:59-65. doi: 10.1016/j.ejmp.2017.05.048. Epub 2017 May 12.
The number of verification portal images in radiotherapy has increased in the last years. On the other hand, radiation delivered during imaging is not confined to the treatment volumes, but also affects the surrounding organs and tissues. In order to reduce the overall radiation dose due to imaging, one approach would be to reduce the dose per image, but noise would increase and the quality of portal images would reduce. The limited quality of portal images makes it difficult to propose a reduction of dose if there is no way to effectively reduce noise. Denoising algorithms could be the solution if the quality of the restored image can match the image obtained with a standard dose. In this work the statistical properties of noise in a portal imaging system and the statistical properties of portal images are used to develop an efficient denoising method. The result is a method that minimizes the Stein's unbiased risk estimator (SURE) in the image domain over a parametric family of shrinkage functions operating in the wavelet domain. The presented denoising method shows a better performance than the adaptive Wiener estimator for different portal images and noise energies.
在过去几年中，放射治疗中验证门静脉图像的数量有所增加。另一方面，成像过程中所施加的辐射不仅局限于治疗体积，还会影响周围的器官和组织。为了减少由于成像导致的总体辐射剂量，一种方法是降低每张图像的剂量，但这样会导致噪声增加且门静脉图像质量下降。如果没有办法有效降低噪声，门静脉图像质量的限制使得难以提议降低剂量。如果恢复图像的质量能够与标准剂量获得的图像相匹配，去噪算法可能是解决方案。在这项工作中，利用门静脉成像系统中噪声的统计特性以及门静脉图像的统计特性来开发一种高效的去噪方法。结果得到一种方法，该方法在小波域中运行的收缩函数参数族上，使图像域中的斯坦无偏风险估计器（SURE）最小化。所提出的去噪方法在不同的门静脉图像和噪声能量情况下，表现出比自适应维纳估计器更好的性能。
