Kruger D G, Zink F, Peppler W W, Ergun D L, Mistretta C A
University of Wisconsin, Madison 53792.
Med Phys. 1994 Feb;21(2):175-84. doi: 10.1118/1.597297.
Single kernel scatter correction algorithms are based on the model that the scatter field can be predicted by convolution of the primary intensity (Iprim) with a spatially invariant scatter point-spread function (PSF). Practical limitations (Iprim unknown) suggest the substitution of the total detected intensity (Idet) for Iprim as the source image in the convolution. In regions of high scatter fraction (SF), Idet is a poor approximation of Iprim, thereby causing an overestimation of scatter originating in the region. This contributes to errors in estimating detected scatter in the mediastinum and neighboring regions. A technique using a regionally variable point-spread function that significantly reduces RMS error in estimation of the primary image as compared to the single PSF method is investigated. The regionally variable convolution method employs a larger PSF in the mediastinum and a smaller PSF in the lungs to reduce the error in estimating the scatter throughout the image. The method to allow for patient differences has also been expanded and various implementations of these methods have been compared. Results show that the dual-kernel algorithm is always more effective than an equivalent single-kernel algorithm. The dual-kernel algorithm using a predicted scatter fraction curve gives an overall RMS error in the primary of as low as 20.8% which is equivalent to 8.7% RMS error in the scatter. The dual-kernel method using a predicted scatter fraction curve approaches the accuracy of the single-kernel method using patient specific scatter measurements. Because using individual scatter measurements is a less desirable method for clinical use, we feel that the dual-kernel algorithm which uses two regions specific convolution kernels and a variable scatter fraction curve is the preferable method.
单内核散射校正算法基于这样一种模型，即散射场可通过原发射线强度（Iprim）与空间不变的散射点扩散函数（PSF）进行卷积来预测。实际限制（Iprim未知）表明，在卷积中用总检测强度（Idet）替代Iprim作为源图像。在高散射分数（SF）区域，Idet对Iprim的近似效果较差，从而导致对该区域产生的散射估计过高。这会导致在估计纵隔及相邻区域的检测散射时出现误差。研究了一种使用区域可变点扩散函数的技术，与单PSF方法相比，该技术可显著降低原图像估计中的均方根误差。区域可变卷积方法在纵隔中采用较大的PSF，在肺部采用较小的PSF，以减少整个图像散射估计中的误差。考虑患者差异的方法也得到了扩展，并比较了这些方法的各种实现方式。结果表明，双内核算法总是比等效的单内核算法更有效。使用预测散射分数曲线的双内核算法在原图像中的总体均方根误差低至20.8%，相当于散射中的均方根误差为8.7%。使用预测散射分数曲线的双内核方法接近使用患者特定散射测量的单内核方法的精度。由于使用个体散射测量在临床应用中不太理想，我们认为使用两个区域特定卷积内核和可变散射分数曲线的双内核算法是更可取的方法。
