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首页> 外文期刊>BMC Medical Physics >The 2D Hotelling filter - a quantitative noise-reducing principal-component filter for dynamic PET data, with applications in patient dose reduction
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The 2D Hotelling filter - a quantitative noise-reducing principal-component filter for dynamic PET data, with applications in patient dose reduction

机译：2D Hotelling过滤器-用于动态PET数据的定量降噪主成分过滤器，适用于降低患者剂量

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Background In this paper we apply the principal-component analysis filter (Hotelling filter) to reduce noise from dynamic positron-emission tomography (PET) patient data, for a number of different radio-tracer molecules. We furthermore show how preprocessing images with this filter improves parametric images created from such dynamic sequence. We use zero-mean unit variance normalization, prior to performing a Hotelling filter on the slices of a dynamic time-series. The Scree-plot technique was used to determine which principal components to be rejected in the filter process. This filter was applied to [11C]-acetate on heart and head-neck tumors, [18F]-FDG on liver tumors and brain, and [11C]-Raclopride on brain. Simulations of blood and tissue regions with noise properties matched to real PET data, was used to analyze how quantitation and resolution is affected by the Hotelling filter. Summing varying parts of a 90-frame [18F]-FDG brain scan, we created 9-frame dynamic scans with image statistics comparable to 20 MBq, 60 MBq and 200 MBq injected activity. Hotelling filter performed on slices (2D) and on volumes (3D) were compared. Results The 2D Hotelling filter reduces noise in the tissue uptake drastically, so that it becomes simple to manually pick out regions-of-interest from noisy data. 2D Hotelling filter introduces less bias than 3D Hotelling filter in focal Raclopride uptake. Simulations show that the Hotelling filter is sensitive to typical blood peak in PET prior to tissue uptake have commenced, introducing a negative bias in early tissue uptake. Quantitation on real dynamic data is reliable. Two examples clearly show that pre-filtering the dynamic sequence with the Hotelling filter prior to Patlak-slope calculations gives clearly improved parametric image quality. We also show that a dramatic dose reduction can be achieved for Patlak slope images without changing image quality or quantitation. Conclusions The 2D Hotelling-filtering of dynamic PET data is a computer-efficient method that gives visually improved differentiation of different tissues, which we have observed improve manual or automated region-of-interest delineation of dynamic data. Parametric Patlak images on Hotelling-filtered data display improved clarity, compared to non-filtered Patlak slope images without measurable loss of quantitation, and allow a dramatic decrease in patient injected dose.
机译：背景技术在本文中，我们针对许多不同的放射性示踪剂分子，应用主成分分析过滤器（Hotelling过滤器）来减少来自动态正电子发射断层扫描（PET）患者数据的噪声。我们进一步展示了使用此滤镜进行预处理的图像如何改善从这种动态序列创建的参数图像。在对动态时间序列的切片执行霍特林滤波之前，我们使用零均值单位方差归一化。 Scree-plot技术用于确定在过滤过程中要剔除的主要成分。将该滤膜用于心脏和头颈部肿瘤的[ 11 C]-乙酸盐，肝肿瘤和脑部的[ 18 F] -FDG和[脑中的11 C]-雷氯必利。使用具有与真实PET数据匹配的噪声特性的血液和组织区域模拟，来分析Hotelling过滤器如何影响定量和分离度。总结了90帧[ 18 F] -FDG脑部扫描的各个部分，我们创建了9帧动态扫描，其图像统计量相当于20 MBq，60 MBq和200 MBq注入活动。比较了在切片（2D）和体积（3D）上执行的Hotelling滤波器。结果2D Hotelling过滤器可大大降低组织摄取中的噪声，因此从噪声数据中手动挑选出感兴趣区域变得很简单。 2D Hotelling滤镜在焦点Raclopride吸收方面比3D Hotelling滤镜引入更小的偏差。模拟表明，Hotelling过滤器对开始吸收组织之前的PET中典型的血峰敏感，从而在早期组织吸收中产生了负偏差。真实动态数据的定量分析是可靠的。两个例子清楚地表明，在Patlak斜率计算之前用Hotelling滤波器对动态序列进行预滤波可以明显改善参数图像质量。我们还表明，对于Patlak斜率图像，可以实现显着的剂量减少，而无需更改图像质量或定量。结论动态PET数据的2D Hotelling滤波是一种计算机有效的方法，可以在视觉上改善不同组织的分化，我们已经观察到动态数据的手动或自动关注区域描述得到了改善。与未过滤的Patlak斜率图像相比，Hotelling过滤的数据上的参数化Patlak图像显示了更高的清晰度，而没有可量化的定量损失，并且可以大大减少患者的注射剂量。

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