PDF(Pubmed)
Abstract：
BACKGROUND: Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) - a locally adaptive image filter - allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering.
METHODS: Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( [ 18 F ] FDG, [ 18 F ] L-DOPA, [ 68 Ga ] DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the \"optimal\" filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs.
RESULTS: The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUV max values of the unfiltered images with a low mean ± SD difference of δ SUV max CNN , STD = (-3.9 ± 5.2)% and δ SUV max BF , STD = (-4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUV max values in the vast majority of cases with an overall average difference of δ SUV max CNN , BF = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δ Noise CNN , BF = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed.
CONCLUSIONS: Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.
METHODS: Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( [ 18 F ] FDG, [ 18 F ] L-DOPA, [ 68 Ga ] DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the \"optimal\" filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs.
RESULTS: The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUV max values of the unfiltered images with a low mean ± SD difference of δ SUV max CNN , STD = (-3.9 ± 5.2)% and δ SUV max BF , STD = (-4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUV max values in the vast majority of cases with an overall average difference of δ SUV max CNN , BF = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δ Noise CNN , BF = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed.
CONCLUSIONS: Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.
摘要：
背景：在正电子发射断层扫描（PET）中，残余图像噪声是大量的，是限制病变检测的因素之一，量化，和整体图像质量。因此，改善降噪仍然相当感兴趣。对于呼吸门控PET研究尤其如此。PET成像中唯一广泛使用的降噪方法是应用低通滤波器，通常是高斯，然而，这会导致空间分辨率的损失和部分体积效应的增加，从而影响小病变的可检测性和定量数据评估。双边滤波器（BF）-一种局部自适应图像滤波器-允许减少图像噪声，同时保留定义明确的对象边缘，但手动优化给定PET扫描的滤波器参数可能是繁琐且耗时的。妨碍其临床使用。在这项工作中，我们研究了一种合适的基于深度学习的方法在多大程度上可以通过训练一个合适的网络来解决这个问题，该网络的目标是再现手动调整的特定案例双边过滤的结果。
方法：总之，使用三种不同的示踪剂进行69次呼吸门控临床PET/CT扫描（[18F]FDG，[18F]L-DOPA,[68Ga]DOTATATE)用于本研究。在数据处理之前，门控数据集被拆分，导致总共552个单门图像卷。对于这些图像体积中的每一个，描绘了四个3DROI:一个ROI用于图像噪声评估,三个ROI用于不同目标/背景对比水平下的局灶性摄取(例如肿瘤病变)测量。使用自动程序对每个数据集的二维BF参数空间进行强力搜索，以识别“最佳”滤波器参数，以生成用户批准的由原始和最佳BF滤波图像对组成的地面实况输入数据。为了再现最佳BF滤波，我们采用了一种结合残差学习原理的改进的3DU-NetCNN。使用5倍交叉验证方案进行网络训练和评估。通过计算CNN之间的绝对和分数差异来评估滤波对病变SUV量化和图像噪声水平的影响，手动BF,或先前定义的ROI中的原始(STD)数据集。
结果：用于过滤器参数确定的自动化程序为大多数数据集选择了足够的过滤器参数，只有19个患者数据集需要手动调整。对聚焦吸收ROI的评估表明，CNN以及基于BF的滤波基本上保持了未滤波图像的聚焦SUV最大值，δSUVmaxCNN的平均值较低，STD=(-3.9±5.2)%,δSUVmaxBF,STD=(-4.4±5.3)%。关于CNN与BF的相对性能，在绝大多数情况下，这两种方法都导致了非常相似的SUV最大值，总平均差为δSUVmaxCNN，BF=(0.5±4.8)%。对噪声特性的评估表明，CNN滤波可以很好地再现具有δNoiseCNN的BF的噪声水平和特性，BF=(5.6±10.5)%。在CNN和BF之间没有观察到显著的示踪剂依赖性差异。
结论：我们的结果表明，基于神经网络的去噪可以完全自动化的方式再现逐例优化BF的结果。除了罕见的情况下，它导致图像的噪声水平几乎相同的质量，边缘保护,和信号恢复。我们相信这样的网络可能证明在改进呼吸门控PET研究的运动校正的背景下特别有用,但也可以帮助在临床PET中建立BF等效边缘保留CNN滤波,因为它避免了耗时的手动BF参数调整。
方法：总之，使用三种不同的示踪剂进行69次呼吸门控临床PET/CT扫描（[18F]FDG，[18F]L-DOPA,[68Ga]DOTATATE)用于本研究。在数据处理之前，门控数据集被拆分，导致总共552个单门图像卷。对于这些图像体积中的每一个，描绘了四个3DROI:一个ROI用于图像噪声评估,三个ROI用于不同目标/背景对比水平下的局灶性摄取(例如肿瘤病变)测量。使用自动程序对每个数据集的二维BF参数空间进行强力搜索，以识别“最佳”滤波器参数，以生成用户批准的由原始和最佳BF滤波图像对组成的地面实况输入数据。为了再现最佳BF滤波，我们采用了一种结合残差学习原理的改进的3DU-NetCNN。使用5倍交叉验证方案进行网络训练和评估。通过计算CNN之间的绝对和分数差异来评估滤波对病变SUV量化和图像噪声水平的影响，手动BF,或先前定义的ROI中的原始(STD)数据集。
结果：用于过滤器参数确定的自动化程序为大多数数据集选择了足够的过滤器参数，只有19个患者数据集需要手动调整。对聚焦吸收ROI的评估表明，CNN以及基于BF的滤波基本上保持了未滤波图像的聚焦SUV最大值，δSUVmaxCNN的平均值较低，STD=(-3.9±5.2)%,δSUVmaxBF,STD=(-4.4±5.3)%。关于CNN与BF的相对性能，在绝大多数情况下，这两种方法都导致了非常相似的SUV最大值，总平均差为δSUVmaxCNN，BF=(0.5±4.8)%。对噪声特性的评估表明，CNN滤波可以很好地再现具有δNoiseCNN的BF的噪声水平和特性，BF=(5.6±10.5)%。在CNN和BF之间没有观察到显著的示踪剂依赖性差异。
结论：我们的结果表明，基于神经网络的去噪可以完全自动化的方式再现逐例优化BF的结果。除了罕见的情况下，它导致图像的噪声水平几乎相同的质量，边缘保护,和信号恢复。我们相信这样的网络可能证明在改进呼吸门控PET研究的运动校正的背景下特别有用,但也可以帮助在临床PET中建立BF等效边缘保留CNN滤波,因为它避免了耗时的手动BF参数调整。
