Patient-specific quantification of image quality: An automated method for measuring spatial resolution in clinical CT images
Patient-specific quantification of image quality: An automated method for measuring spatial resolution in clinical CT images
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DOI：
10.1118/1.4961984
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发表时间：
2016-10-01
期刊：
影响因子：
3.8
通讯作者：
Samei, Ehsan
中科院分区：
文献类型：
--
作者：
Sanders, Jeremiah;Hurwitz, Lynne;Samei, Ehsan
目的：为了开发和验证一种用于评估临床CT图像空间分辨率特性的自动化技术，方法：在这项研究中检查了21个胸部和腹部骨盆临床CT数据集。开发了一种算法，通过测量患者皮肤上的边缘扩展函数（ESF），从临床CT图像中提取类似于调制传递函数的CT分辨率指数（RI）。创建空气-皮肤边界的多边形网格。然后使用网格的面来测量穿过空气-皮肤界面的ESF。对ESF进行微分得到线扩展函数（LSF），对LSF进行傅立叶变换得到RI。研究了该算法检测RI径向依赖性的能力。使用所提出的方法测量的RI与传统的基于体模的方法在两种重建算法（FBP和迭代）上进行比较，使用50%RI时的空间频率f（50）作为比较的度量。三个重建内核进行了研究，为每个重建算法。最后，观察者的研究进行，以确定观察者是否可以在视觉上感知的测量模糊的图像重建与给定的reconstruction method.Results的差异：RI测量所提出的技术表现出预期的依赖关系的图像重建。对于FBP和迭代重建，测量的f（50）值随着更硬的内核而增加。此外，该算法能够检测径向依赖的RI。RI的患者特定测量值与基于体模的技术相当，但患者数据在测量的f（50）中表现出较大的扩散，表明即使使用相同的重建算法和内核重建投影数据，某些数据集也比其他数据集更模糊。观察者研究的结果证实了这一发现。结论：临床知情，患者特异性的空间分辨率可以从临床数据集测量。该方法是足够敏感，以反映由于不同的重建参数的空间分辨率的变化。该方法可以应用于自动评估患者图像的空间分辨率，并量化可能无法在体模数据中捕获的依赖性。(C)2016年美国医学物理学家协会。
Purpose: To develop and validate an automated technique for evaluating the spatial resolution characteristics of clinical computed tomography (CT) images.Methods: Twenty one chest and abdominopelvic clinical CT datasets were examined in this study. An algorithm was developed to extract a CT resolution index (RI) analogous to the modulation transfer function from clinical CT images by measuring the edge-spread function (ESF) across the patient's skin. A polygon mesh of the air-skin boundary was created. The faces of the mesh were then used to measure the ESF across the air-skin interface. The ESF was differentiated to obtain the line-spread function (LSF), and the LSF was Fourier transformed to obtain the RI. The algorithm's ability to detect the radial dependence of the RI was investigated. RIs measured with the proposed method were compared with a conventional phantom-based method across two reconstruction algorithms (FBP and iterative) using the spatial frequency at 50% RI, f(50), as the metric for comparison. Three reconstruction kernels were investigated for each reconstruction algorithm. Finally, an observer study was conducted to determine if observers could visually perceive the differences in the measured blurriness of images reconstructed with a given reconstruction method.Results: RI measurements performed with the proposed technique exhibited the expected dependencies on the image reconstruction. The measured f(50) values increased with harder kernels for both FBP and iterative reconstruction. Furthermore, the proposed algorithm was able to detect the radial dependence of the RI. Patient-specific measurements of the RI were comparable to the phantom-based technique, but the patient data exhibited a large spread in the measured f(50), indicating that some datasets were blurrier than others even when the projection data were reconstructed with the same reconstruction algorithm and kernel. Results from the observer study substantiated this finding.Conclusions: Clinically informed, patient-specific spatial resolution can be measured from clinical datasets. The method is sufficiently sensitive to reflect changes in spatial resolution due to different reconstruction parameters. The method can be applied to automatically assess the spatial resolution of patient images and quantify dependencies that may not be captured in phantom data. (C) 2016 American Association of Physicists in Medicine.