Ahmad Moiz, Jacobsen Megan C, Thomas M Allan, Chen Henry S, Layman Rick R, Jones A Kyle
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Med Phys. 2021 Feb;48(2):640-647. doi: 10.1002/mp.14635. Epub 2020 Dec 22.
Assessment of image quality directly in clinical image data is an important quality control objective as phantom-based testing does not fully represent image quality across patient variation. Computer algorithms for automatically measuring noise in clinical computed tomography (CT) images have been introduced, but the accuracy of these algorithms is unclear. This work benchmarks the accuracy of the global noise (GN) algorithm for automatic noise measurement in contrast-enhanced abdomen CT exams in comparison to precise reference noise measurements. The GN algorithm was further optimized compared to the previous report in the literature.
Reference values of noise were established in a public image dataset of 82 contrast-enhanced abdomen CT exams. The reference noise values were obtained by manual regions-of-interest measurements of pixel standard deviation in the liver parenchyma according to an instruction protocol. Noise measurements taken by six observers were averaged together to improve reference noise statistical precision. The GN algorithm was used to automatically measure noise in each image set. The accuracy of the GN algorithm was determined in terms of RMS error compared to reference noise. The GN algorithm was optimized by conducting 1000 trials with random algorithm parameter values. The trial with the lowest RMS error was used to select optimum algorithm parameters.
The range of noise across CT image sets was 8.8-28.8 HU. Reference noise measurements were made with a precision of ±0.78 HU (95% confidence interval). The RMS error of automatic noise measurement was 0.93 HU (0.77-1.19 HU 95% confidence interval). The automatic noise measurements were equally accurate across image sets of varying noise magnitude. Optimum GN algorithm parameter values were: a kernel size of 7 pixels, and soft tissue lower and upper thresholds of 0 and 170 HU, respectively.
The performance of automatic noise measurement was benchmarked in a large clinical CT dataset. The study provides a framework for thorough validation of automatic clinical image quality measurement methods. The GN algorithm was optimized and validated for automatic measurement of soft-tissue noise in abdomen CT exams.
直接在临床图像数据中评估图像质量是一项重要的质量控制目标，因为基于模体的测试不能完全代表不同患者情况下的图像质量。已引入用于自动测量临床计算机断层扫描（CT）图像噪声的计算机算法，但这些算法的准确性尚不清楚。本研究将对比增强腹部CT检查中用于自动噪声测量的全局噪声（GN）算法的准确性与精确的参考噪声测量结果进行对比。与文献中先前的报告相比，GN算法得到了进一步优化。
在一个包含82例对比增强腹部CT检查的公共图像数据集中确定噪声参考值。根据操作协议，通过对肝实质像素标准差进行手动感兴趣区域测量来获得参考噪声值。将六位观察者进行的噪声测量结果平均，以提高参考噪声统计精度。使用GN算法自动测量每个图像集的噪声。与参考噪声相比，根据均方根误差确定GN算法的准确性。通过使用随机算法参数值进行1000次试验来优化GN算法。选择均方根误差最低的试验来确定最佳算法参数。
CT图像集的噪声范围为8.8 - 28.8 HU。参考噪声测量的精度为±0.78 HU（95%置信区间）。自动噪声测量的均方根误差为0.93 HU（0.77 - 1.19 HU 95%置信区间）。在不同噪声大小的图像集中，自动噪声测量的准确性相同。GN算法的最佳参数值为：内核大小7像素，软组织下限和上限阈值分别为0和170 HU。
在一个大型临床CT数据集中对自动噪声测量的性能进行了对比。该研究为全面验证自动临床图像质量测量方法提供了一个框架。GN算法针对腹部CT检查中软组织噪声的自动测量进行了优化和验证。
