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一种用于评估肝脏CT中感知噪声纹理的自动化方法的开发与验证。

Development and validation of an automated methodology to assess perceptual noise texture in liver CT.

作者信息

Smith Taylor Brunton, Abadi Ehsan, Sauer Thomas J, Fu Wanyi, Solomon Justin, Samei Ehsan

机构信息

Duke University, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States.

Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States.

出版信息

J Med Imaging (Bellingham). 2021 Sep;8(5):052113. doi: 10.1117/1.JMI.8.5.052113. Epub 2021 Oct 25.

Abstract

: Developing, validating, and evaluating a method for measuring noise texture directly from patient liver CT images (i.e., ). : The method identifies target regions within patient scans that are least likely to have major contribution of patient anatomy, detrends them locally, and measures noise power spectrum (NPS) there using a previously phantom-validated technique targeting perceptual noise-non-anatomical fluctuations in the image that may interfere with the detection of focal lesions. Method development and validation used scanner-specific CT simulations of computational, anthropomorphic phantom (XCAT phantom, three phases of contrast-enhancement) with known ground truth of the NPS. Simulations were based on a clinical scanner (Definition Flash, Siemens) and clinically relevant settings (tube voltage of 120 kV at three dose levels). Images were reconstructed with filtered backprojection (kernel: B31, B41, and B50) and Sinogram Affirmed Iterative Reconstruction (kernel: I31, I41, and I50) using a manufacturer-specific reconstruction software (ReconCT, Siemens). All NPS measurements were made in the liver. Ground-truth NPS were taken as the sum of (1) a measurement in parenchymal regions of anatomy-subtracted (i.e., noise only) scans, and (2) a measurement in the same region of noise-free (pre-noise-insertion) images. To assess NPS performance, correlation of NPS average frequency ( ), was reported. Sensitivity of accuracy [root-mean-square-error (RMSE)] to number of pixels included in measurement was conducted via bootstrapped pixel-dropout. Sensitivity of NPS to dose and reconstruction kernel was assessed to confirm that ground truth NPS similarities were maintained in patient-specific measurements. : Pearson and Spearman correlation coefficients 0.97 and 0.96 for indicated good correlation. Results suggested accurate NPS measurements (within 5% total RMSE) could be acquired with . : Relationships of similar NPS due to reconstruction kernel and dose were preserved between gold standard and observed estimations. The NPS estimation method was further deployed on clinical cases to demonstrate the feasibility of clinical analysis.

摘要

开发、验证和评估一种直接从患者肝脏CT图像测量噪声纹理的方法(即 )。:该方法识别患者扫描中最不可能对患者解剖结构有主要贡献的目标区域,对其进行局部去趋势处理,并使用先前针对图像中可能干扰局灶性病变检测的感知噪声(非解剖学波动)进行体模验证的技术在那里测量噪声功率谱(NPS)。方法开发和验证使用了具有已知NPS真实值的计算型拟人化体模(XCAT体模,三个对比增强阶段)的特定扫描仪CT模拟。模拟基于临床扫描仪(Definition Flash,西门子)和临床相关设置(三种剂量水平下120 kV的管电压)。使用制造商特定的重建软件(ReconCT,西门子)通过滤波反投影(内核:B31、B41和B50)和正弦图确认迭代重建(内核:I31、I41和I50)重建图像。所有NPS测量均在肝脏中进行。真实NPS取为(1)解剖结构减去(即仅噪声)扫描的实质区域中的测量值与(2)无噪声(预噪声插入)图像相同区域中的测量值之和。为了评估NPS性能,报告了NPS平均频率()的相关性。通过自举重采样像素剔除来评估测量中包含的像素数量对精度[均方根误差(RMSE)]的敏感性。评估NPS对剂量和重建内核的敏感性,以确认在患者特定测量中保持真实NPS的相似性。:Pearson和Spearman相关系数分别为0.97和0.96,表明相关性良好。结果表明,使用 可以获得准确的NPS测量值(总RMSE在5%以内)。:金标准与观察到的估计值之间保持了由于重建内核和剂量导致的相似NPS关系。NPS估计方法进一步应用于临床病例,以证明临床分析的可行性。

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