Accurate assessment and prediction of noise in clinical CT images
Accurate assessment and prediction of noise in clinical CT images
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DOI：
10.1118/1.4938588
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发表时间：
2016-01-01
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：
Tian, Xiaoyu;Samei, Ehsan
目的：(A)设计一种测量临床人体CT图像中量子噪声的技术；(B)开发一个高精度预测该噪声的模型。方法：本研究包括两个剂量水平(临床剂量水平和50%剂量水平)的83个临床图像集。通过减去连续切片和滤除边缘来测量临床图像中的量子噪声。然后在合成的均匀区域中测量噪声。使用17个临床影像病例和一个火鸡模型对噪声测量技术进行了验证。用一种有效的方法测量临床图像中的噪声，通过建立不同大小体模中的水当量直径(DW)与噪声之间的相关性，并根据从CT图像估计的DW将噪声水平归因于患者来预测这种噪声。结果：两种重建算法的噪声测量误差均在1.5HU以内。在噪声预测方面，在83个临床图像集上，自适应统计迭代重建和滤波反投影重建的预测噪声与实际噪声的平均偏差分别为6.9%和6.6%。结论：本研究提出了一种实用的评估临床图像中量子噪声的方法。基于图像的测量技术使临床实践中对图像噪声的自动质量控制监控成为可能。此外，基于体模的模型可以准确地预测患者图像中的量子噪声水平。该预测模型可用于定量优化个体方案，以达到临床图像中的目标噪声水平。(C)2016年美国医学物理学家协会。
Purpose: The objectives of this study were (a) to devise a technique for measuring quantum noise in clinical body computed tomography (CT) images and (b) to develop a model for predicting that noise with high accuracy.Methods: The study included 83 clinical image sets at two dose levels (clinical and 50% reduced dose levels). The quantum noise in clinical images was measured by subtracting sequential slices and filtering out edges. Noise was then measured in the resultant uniform area. The noise measurement technique was validated using 17 clinical image cases and a turkey phantom. With a validated method to measure noise in clinical images, this noise was predicted by establishing the correlation between water-equivalent diameter (Dw) and noise in a variable-sized phantom and ascribing a noise level to the patient based on Dw estimated from CT image. The accuracy of this prediction model was validated using 66 clinical image sets.Results: The error in noise measurement was within 1.5 HU across two reconstruction algorithms. In terms of noise prediction, across the 83 clinical image sets, the average discrepancies between predicted and measured noise were 6.9% and 6.6% for adaptive statistical iterative reconstruction and filtered back projection reconstruction, respectively.Conclusions: This study proposed a practically applicable method to assess quantum noise in clinical images. The image-based measurement technique enables automatic quality control monitoring of image noise in clinical practice. Further, a phantom-based model can accurately predict quantum noise level in patient images. The prediction model can be used to quantitatively optimize individual protocol to achieve targeted noise level in clinical images. (C) 2016 American Association of Physicists in Medicine.