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多中心神经影像学研究中脑结构磁共振图像的标准化质量评估体系

Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study.

作者信息

Osadebey Michael E, Pedersen Marius, Arnold Douglas L, Wendel-Mitoraj Katrina E, Alzheimer's Disease Neuroimaging Initiative For The

机构信息

NeuroRx Research Inc, Montreal, 3575 Parc Avenue, Suite # 5322, Montreal, Quebec, H2X 3P9, Canada.

Department of Computer Science, Norwegian University of Science and Technology, Teknologivegen 22, Gjøvik, N-2815, Norway.

出版信息

BMC Med Imaging. 2018 Sep 17;18(1):31. doi: 10.1186/s12880-018-0266-4.

Abstract

BACKGROUND

Multi-site neuroimaging offer several benefits and poses tough challenges in the drug development process. Although MRI protocol and clinical guidelines developed to address these challenges recommend the use of good quality images, reliable assessment of image quality is hampered by the several shortcomings of existing techniques.

METHODS

Given a test image two feature images are extracted. They are grayscale and contrast feature images. Four binary images are generated by setting four different global thresholds on the feature images. Image quality is predicted by measuring the structural similarity between appropriate pairs of binary images. The lower and upper limits of the quality index are 0 and 1. Quality prediction is based on four quality attributes; luminance contrast, texture, texture contrast and lightness.

RESULTS

Performance evaluation on test data from three multi-site clinical trials show good objective quality evaluation across MRI sequences, levels of distortion and quality attributes. Correlation with subjective evaluation by human observers is ≥ 0.6.

CONCLUSION

The results are promising for the evaluation of MRI protocols, specifically the standardization of quality index, designed to overcome the challenges encountered in multi-site clinical trials.

摘要

背景

多中心神经成像在药物研发过程中有诸多益处,但也带来了严峻挑战。尽管为应对这些挑战而制定的MRI协议和临床指南推荐使用高质量图像,但现有技术的若干缺陷阻碍了对图像质量的可靠评估。

方法

给定一幅测试图像,提取两幅特征图像,即灰度特征图像和对比度特征图像。通过在特征图像上设置四个不同的全局阈值生成四幅二值图像。通过测量适当的二值图像对之间的结构相似性来预测图像质量。质量指数的下限和上限分别为0和1。质量预测基于四个质量属性,即亮度对比度、纹理、纹理对比度和亮度。

结果

对来自三项多中心临床试验的测试数据进行的性能评估表明,在MRI序列、失真程度和质量属性方面,客观质量评估效果良好。与人类观察者的主观评估的相关性≥0.6。

结论

这些结果对于评估MRI协议很有前景,特别是质量指数的标准化,旨在克服多中心临床试验中遇到的挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e60/6142697/bb95f52175eb/12880_2018_266_Fig1_HTML.jpg

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