Yu Shaode, Dai Guangzhe, Wang Zhaoyang, Li Leida, Wei Xinhua, Xie Yaoqin
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China.
BMC Med Imaging. 2018 May 16;18(1):17. doi: 10.1186/s12880-018-0256-6.
Quality assessment of medical images is highly related to the quality assurance, image interpretation and decision making. As to magnetic resonance (MR) images, signal-to-noise ratio (SNR) is routinely used as a quality indicator, while little knowledge is known of its consistency regarding different observers.
In total, 192, 88, 76 and 55 brain images are acquired using T, T, T and contrast-enhanced T (TC) weighted MR imaging sequences, respectively. To each imaging protocol, the consistency of SNR measurement is verified between and within two observers, and white matter (WM) and cerebral spinal fluid (CSF) are alternately used as the tissue region of interest (TOI) for SNR measurement. The procedure is repeated on another day within 30 days. At first, overlapped voxels in TOIs are quantified with Dice index. Then, test-retest reliability is assessed in terms of intra-class correlation coefficient (ICC). After that, four models (BIQI, BLIINDS-II, BRISQUE and NIQE) primarily used for the quality assessment of natural images are borrowed to predict the quality of MR images. And in the end, the correlation between SNR values and predicted results is analyzed.
To the same TOI in each MR imaging sequence, less than 6% voxels are overlapped between manual delineations. In the quality estimation of MR images, statistical analysis indicates no significant difference between observers (Wilcoxon rank sum test, p ≥ 0.11; paired-sample t test, p ≥ 0.26), and good to very good intra- and inter-observer reliability are found (ICC, p ≥ 0.74). Furthermore, Pearson correlation coefficient (r ) suggests that SNR correlates strongly with BIQI, BLIINDS-II and BRISQUE in T (r ≥ 0.78), BRISQUE and NIQE in T (r ≥ 0.77), BLIINDS-II in T (r ≥ 0.68) and BRISQUE and NIQE in TC (r ≥ 0.62) weighted MR images, while SNR correlates strongly with BLIINDS-II in T (r ≥ 0.63) and in T (r ≥ 0.64) weighted MR images.
The consistency of SNR measurement is validated regarding various observers and MR imaging protocols. When SNR measurement performs as the quality indicator of MR images, BRISQUE and BLIINDS-II can be conditionally used for the automated quality estimation of human brain MR images.
医学图像的质量评估与质量保证、图像解读及决策制定密切相关。对于磁共振（MR）图像，信噪比（SNR）通常用作质量指标，然而对于不同观察者而言，其一致性却鲜为人知。
分别使用T、T、T和对比增强T（TC）加权MR成像序列采集了总共192、88、76和55幅脑图像。对于每种成像协议，在两名观察者之间以及观察者内部验证SNR测量的一致性，并交替使用白质（WM）和脑脊液（CSF）作为感兴趣的组织区域（TOI）进行SNR测量。该过程在30天内的另一天重复进行。首先，使用Dice指数对TOI中的重叠体素进行量化。然后，根据组内相关系数（ICC）评估重测信度。之后，借鉴四种主要用于自然图像质量评估的模型（BIQI、BLIINDS-II、BRISQUE和NIQE）来预测MR图像的质量。最后，分析SNR值与预测结果之间的相关性。
对于每个MR成像序列中的相同TOI，手动勾勒之间的重叠体素少于6%。在MR图像的质量评估中，统计分析表明观察者之间无显著差异（Wilcoxon秩和检验，p≥0.11；配对样本t检验，p≥0.26），并且观察者内和观察者间的可靠性良好至非常好（ICC，p≥0.74）。此外，Pearson相关系数（r）表明，在T加权MR图像中，SNR与BIQI、BLIINDS-II和BRISQUE密切相关（r≥0.78），在T加权MR图像中与BRISQUE和NIQE密切相关（r≥0.77），在T加权MR图像中与BLIINDS-II密切相关（r≥0.68），在TC加权MR图像中与BRISQUE和NIQE密切相关（r≥0.62），而在T加权MR图像中SNR与BLIINDS-II密切相关（r≥0.63），在T加权MR图像中与BLIINDS-II密切相关（r≥0.64）。
关于不同观察者和MR成像协议，SNR测量的一致性得到了验证。当SNR测量用作MR图像的质量指标时，BRISQUE和BLIINDS-II可在条件下用于人脑MR图像的自动质量评估。
