DETECTION OF DEGRADATION OF MAGNETIC-RESONANCE (MR) IMAGES - COMPARISON OF AN AUTOMATED MR IMAGE-QUALITY ANALYSIS SYSTEM WITH TRAINED HUMAN OBSERVERS
DETECTION OF DEGRADATION OF MAGNETIC-RESONANCE (MR) IMAGES - COMPARISON OF AN AUTOMATED MR IMAGE-QUALITY ANALYSIS SYSTEM WITH TRAINED HUMAN OBSERVERS
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DOI：
10.1016/s1076-6332(05)80184-9
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发表时间：
1995-04-01
影响因子：
4.8
通讯作者：
CARSON, PL
中科院分区：
文献类型：
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作者：
GARDNER, EA;ELLIS, JH;CARSON, PL
基本原理和目标。他们对磁共振（MR）成像质量控制（QC）的感知需求偶尔会最小化，因为假设用户会检测到重大错误。为了评估这一假设的有效性，我们通过评估故意降级的图像，将测试对象和用于MR成像QC的自动图像分析系统的灵敏度与训练有素的人类观察者的灵敏度进行了比较。将被测对象和正常人类志愿者成像的参数设置为降低信噪比（SNR）、造成失真、增加切片厚度和分离度的值。人类观察者能够在人类图像中检测到6-13%的信噪比降低和超过15%的失真。他们无法确定切片厚度增加了40%。测试对象图像的自动分析能够在最小应用水平上检测所有图像退化。人类观察者的敏感度差表明，在通过临床图像的视觉分析检测到退化之前，尤其是空间测量，可能会出现明显的误差。这些错误将通过对所使用的测试对象的自动分析来检测。定量图像质量分析预测图像质量下降对人类观察检测临床图像中细微异常的能力的影响，需要进一步的研究来更好地确定其准确性。
Rationale and Objectives. Ther perceived need for magnetic resonance (MR) imaging quality control (QC) is occasionally minimized on the assumption that significant errors will be detected by the users. To evaluate the validity of this assumption, we compared the sensitivity of a test object and automated image analysis system for MR imaging QC with the sensitivity of trained human observers by evaluating images that were intentionally degraded.Methods. Parameters for imaging the test object and normal human volunteers were set to values that decreased the signal-to-noise ratio (SNR), caused distortion, and increased the slice thickness and separation.Results. The human observers were able to detect a 6-13% reduction in the SNR and distortions of more than 15% in human images. They were unable to identify 40% increases in the slice thickness. Automated analysis of test object images was able to detect all image degradations at the minimum levels applied.Conclusion. The poor sensitivity of the human observers indicated that degradation, especially spatial measurements, could be significantly in error before being detected through visual analysis of clinical images. These errors would be detected by automated analysis of the test object used. Further investigation is needed to better define the accuracy with which quantitative image-quality analysis predicts the effects of degraded image quality on the ability of human observes to detect subtle abnormalities in clinical images.