School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
Department of Radiology, Yonsei University College of Medicine, Yonsei University, Seoul, Republic of Korea.
Med Phys. 2022 Nov;49(11):7247-7261. doi: 10.1002/mp.15831. Epub 2022 Jul 10.
It is important to fully automate the evaluation of gadoxetate disodium-enhanced arterial phase images because the efficient quantification of transient severe motion artifacts can be used in a variety of applications. Our study proposes a fully automatic evaluation method of motion artifacts during the arterial phase of gadoxetate disodium-enhanced MR imaging.
The proposed method was based on the construction of quality-aware features to represent the motion artifact using MR image statistics and multidirectional filtered coefficients. Using the quality-aware features, the method calculated quantitative quality scores of gadoxetate disodium-enhanced images fully automatically. The performance of our proposed method, as well as two other methods, was acquired by correlating scores against subjective scores from radiologists based on the 5-point scale and binary evaluation. The subjective scores evaluated by two radiologists were severity scores of motion artifacts in the evaluation set on a scale of 1 (no motion artifacts) to 5 (severe motion artifacts).
Pearson's linear correlation coefficient (PLCC) and Spearman's rank-ordered correlation coefficient (SROCC) values of our proposed method against the subjective scores were 0.9036 and 0.9057, respectively, whereas the PLCC values of two other methods were 0.6525 and 0.8243, and the SROCC values were 0.6070 and 0.8348. Also, in terms of binary quantification of transient severe respiratory motion, the proposed method achieved 0.9310 sensitivity, 0.9048 specificity, and 0.9200 accuracy, whereas the other two methods achieved 0.7586, 0.8996 sensitivities, 0.8098, 0.8905 specificities, and 0.9200, 0.9048 accuracies CONCLUSIONS: This study demonstrated the high performance of the proposed automatic quantification method in evaluating transient severe motion artifacts in arterial phase images.
充分实现钆塞酸二钠增强动脉期图像评估的自动化非常重要,因为高效定量评估短暂性剧烈运动伪影可应用于多种场景。本研究提出了一种用于评估钆塞酸二钠增强磁共振成像动脉期运动伪影的全自动评估方法。
所提出的方法基于构建质量感知特征,使用磁共振图像统计和多方向滤波系数来表示运动伪影。使用质量感知特征,该方法可全自动计算钆塞酸二钠增强图像的定量质量评分。通过将评分与基于 5 分制和二进制评估的放射科医生的主观评分进行相关联,获得我们提出的方法与另外两种方法的性能。两名放射科医生对评估集中的运动伪影严重程度进行了评估,其严重程度评分范围为 1(无运动伪影)至 5(严重运动伪影)。
与主观评分相比,我们提出的方法的 Pearson 线性相关系数(PLCC)和 Spearman 秩相关系数(SROCC)值分别为 0.9036 和 0.9057,而另外两种方法的 PLCC 值分别为 0.6525 和 0.8243,SROCC 值分别为 0.6070 和 0.8348。此外,在对短暂性剧烈呼吸运动的二进制量化方面,所提出的方法实现了 0.9310 的灵敏度、0.9048 的特异性和 0.9200 的准确率,而另外两种方法的灵敏度、特异性和准确率分别为 0.7586、0.8996、0.8098、0.8905 和 0.9200、0.9048。
本研究表明,所提出的自动量化方法在评估动脉期图像中的短暂性剧烈运动伪影方面具有优异的性能。