Purpose: 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. Methods: 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). Results: 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.
|Number of pages||15|
|Publication status||Published - 2022 Nov|
Bibliographical noteFunding Information:
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Grant nos. 2021R1A4A1031437, 2022R1A2C2008983) and partially supported by the Yonsei Signature Research Cluster Program of 2022 (Grant no. 2022‐22‐0002), the KIST Institutional Program (Project No. 2E31051‐21‐204), the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (MSIT), and Artificial Intelligence Graduate School Program, Yonsei University (Grant no. 2020‐0‐01361).
© 2022 American Association of Physicists in Medicine.
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging