https://scholars.lib.ntu.edu.tw/handle/123456789/611435
標題: | Respiratory motion prediction using fusion-based multi-rate kalman filtering and real-time golden-angle radial mri | 作者: | Li X. Lee Y.-H. Mikaiel S. Simonelli J. Tsao T.-C. Wu H.H. YU-HSIU LEE |
關鍵字: | Biomedical signal processing;Data streams;Errors;Forecasting;Image fusion;Kalman filters;Magnetic resonance imaging;Motion estimation;Phantoms;Respiratory mechanics;Time and motion study;Feed back information;Image based tracking;Motion tracking methods;Phantom experiment;Provide guidances;Respiratory motion prediction;Software pipeline;Wilcoxon signed rank test;Motion tracking;Article;breathing;human;image quality;image reconstruction;in vivo study;liver;nuclear magnetic resonance imaging;organ motion;prediction;retrospective study;signal noise ratio;software;imaging phantom;motion;Humans;Magnetic Resonance Imaging;Motion;Phantoms, Imaging;Respiration;Retrospective Studies | 公開日期: | 2020 | 卷: | 67 | 期: | 6 | 起(迄)頁: | 1727-1738 | 來源出版物: | IEEE Transactions on Biomedical Engineering | 摘要: | Objective: Magnetic resonance imaging (MRI) can provide guidance for interventions in organs affected by respiration (e.g., liver). This study aims to: 1) investigate image-based and surrogate-based motion tracking methods using real-time golden-angle radial MRI; and 2) propose and evaluate a new fusion-based respiratory motion prediction framework with multi-rate Kalman filtering. Methods: Images with different temporal footprints were reconstructed from the same golden-angle radial MRI data stream to simultaneously enable image-based and surrogate-based tracking at 10 Hz. A custom software pipeline was constructed to perform online tracking and calibrate tracking error and latency using a programmable motion phantom. A fusion-based motion prediction method was developed to combine the lower tracking error of image-based tracking with the lower latency of surrogate-based tracking. The fusion-based method was evaluated in retrospective studies using in vivo real-time free-breathing liver MRI. Results: Phantom experiments confirmed that the median online tracking error of image-based tracking was lower than surrogate-based methods, however, with higher median system latency (350 ms vs. 150 ms). In retrospective in vivo studies, 75 respiratory waveforms of target features from eight subjects were analyzed. The median root-mean-squared prediction error (RMSE) for the fusion-based method (0.97 mm) was reduced (Wilcoxon signed rank test p < 0.05) compared to surrogate-based (1.18 mm) and image-based (1.3 mm) methods. Conclusion: The proposed fusion-based respiratory motion prediction framework using golden-angle radial MRI can achieve low-latency feedback with improved accuracy. Significance: Respiratory motion prediction using the fusion-based method can overcome system latency to provide accurate feedback information for MRI-guided interventions. ? 1964-2012 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080032031&doi=10.1109%2fTBME.2019.2944803&partnerID=40&md5=3bb2e7dc77f9d1e89af696ff5d86dd1e https://scholars.lib.ntu.edu.tw/handle/123456789/611435 |
DOI: | 10.1109/TBME.2019.2944803 |
顯示於: | 機械工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。