Respiratory motion prediction using fusion-based multi-rate kalman filtering and real-time golden-angle radial mri
Journal
IEEE Transactions on Biomedical Engineering
Journal Volume
67
Journal Issue
6
Pages
1727-1738
Date Issued
2020
Author(s)
Abstract
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.
Subjects
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
Type
journal article