OBJECTIVE: Determination of end-expiration (EE) and end-inspiration (EI) time points in the respiratory cycle in free-breathing slice image acquisitions of the thorax is one key step needed for 4D image construction via dynamic magnetic resonance imaging. The purpose of this paper is to realize the automation of the labeling process. METHODS: The diaphragm is used as a surrogate for tracking respiratory motion and determining the state of breathing. Regions of interest (ROIs) containing the hemi-diaphragms are set by human interaction to compute the optical flow matrix between two adjacent 2D time slices. Subsequently, our approach examines the diaphragm speed and direction and by considering the change in the optical flow matrix, the EE or EI points are detected. RESULTS AND CONCLUSION: The labeling accuracy for the lateral aspect of the left lung and the lateral aspect of the right lung (0.63±0.71) is significantly lower (P < 0.05) than the accuracy for other positions (0.42±0.44), but the error in almost all scenarios is less than 1 time point. By comparing between automatic and manual labeling in 12 scenarios, we found out that 9 scenarios showed no significant difference (P > 0.05) between two methods. Overall, our method is found to be highly agreeable with manual labeling and greatly shortens the labeling time, requiring less than 8 minutes/ study compared to 4 hours/ study for manual labeling. SIGNIFICANCE: Our method achieves automatic labeling of EE and EI points without the need for use of patientinternal or external markers.
OBJECTIVE: Determination of end-expiration (EE) and end-inspiration (EI) time points in the respiratory cycle in free-breathing slice image acquisitions of the thorax is one key step needed for 4D image construction via dynamic magnetic resonance imaging. The purpose of this paper is to realize the automation of the labeling process. METHODS: The diaphragm is used as a surrogate for tracking respiratory motion and determining the state of breathing. Regions of interest (ROIs) containing the hemi-diaphragms are set by human interaction to compute the optical flow matrix between two adjacent 2D time slices. Subsequently, our approach examines the diaphragm speed and direction and by considering the change in the optical flow matrix, the EE or EI points are detected. RESULTS AND CONCLUSION: The labeling accuracy for the lateral aspect of the left lung and the lateral aspect of the right lung (0.63±0.71) is significantly lower (P < 0.05) than the accuracy for other positions (0.42±0.44), but the error in almost all scenarios is less than 1 time point. By comparing between automatic and manual labeling in 12 scenarios, we found out that 9 scenarios showed no significant difference (P > 0.05) between two methods. Overall, our method is found to be highly agreeable with manual labeling and greatly shortens the labeling time, requiring less than 8 minutes/ study compared to 4 hours/ study for manual labeling. SIGNIFICANCE: Our method achieves automatic labeling of EE and EI points without the need for use of patientinternal or external markers.
Authors: Jie Song; Jayaram K Udupa; Yubing Tong; Liang Xiao; Caiyun Wu; Joseph McDonough; Anthony Capraro; Drew A Torigian; Robert M Campbell Journal: Proc SPIE Int Soc Opt Eng Date: 2018-03-13
Authors: Kathrin Breuer; Cord B Meyer; Felix A Breuer; Anne Richter; Florian Exner; Andreas M Weng; Serge Ströhle; Bülent Polat; Peter M Jakob; Otto A Sauer; Michael Flentje; Stefan Weick Journal: Phys Med Biol Date: 2018-03-22 Impact factor: 3.609
Authors: Jayaram K Udupa; Yubing Tong; Anthony Capraro; Joseph M McDonough; Oscar H Mayer; Suzanne Ho; Paul Wileyto; Drew A Torigian; Robert M Campbell Journal: J Pediatr Orthop Date: 2020-04 Impact factor: 2.537