Auto-labeling of respiratory time points in free-breathing thoracic dynamic MR image acquisitions for 4D image construction

Changjian Sun; Jayaram K. Udupa; Yubing Tong; Caiyun Wu; Shuxu Guo; Joseph M. McDonough; Drew A. Torigian; Robert M. Campbell

doi:10.1117/12.2513218

15 March 2019 Auto-labeling of respiratory time points in free-breathing thoracic dynamic MR image acquisitions for 4D image construction

Changjian Sun, Jayaram K. Udupa, Yubing Tong, Caiyun Wu, Shuxu Guo, Joseph M. McDonough, Drew A. Torigian, Robert M. Campbell

Author Affiliations +

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109531B (2019) https://doi.org/10.1117/12.2513218
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Determining the EE (End of Expiration) and EI (End of Inspiration) time points in the respiratory cycle is one key step during the 4D image construction from free-breathing dynamic thoracic computed tomography (CT) or magnetic resonance imaging (MRI) acquisitions. However, the cost of manually labeling EE and EI time points is extensive. An automatic image-based EE and EI labeling method makes image annotation independent of the image acquisition process, avoiding use of internal or external markers for the patient during image acquisition. The purpose of this paper is to introduce a novel optical-flow-based technique for finding EE and EI time points from dynamic thoracic MRI acquired during natural tidal-breathing. The diaphragm is tracked as a marker to determine the state of breathing. A region of interest (ROI) containing the diaphragm is selected to calculate the pixel optical flow values between two adjacent time slices. The average optical flow values of all pixels including diaphragm motion speed is used as a reference for labeling EE and EI. When the direction of movement of the diaphragm changes, EE or EI is found depending on the direction of the change. Quantitative evaluation was carried out to evaluate the effectiveness of our method in different locations in the lungs as compared to manual labeling. When tested on 28 patient dynamic thoracic MRI data sets, the average error was found to be less than 1 time point. Automatic labeling greatly shortened the labeling time, requiring less than 8 minutes compared to 4 hours for manual labeling per study.

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Changjian Sun, Jayaram K. Udupa, Yubing Tong, Caiyun Wu, Shuxu Guo, Joseph M. McDonough, Drew A. Torigian, and Robert M. Campbell "Auto-labeling of respiratory time points in free-breathing thoracic dynamic MR image acquisitions for 4D image construction", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109531B (15 March 2019); https://doi.org/10.1117/12.2513218

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