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

被引:2
|
作者
Sun, Changjian [1 ,2 ]
Udupa, Jayaram K. [2 ]
Tong, Yubing [2 ]
Wu, Caiyun [2 ]
Guo, Shuxu [1 ]
McDonough, Joseph M. [3 ]
Torigian, Drew A. [2 ]
Campbell, Robert M. [3 ]
机构
[1] Jilin Univ, Coll Elect Sci & Engn, Changchun, Jilin, Peoples R China
[2] Univ Penn, Dept Radiol, Med Image Proc Grp, 602 Goddard Bldg,3710 Hamilton Walk, Philadelphia, PA 19104 USA
[3] Childrens Hosp Philadelphia, Ctr Thorac Insufficiency Syndrome, Philadelphia, PA 19104 USA
基金
美国国家卫生研究院;
关键词
INSUFFICIENCY SYNDROME;
D O I
10.1117/12.2513218
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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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页数:7
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