Chong Chen1, Preethi Chandrasekaran2, Yingmin Liu2, Orlando P Simonetti2,3,4, Matthew Tong3, Rizwan Ahmad1,2,5. 1. Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA. 2. Davis Heart & Lung Research Institute, The Ohio State University, Columbus, Ohio, USA. 3. Internal Medicine, The Ohio State University, Columbus, Ohio, USA. 4. Radiology, The Ohio State University, Columbus, Ohio, USA. 5. Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA.
Abstract
PURPOSE: To develop an automatic method for selecting heartbeats with consistent respiratory phase to improve accuracy of cardiac function quantification in real-time (RT) cardiac MRI. METHODS: The respiratory signal is extracted by a principal component analysis method from RT cine images. Then, a two-step procedure is used to determine the directionality (sign) of the respiratory signal. With the motion in a manually selected region-of-interest as a reference, the quality of the extracted respiratory signal is assessed using multislice RT cine data from 11 volunteers and 10 patients. In addition, the impact of selecting heartbeats with consistent respiratory phase on the cardiac function quantification is evaluated. RESULTS: The extracted respiratory signal using the proposed method exhibits a high, positive correlation with the reference in all cases and is more robust compared to a recently proposed method. Also, for right ventricular function quantification, selecting heartbeats at expiratory position improves agreement between RT cine and breath-held reference. CONCLUSION: The proposed method enables fully automatic extraction and directionality determinations of respiratory signal from RT cardiac cine images, allowing accurate cardiac function quantification.
PURPOSE: To develop an automatic method for selecting heartbeats with consistent respiratory phase to improve accuracy of cardiac function quantification in real-time (RT) cardiac MRI. METHODS: The respiratory signal is extracted by a principal component analysis method from RT cine images. Then, a two-step procedure is used to determine the directionality (sign) of the respiratory signal. With the motion in a manually selected region-of-interest as a reference, the quality of the extracted respiratory signal is assessed using multislice RT cine data from 11 volunteers and 10 patients. In addition, the impact of selecting heartbeats with consistent respiratory phase on the cardiac function quantification is evaluated. RESULTS: The extracted respiratory signal using the proposed method exhibits a high, positive correlation with the reference in all cases and is more robust compared to a recently proposed method. Also, for right ventricular function quantification, selecting heartbeats at expiratory position improves agreement between RT cine and breath-held reference. CONCLUSION: The proposed method enables fully automatic extraction and directionality determinations of respiratory signal from RT cardiac cine images, allowing accurate cardiac function quantification.
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