Real-time myocardial segmentation using coupled active geometric functions.
Myocardial segmentation is essential for quantitative evaluation of cardiac functional images. As imaging techniques advance, 3D and 4D image data have become available. These data can provide clinically important cardiac dynamic information at high spatial or temporal resolution. However, the enormous amount of information contained in these data has also raised a challenge for traditional image analysis algorithms in terms of efficiency and clinical workflow. In this context, an automated real-time myocardial segmentation framework based on coupled Active Geometric Functions was proposed and tested on 414 frames of Phase Train Imaging data, a real-time cardiac MR imaging technique, with an average temporal resolution of 2 ms. The performance of myocardial segmentation was visually and quantitatively validated. Implemented in Matlab(c), the current method takes less than 1.2 ms per cardiac phase, allowing realization of true real-time online segmentation.
Duan
Qi
Q
Department of Biomedical Engineering, Columbia University, New York 10027, USA. qd2002@columbia.edu
Laine
Andrew F
AF
Pai
Vinay M
VM
D000465:Algorithms; D000818:Animals; D004699:Endocardium / Q000473:pathology; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D008962:Models, Theoretical; D009206:Myocardium / Q000473:pathology*; D010363:Pattern Recognition, Automated; D010496:Pericardium / Q000473:pathology; D051381:Rats; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors