Abstract
Introduction The Coronary Artery (CA) rest period in the cardiac cycle varies substantially from patient to patient [Wang 1999]. The cross-correlations between images of consecutive heart phases may be used to characterize the cardiac motion, and an optimal acquisition window could be automatically identified [Nehrke 2003]. For selecting an optimal data acquisition window, an automated placing of a Region of Interest (ROI) without user interaction was implemented, and yielded similar result compared to the visual assessment [Jahnke 2005; Maruyama 2007). Combined with user interaction in placing an ROI, the semiautomated approach was developed [Uneno 2009]. However, accurate identification of an ROI still needs to be enhanced.
Highlights
The Coronary Artery (CA) rest period in the cardiac cycle varies substantially from patient to patient [Wang 1999]
For selecting an optimal data acquisition window, an automated placing of a Region of Interest (ROI) without user interaction was implemented, and yielded similar result compared to the visual assessment [Jahnke 2005; Maruyama 2007)
Gated cine images from Twenty five health volunteers were acquired using 1.5 Tesla Scanner (Toshiba Medical Systems Corporation, Japan). 2D SSFP images (50-70 phases/cardiac cycles) in 4 chamber orientation were acquired with FOV of 350 mm and slice thickness of 10 mm
Summary
The Coronary Artery (CA) rest period in the cardiac cycle varies substantially from patient to patient [Wang 1999]. The cross-correlations between images of consecutive heart phases may be used to characterize the cardiac motion, and an optimal acquisition window could be automatically identified [Nehrke 2003]. For selecting an optimal data acquisition window, an automated placing of a Region of Interest (ROI) without user interaction was implemented, and yielded similar result compared to the visual assessment [Jahnke 2005; Maruyama 2007). Combined with user interaction in placing an ROI, the semiautomated approach was developed [Uneno 2009]. Accurate identification of an ROI still needs to be enhanced. To compares the performance of the developed automated and semi-automated approach for rest period detection with the visual assessment by two radiologist
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