Investigating 3D echocardiography image fusion for improving image quality

3D echocardiography offers the ability to perform cardiac functional analysis by visualizing the full 3D geometry of the heart. The full potential of 3D echocardiography has still not been achieved due to problems with image quality and automated quantitative analysis. Native single-view images often lack sufficient anatomical information and are low in contrast and noisy in nature due to poor acoustic window and ultrasound physics limitations. In this work, we explore various ways of fusing the multiple single-view 3D echocardiography images in order to obtain a complete 3D view of the heart by preserving maximum salient information from individual images. Three fusion techniques have been explored for image fusion that include: maximum, averaging, and wavelet image fusion. A novel method of 3D echocardiography fusion utilizing principal component analysis is proposed and a comparative analysis of all discussed techniques is conducted Results obtained from 10 subjects demonstrate that 3D echocardiography image fusion helps in improving quantitative evaluation measures SNR, CNR and contrast while extending FOV and thus filling the missing information in the individual source images. It is hoped that this improved image quality leads to an improved cardiac functional analysis as the multi-view fused image shows the whole picture of the heart.