Multiview 3-D Echocardiography Image Fusion with Mutual Information Neural Estimation

Multiview three-dimensional echocardiography (M3DE) fuses volumetric datasets acquired from complementary acoustic windows to expand field-of-view and allow for visualization of the entire heart. This is of great importance for cardiac chamber quantification. The M3DE also allows for image quality improvement through fusion of single views on overlapping regions. However, shape variations and increase in noise stemming from the nature of ultrasound physics make fusion a challenging task. This study proposes a novel machine learning-based fusion method to combine ultrasound views that are spatially apart, namely, apical and parasternal. Our method jointly uses: 1) an autoencoder framework to generate the fused image; and 2) a mutual information neural estimation network to maximize the mutual information between source and fused images. The experimental evaluations show promising results and the fused image generated by the proposed method improves the signal-to-noise ratio by up to 18.23 dB and the contrast-to-noise ratio by up to 21.76 dB compared to the state-of-art approaches.