Robotic-Arm Assisted Multi-Apical View 3-D Fusion of Echocardiography for Enhanced Left Ventricular Assessment Using...

Accurate assessment of left ventricular (LV) function using three-dimensional echocardiography (3-DE) remains limited by suboptimal image quality and restricted field of view. This study proposes a robotic-arm-assisted acquisition protocol combined with a wavelet-based multi-apical view fusion approach to enhance LV image quality in 3-DE.

Volunteer scans were acquired using a UR10e robotic arm integrated with a Philips EPIQ 7C ultrasound system to ensure consistent multi-apical 3-DE acquisition. Echocardiographic volumes were converted to NRRD format using 3-D Slicer for visualization and verification of spatial and temporal alignment. Two-view and three-view apical datasets were fused using a wavelet-based approach. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were computed from matched 2-D slices in the end-diastolic phase, and qualitative image assessment was conducted by expert raters using blinded scoring of image clarity, myocardial border continuity and diagnostic confidence.

Wavelet-based fusion significantly improved image quality compared to single-view 3-DE, with increased SNR (9.36 ± 5.03 vs. 7.09 ± 4.44, p < 0.0001) and CNR (1.68 ± 0.54 vs. 1.49 ± 0.57, p = 0.0020). Three-view fusion provided additional quantitative improvement over 2-view fusion. Inter-rater agreement on visual assessment confirmed that fused images were consistently rated as equal or superior in quality, with substantial agreement across all scoring categories.

Wavelet-based fusion of multi-apical 3-DE images acquired with robotic arm assistance significantly enhances image quality for LV assessment, improving both quantitative metrics and visual interpretability, practically with the 3-view fusion. The use of the robotic arm played a key role in ensuring standardized and reproducible probe positioning, which is essential for successful image alignment and fusion. This approach demonstrates the potential to improve the reliability and diagnostic value of 3-DE, and future work should explore incorporating additional views and deep learning methods to further advance robotic-assisted cardiac imaging.