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

Area of Science:

• Cardiovascular Imaging
• Medical Robotics
• Image Processing

Background:

• Accurate assessment of left ventricular (LV) function via three-dimensional echocardiography (3-DE) is often hindered by poor image quality and limited field of view.
• Existing 3-DE techniques face challenges in achieving consistent and high-quality image acquisition for comprehensive LV analysis.

Purpose of the Study:

• To enhance left ventricular (LV) image quality in three-dimensional echocardiography (3-DE) using a novel robotic-arm-assisted acquisition protocol.
• To improve LV assessment by combining multi-apical view fusion with wavelet-based image processing techniques.

Main Methods:

• Utilized a UR10e robotic arm integrated with an ultrasound system for standardized multi-apical 3-DE acquisition.
• Employed a wavelet-based fusion approach to combine two-view and three-view apical datasets, processing volumes using 3-D Slicer.
• Quantitatively assessed image quality using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), alongside qualitative evaluation by expert raters.

Main Results:

• Wavelet-based fusion significantly improved image quality, evidenced by increased SNR (9.36 ± 5.03 vs. 7.09 ± 4.44) and CNR (1.68 ± 0.54 vs. 1.49 ± 0.57) compared to single-view 3-DE (p < 0.0001).
• Three-view fusion demonstrated superior quantitative improvements over two-view fusion.
• Blinded expert assessments showed fused images were consistently rated as equal or superior in clarity, border continuity, and diagnostic confidence, with substantial inter-rater agreement.

Conclusions:

• Robotic-arm-assisted acquisition and wavelet-based fusion of multi-apical 3-DE images substantially enhance LV image quality and diagnostic value.
• The robotic arm ensures reproducible probe positioning, crucial for successful image alignment and fusion, thereby improving the reliability of 3-DE.
• This integrated approach shows promise for advancing robotic-assisted cardiac imaging, with potential for further improvements using deep learning methods.