Dynamic distribution entropy analysis via ultrafast intracardiac echocardiography for monitoring of cardiac radiofrequency ablation

Objective: To enable reliable, noise-robust monitoring of lesion formation during radiofrequency (RF) ablation with intracardiac echocardiography (ICE), this study introduces Dynamic Distribution Entropy (DDE), which quantifies temporal–spatial changes in backscatter statistics while suppressing ablation-induced interference. Methods: DDE integrates ultrafast plane-wave acquisition with singular value decomposition clutter filtering to stabilize entropy estimates. We evaluated DDE in (i) simulations modeling ablation-driven scatterer-size changes and (ii)ex-vivoporcine hearts imaged by ICE during RF ablation. DDE was compared with typical Shannon entropy, k-nearest neighbor entropy, cumulative residual entropy, and horizontally normalized Shannon entropy. Metrics included structural similarity (SSIM), intersection-over-union (IoU), and lesion-size agreement versus optical ground truth. Results: In simulations, DDE and CRE closely tracked dynamic scatterer evolution, yielding highest SSIM over frames (0.96). In ex-vivo experiments, DDE demonstrated the most accurate lesion-size estimation under RF-ON conditions, exhibiting the lowest bias (4.85 mm2), standard deviation (1.53 mm2), and root mean square error (5.08 mm2) among all evaluated entropy-based methods. Lesion sizes derived from DDE exhibited the best agreement with optical measurements. Conclusion: DDE provides robust and accurate intraoperative monitoring of lesion formation in ICE guided RF ablation, outperforming conventional entropy imaging under ablation-related noise. Significance: DDE offers a practical, noise-resistant quantitative ultrasound biomarker for real-time lesion assessment, supporting decision-making during ICE-guided cardiac ablation without requiring changes to clinical workflow.