ArticleScientific reports2024
Enhancing boundary detection of radiofrequency ablation lesions through photoacoustic mapping.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-Analysis of Thermal Versus Pulse Field Ablation for Pulmonary Vein Isolation Durability in Atrial Fibrillation: Insights From Repeat Ablation.Clinical cardiology · 2025Pooled it
- Robot-assisted minimally invasive photoacoustic imaging for monitoring liver ablation using diffusing fiber illumination.Journal of biomedical optics · 2026Article
- Flexible bioelectronic sensors promote the advancement of wearable ultrasound technology in the medicine.Microsystems & nanoengineering · 2026Review
- Custom-designed Verasonics-based multi-wavelength photoacoustic and ultrasound imaging: from technical advances to preclinical applications in cancer.Biomedical optics express · 2025Review
- Spectroscopic photoacoustic denoising framework using hybrid analytical and data-free learning method.Photoacoustics · 2025Article
- Miniaturized side-firing intracardiac photoacoustic imaging catheter for monitoring radiofrequency ablation.Scientific reports · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
Atrial fibrillation (A-fib) is the most common type of heart arrhythmia, typically treated with radiofrequency catheter ablation to isolate the heart from abnormal electrical signals. Monitoring the formation of ablation-induced lesions is crucial for preventing recurrences and complications arising from excessive or insufficient ablation. Existing imaging modalities lack real-time feedback, and their intraoperative usage is in its early stages. A critical need exists for an imaging-based lesion indexing (LSI) method that directly reflects tissue necrosis formation. Previous studies have indicated that spectroscopic photoacoustic (sPA) imaging can differentiate ablated tissues from their non-ablated counterparts based on PA spectrum variation. In this paper, we introduce a method for detecting ablation lesion boundaries using sPA imaging. This approach utilizes ablation LSI, which quantifies the ratio between the signal from ablated tissue and the total tissue signal. We enhance boundary detection accuracy by adapting a regression model-based compensation. Additionally, the method was cross-validated with clinically used intraoperative monitoring parameters. The proposed method was validated with ex vivo porcine cardiac tissues with necrotic lesions created by different ablation durations. The PA-measured lesion size was compared with gross pathology. Statistical analysis demonstrates a strong correlation (R > 0.90) between the PA-detected lesion size and gross pathology. The PA-detected lesion size also exhibits a moderate to strong correlation (R > 0.75) with local impedance changes recorded during procedures. These results suggest that the introduced PA imaging-based LSI has great potential to be incorporated into the clinical workflow, guiding ablation procedures intraoperatively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.