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<p>Myocardial infarction, a leading cause of mortality worldwide, leaves survivors at significant risk of recurrence caused by scar-related re-entrant ventricular tachyarrhythmias. Effective treatment with ablation therapy requires a precise guidance system. Non-linear optical microscopy techniques, such as second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), are promising candidates for a high-resolution alternative to conventional electrical mapping for assessing infarcted cardiac tissue. Here, we apply SHG and TPEF with a resolution advantage over commonly used electrical mapping techniques to assess ex-vivo sheep heart infarction. Analyzing conventional and radiomic features allows for quantitative characterization of scar tissue. Our machine learning classifier achieved high accuracy, offering a promising, data-driven approach for guiding in-situ ablation therapy with increased precision. This study represents a significant step towards integrating quantitative image analysis in therapeutic interventions.</p>

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Comparison of conventional and radiomics-based analysis of myocardial infarction using multimodal non-linear optical microscopy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月18日

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  • Gabriel Giardina,
  • Laszlo Papp,
  • Arno Krause,
  • James Marchant,
  • Nestor Pallares-Lupon,
  • Kanchan Kulkarni,
  • Clemens P. Spielvogel,
  • David Haberl,
  • Xu Li,
  • Wolfgang Drexler,
  • Richard D. Walton,
  • Angelika Unterhuber,
  • Marco Andreana

摘要

Myocardial infarction, a leading cause of mortality worldwide, leaves survivors at significant risk of recurrence caused by scar-related re-entrant ventricular tachyarrhythmias. Effective treatment with ablation therapy requires a precise guidance system. Non-linear optical microscopy techniques, such as second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), are promising candidates for a high-resolution alternative to conventional electrical mapping for assessing infarcted cardiac tissue. Here, we apply SHG and TPEF with a resolution advantage over commonly used electrical mapping techniques to assess ex-vivo sheep heart infarction. Analyzing conventional and radiomic features allows for quantitative characterization of scar tissue. Our machine learning classifier achieved high accuracy, offering a promising, data-driven approach for guiding in-situ ablation therapy with increased precision. This study represents a significant step towards integrating quantitative image analysis in therapeutic interventions.