With the continued development of non-toxic photosensitizer drugs, interstitial photodynamic therapy (iPDT) is showing more favorable outcomes in recent clinical trials. IPDT planning is crucial to further increase the treatment efficacy. However, it remains a major challenge to generate a high-quality, patient-specific plan due to uncertainty in tissue optical properties (OPs), µ_a and µ_s. These parameters govern how light propagates inside tissues, and any deviation from the planning-assumed values during treatment could significantly affect the treatment outcome. In this work, we increase the robustness of iPDT against OP variations by using machine learning models to recover the patient-specific OPs from light dosimetry measurements and then re-optimizing the diffusers’ optical powers to adapt to these OPs in real time. Simulations on virtual brain tumor models show that reoptimizing the power allocation with the recovered OPs significantly reduces uncertainty in the predicted light dosimetry for all tissues involved.

中文翻译：

---

用于间隙光动力治疗中实时光学特性恢复的机器学习：一项基于刺激的研究

随着无毒光敏剂药物的不断发展，间质光动力疗法（iPDT）在最近的临床试验中显示出更有利的结果。IPDT 计划对于进一步提高治疗效果至关重要。然而，由于组织光学特性 (OP)、μ _a和μ _{s 的}不确定性，生成高质量、针对患者的计划仍然是一个重大挑战. 这些参数控制着光在组织内的传播方式，治疗期间与计划假设值的任何偏差都可能显着影响治疗结果。在这项工作中，我们通过使用机器学习模型从光剂量测量中恢复患者特定的 OP，然后重新优化漫射器的光功率以实时适应这些 OP，从而提高了 iPDT 对 OP 变化的鲁棒性。虚拟脑肿瘤模型的模拟表明，使用恢复的 OP 重新优化功率分配可显着降低所有相关组织的预测光剂量测定的不确定性。