Computational Bioengineering and Control Lab, The University of Texas at San Antonio, USA.
Int J Hyperthermia. 2011;27(8):751-61. doi: 10.3109/02656736.2011.611962.
In this article, the major idea and mathematical aspects of model-based planning and real-time predictive control for laser-induced thermal therapy (LITT) are presented. In particular, a computational framework and its major components developed by authors in recent years are reviewed. The framework provides the backbone for not only treatment planning but also real-time surgical monitoring and control with a focus on MR thermometry enabled predictive control and applications to image-guided LITT, or MRgLITT. Although this computational framework is designed for LITT in treating prostate cancer, it is further applicable to other thermal therapies in focal lesions induced by radio-frequency (RF), microwave and high-intensity-focused ultrasound (HIFU). Moreover, the model-based dynamic closed-loop predictive control algorithms in the framework, facilitated by the coupling of mathematical modelling and computer simulation with real-time imaging feedback, has great potential to enable a novel methodology in thermal medicine. Such technology could dramatically increase treatment efficacy and reduce morbidity.
本文介绍了基于模型的规划和实时预测控制在激光诱导热疗（LITT）中的主要思想和数学方面。特别是，本文回顾了作者近年来开发的计算框架及其主要组成部分。该框架不仅为治疗计划提供了基础，而且还为实时手术监测和控制提供了基础，重点是基于磁共振测温的预测控制以及对图像引导 LITT 或 MRgLITT 的应用。尽管这个计算框架是为治疗前列腺癌的 LITT 设计的，但它也可以进一步应用于其他由射频（RF）、微波和高强度聚焦超声（HIFU）引起的局灶性病变的热疗。此外，该框架中的基于模型的动态闭环预测控制算法，通过数学建模和计算机模拟与实时成像反馈的结合，为热医学提供了一种新的方法，具有很大的潜力。这种技术可以显著提高治疗效果，降低发病率。
