Schwenke Michael, Strehlow Jan, Demedts Daniel, Haase Sabrina, Barrios Romero Diego, Rothlübbers Sven, von Dresky Caroline, Zidowitz Stephan, Georgii Joachim, Mihcin Senay, Bezzi Mario, Tanner Christine, Sat Giora, Levy Yoav, Jenne Jürgen, Günther Matthias, Melzer Andreas, Preusser Tobias
Fraunhofer Institute for Medical Image Computing MEVIS, Am Fallturm 1, Bremen, 28359 Germany.
Mediri, Heidelberg, Germany.
J Ther Ultrasound. 2017 Jul 24;5:20. doi: 10.1186/s40349-017-0098-7. eCollection 2017.
Focused ultrasound (FUS) is entering clinical routine as a treatment option. Currently, no clinically available FUS treatment system features automated respiratory motion compensation. The required quality standards make developing such a system challenging.
A novel FUS treatment system with motion compensation is described, developed with the goal of clinical use. The system comprises a clinically available MR device and FUS transducer system. The controller is very generic and could use any suitable MR or FUS device. MR image sequences (echo planar imaging) are acquired for both motion observation and thermometry. Based on anatomical feature tracking, motion predictions are estimated to compensate for processing delays. FUS control parameters are computed repeatedly and sent to the hardware to steer the focus to the (estimated) target position. All involved calculations produce individually known errors, yet their impact on therapy outcome is unclear. This is solved by defining an intuitive quality measure that compares the achieved temperature to the static scenario, resulting in an overall efficiency with respect to temperature rise. To allow for extensive testing of the system over wide ranges of parameters and algorithmic choices, we replace the actual MR and FUS devices by a virtual system. It emulates the hardware and, using numerical simulations of FUS during motion, predicts the local temperature rise in the tissue resulting from the controls it receives.
With a clinically available monitoring image rate of 6.67 Hz and 20 FUS control updates per second, normal respiratory motion is estimated to be compensable with an estimated efficiency of 80%. This reduces to about 70% for motion scaled by 1.5. Extensive testing (6347 simulated sonications) over wide ranges of parameters shows that the main source of error is the temporal motion prediction. A history-based motion prediction method performs better than a simple linear extrapolator.
The estimated efficiency of the new treatment system is already suited for clinical applications. The simulation-based in-silico testing as a first-stage validation reduces the efforts of real-world testing. Due to the extensible modular design, the described approach might lead to faster translations from research to clinical practice.
聚焦超声（FUS）作为一种治疗选择正在进入临床常规应用。目前，尚无临床可用的FUS治疗系统具备自动呼吸运动补偿功能。所需的质量标准使得开发这样一个系统具有挑战性。
描述了一种具有运动补偿功能的新型FUS治疗系统，其开发目标是临床应用。该系统包括一台临床可用的磁共振设备和FUS换能器系统。控制器非常通用，可以使用任何合适的磁共振或FUS设备。采集磁共振图像序列（回波平面成像）用于运动观察和温度测量。基于解剖特征跟踪，估计运动预测以补偿处理延迟。反复计算FUS控制参数并发送到硬件，将焦点引导到（估计的）目标位置。所有涉及的计算都会产生各自已知的误差，但其对治疗结果的影响尚不清楚。通过定义一种直观的质量度量来解决这个问题，该度量将达到的温度与静态场景进行比较，从而得出关于温度升高的总体效率。为了在广泛的参数和算法选择范围内对系统进行广泛测试，我们用一个虚拟系统取代了实际的磁共振和FUS设备。它模拟硬件，并利用运动过程中FUS的数值模拟，预测其接收到的控制所导致的组织局部温度升高。
在临床可用的监测图像速率为6.67 Hz且每秒进行20次FUS控制更新的情况下，正常呼吸运动估计可补偿，估计效率为80%。对于放大1.5倍的运动，该效率降至约70%。在广泛的参数范围内进行的大量测试（6347次模拟超声治疗）表明，误差的主要来源是时间运动预测。基于历史的运动预测方法比简单的线性外推器表现更好。
新治疗系统的估计效率已适用于临床应用。基于模拟的虚拟测试作为第一阶段验证减少了实际测试的工作量。由于可扩展的模块化设计，所描述的方法可能会加快从研究到临床实践的转化。
