University of Dundee A focused ultrasound treatment system for moving targets ( part I )

Background: 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. Methods: 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. Results: 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. Conclusions: 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. *Correspondence: michael.schwenke@mevis.fraunhofer.de 1Fraunhofer Institute for Medical Image Computing MEVIS, Am Fallturm 1, 28359 Bremen, Germany Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Schwenke et al. Journal of Therapeutic Ultrasound (2017) 5:20 Page 2 of 14 Background Focused ultrasound (FUS) is increasingly entering clinical routine as an alternative treatment method in different indications [1]. Current clinical FUS applications mostly target static organs. FUS treatment of abdominal targets that move with the respiration of the patient (e.g. the liver) is not yet an available option in clinical routine due to the lack of treatment systems supporting this functionality. The current clinical use cases of FUS for moving abdominal organs include both palliative treatments and patients for whom resection is impossible due to the position of the malignant tissue or co-morbidities [2, 3]. Treating targets in abdominal organs like the liver with FUS is challenging [2]: The target moves with the respiration and may be partially blocked by the ribcage for parts of the respiratory cycle. Real-time temperature monitoring of the treatment needs to be performed using MR thermometry [4]. For moving organs this is even more complicated than for static organs. Thermometry using ultrasound imaging is possible [5], but not yet available for clinical use. To tackle these challenges, in today’s FUS treatment of moving abdominal targets, the motion of the target needs to be controlled under anesthesia [6–8]: One option is to stop the respiration (apnea) for the duration of active ultrasound application and afterwards let the respiration normalize before starting the next sonication. This procedure, however, results in long treatment duration and the need for general anesthesia. Another approach that is close to clinical application is respiratory-gated sonication [9]. Thereby, the therapeutic ultrasound is active during the respiratory cycle only when the target is detected to be in a certain position. This approach allows for a treatment without general anesthesia, but suffers also from overly long treatment duration due to the reduction of the FUS duty cycle. In the long run, FUS can become competitive to other thermal ablation techniques as radio-frequency ablation (RFA), microwave ablation (MWA), laser-induced thermotherapy (LITT), and cryoablation. It may even become an alternative to resection and radiosurgery.Whether FUS can be competitive to these well-established techniques strongly depends on results of current and future clinical application of the treatment. In our research we hypothesize that the full potential of FUS in moving organs can be unleashed only with dedicated computer support in planning and control of the treatment. In this paper we present our efforts from the past years in developing model-based software support for FUS in the moving liver. An integrated system has been developed that shall allow to treat the liver during motion. Here we focus in particular on the system design and on our framework for in-silico testing of the system. We also describe our efforts to simplify the translation of research results to prototypes that may be used in clinical studies. A major effort in developing a software system for treatment planning and control is quality assurance (QA): Making sure that the system does what it is supposed to do (efficacy), and, most importantly, that it does no harm (safety). For safety-critical systems estimates for the proportion of life cycle costs spent on QA and testing go up to 80% [10]. Thus, for industry, the hurdle to develop a FUS treatment system with motion compensation is high because of the risk that FUS may in the end turn out to not be competitive to the above-mentioned established ablation methods. From the viewpoint of research and development, changing currently available clinical FUS treatment systems to make them capable of motion-compensated FUS is difficult as the clinically approved systems are not open for extensions and are subject to closed specifications. Also, introducing changes to the certified systems means that any validation needs to be re-performed. For systems of such complexity the effort required for a change in the system grows drastically the later a defect is detected or a new feature should be incorporated (exponential cost-of-change curve [11]). In our work, we define the requirements and design specification in an open way to simplify extension and collaboration. We aim at reducing the necessary QA efforts by code generation and extensive continuous testing. A virtual system using numerical FUS simulations has been developed and used for first-stage numerical experimental validation. Using this virtual system, automatic execution of system tests is possible and system design parameter studies can be performed to analyze the efficiency of the system. This way, the effort for actual real-world experimentation and validation can be reduced allowing for a more changeable system. Contribution In this article we report on our development of a FUS treatment system including motion compensation with the long-term goal of meeting the requirements for a “Class III Medical Product”. The system allows to perform a single-target motion-compensated sonication during respiratory motion. It is specified and defined in a generic way abstracting from actual hardware components. A first implementation of the system has been performed with clinically available hardware, i.e. an MR device (GE Healthcare Systems, Chicago, USA) and FUS device (Conformal Bone System 2100, InSightec Ltd., Tirat Camel, Israel). To facilitate the translation of FUS research results to the clinic, the treatment system is defined open for extensions and the use with hardware devices of other vendors and may be used as a research platform for FUS experimentation. Schwenke et al. Journal of Therapeutic Ultrasound (2017) 5:20 Page 3 of 14 A virtual system using numerical FUS simulations is presented and used for first-stage numerical experimental validation. The treatment system interacts with the emulation in the same way it does with real hardware and is provided with realistic data that is influenced by its control commands. A comprehensive numerical FUS simulation during respiratory motion (previously described in detail [12]) is used to predict the treatment effects that would happen in a real FUS experiment. Using this virtual system, automatic execution of system tests is possible and system design parameter studies are performed to analyze the efficiency of the system. One advantage of this approach is that the numerical experiments can easily be observed in the simulation whereas physical measurements in real-world experiments are challenging, time-consuming, and require a lot of resources. Related work Motion-compensation for FUS. The challenges for FUS treatment systems imposed by respiratory motion have been addressed by several stud