Focused ultrasound (FUS/HIFU) relies on ablation of pathological tissues by delivering a sufficiently high level of acoustic energy in situ of the human body. Magnetic Resonance guided FUS (MRgFUS/HIFU) and Ultrasound guided (USgFUS/HIFU) are image guided techniques combined with therapeutic FUS for monitoring purposes. The principles and technologies of FUS/HiFU are described in this paper including the basics of MR guidance techniques and MR temperature mapping. Clinical applications of FUS/HIFU gained CE and FDA approvals for the treatment of various benign and few malignant lesions in the last two decades. Current technical limitations of ultrasound guided and MRI guided Focused Ultrasound, as well as adverse effects for the application of this technique are outlined including challenges of ablating moving organs (liver and kidney). An outlook to possible applications is provided; exampling clinical trials discussing future options.
Extracting data from {Zhu, 2019 #5} daily life activities is important in biomechanical applications to define exact boundary conditions for the intended use-based applications. Although optoelectronic camera-marker based systems are used as gold standard tools for medical applications, due to line-of-sight problem, there is a need for wearable, affordable motion capture (MOCAP) systems. We investigate the potential use of a wearable inertial measurement unit (IMU) based-wearable MOCAP system for biomechanical applications. The in vitro proof of concept is provided for the full lower body consisting of hip, knee, and ankle joints via controlled single-plane anatomical range of motion (ROM) simulations using an electrical motor, while collecting data simultaneously via opto-electronic markers and IMU sensors. On 15 healthy volunteers the flexion-extension, abduction-adduction, internal-external rotation (ROM) values of hip and, the flexion – extension ROM values of the knee and ankle joints are calculated for both systems. The Bland-Altman graphs showed promising agreement both for in vitro and in vivo experiments. The maximum Root Mean Square Errors (RMSE) between the systems in vitro was 3.4° for hip and 5.9° for knee flexion motion in vivo, respectively. The gait data of the volunteers were assessed between the heel strike and toe off events to investigate the limits of agreement, calculating the population averages and standard deviation for both systems over the gait cycle. The maximum difference was for the ankle joint <6°. The results show that proposed system could be an option as an affordable-democratic solution.
Human motion capture (MOCAP) systems are vital, while determining the loads occurring at the joints. Most of the clinical MOCAP systems are very costly, requiring investment and infrastructure. Therefore, alternative technologies are in demand. In this study, a novel marker-less wearable MOCAP system, was assessed for its compatibility with a biomechanical modelling software. To collect evidence, experiments were designed in two stages for quantifying the range of motion of the hip joint; in vitro and in vivo. Three constrained-single-plane motions; abduction/adduction, flexion/extension, and internal/external rotation movements of the active leg were analysed. The data were collected from 14 healthy volunteers, using the wearable system and a medical grade optoelectronic MOCAP system simultaneously and compared against. For the in vitro study, the Root Mean Square Error (RMSE) for the abduction/adduction motion of the hip joint was calculated as 0.11°/0.30° and 0.11°/0.09° respectively for the wearable and the opto-electronic system. The in vivo Bland-Altman plots showed that the two system data are comparable. The simulation software is found compatible to run the simulations in offline mode. The wearable system could be utilized in the field of biomechanics software for running the kinetic simulations. The results demonstrated that the wearable system could be an alternative in the field of biomechanics based on the evidence collected.
BackgroundFocused 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.MethodsA 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.ResultsWith 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.ConclusionsThe 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.
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