下腹部を対象とした極細針によるCTガイド下高正確度穿刺プランニング

早大学位記番号:新8149早稲田大

Zerobot®: A Remote-controlled Robot for Needle Insertion in CT-guided Interventional Radiology Developed at Okayama University

Since 2012, we have been developing a remote-controlled robotic system (Zerobot®) for needle insertion during computed tomography (CT)-guided interventional procedures, such as ablation, biopsy, and drainage. The system was designed via a collaboration between the medical and engineering departments at Okayama University, including various risk control features. It consists of a robot with 6 degrees of freedom that is manipulated using an operation interface to perform needle insertions under CT-guidance. The procedure includes robot positioning, needle targeting, and needle insertion. Phantom experiments have indicated that robotic insertion is equivalent in accuracy to manual insertion, without physician radiation exposure. Animal experiments have revealed that robotic insertion of biopsy introducer needles and various ablation needles is safe and accurate in vivo. The first in vivo human trial, therefore, began in April 2018. After its completion, a larger clinical study will be conducted for commercialization of the robot. This robotic procedure has many potential advantages over a manual procedure: 1) decreased physician fatigue; 2) stable and accurate needle posture without tremor; 3) procedure automation; 4) less experience required for proficiency in needle insertion skills; 5) decreased variance in technical skills among physicians; and 6) increased likelihood of performing the procedure at remote hospitals (i.e., telemedicine)

Phlebot: The Robotic Phlebotomist

Phlebotomy is a routine task, performed over a billion times annually in the United States alone, that is essential for proper diagnosis and treatment. We designed and constructed Phlebot, a robotic assistive device that uses near- infrared imaging and force-feedback to guide a needle into a forearm vein for blood sample collection or intravenous catheterization. Through initial validation on phantoms, we show that it is feasible to automate phlebotomy reliably. We envision the device to be a first major step towards more affordable point-of-care testing and diagnostic healthcare systems. In the long term, we expect that Phlebot will expedite healthcare delivery and drastically reduce needle stick injuries, instances of hemolysis, and infections caused by blood-borne pathogens

Planning for steerable needles in neurosurgery

The increasing adoption of robotic-assisted surgery has opened up the possibility to control innovative dexterous tools to improve patient outcomes in a minimally invasive way. Steerable needles belong to this category, and their potential has been recognised in various surgical fields, including neurosurgery. However, planning for steerable catheters' insertions might appear counterintuitive even for expert clinicians. Strategies and tools to aid the surgeon in selecting a feasible trajectory to follow and methods to assist them intra-operatively during the insertion process are currently of great interest as they could accelerate steerable needles' translation from research to practical use. However, existing computer-assisted planning (CAP) algorithms are often limited in their ability to meet both operational and kinematic constraints in the context of precise neurosurgery, due to its demanding surgical conditions and highly complex environment. The research contributions in this thesis relate to understanding the existing gap in planning curved insertions for steerable needles and implementing intelligent CAP techniques to use in the context of neurosurgery. Among this thesis contributions showcase (i) the development of a pre-operative CAP for precise neurosurgery applications able to generate optimised paths at a safe distance from brain sensitive structures while meeting steerable needles kinematic constraints; (ii) the development of an intra-operative CAP able to adjust the current insertion path with high stability while compensating for online tissue deformation; (iii) the integration of both methods into a commercial user front-end interface (NeuroInspire, Renishaw plc.) tested during a series of user-controlled needle steering animal trials, demonstrating successful targeting performances. (iv) investigating the use of steerable needles in the context of laser interstitial thermal therapy (LiTT) for maesial temporal lobe epilepsy patients and proposing the first LiTT CAP for steerable needles within this context. The thesis concludes with a discussion of these contributions and suggestions for future work.Open Acces

Ultrasound-Guided Mechatronic System for Targeted Delivery of Cell-Based Cancer Vaccine Immunotherapy in Preclinical Models

Injection of dendritic cell (DC) vaccines into lymph nodes (LN) is a promising strategy for eliciting immune responses against cancer, but these injections in mouse cancer models are challenging due to the small target scale (~ 1 mm × 2 mm). Direct manual intranodal injection is difficult and can cause architectural damage to the LN, potentially disrupting crucial interactions between DC and T cells. Therefore, a second-generation ultrasound-guided mechatronic device has been developed to perform this intervention. A targeting accuracy of \u3c 500 μm will enable targeted delivery of the DCs specifically to a LN subcapsular space. The device was redesigned from its original CT-guided edition, which used a remote centre of motion architecture, to be easily integrated onto a commercially available VisualSonics imaging rail system. Subtle modifications were made to ensure simple workflow that allows for live-animal interventions that fall within the knockout periods stated in study protocols. Several calibration and registration techniques were developed in order to achieve an overall targeting accuracy appropriate for the intended application. A variety of methods to quantify the positioning accuracy of the device were investigated. The method chosen involved validating a guided injection into a tissue-mimicking phantom using ultrasound imaging post-operatively to localize the end-point position of the needle tip in the track left behind by the needle. Ultrasound-guided injections into a tissue-mimicking phantom revealed a targeting accuracy of 285 ± 94 μm for the developed robot compared to 508 ± 166 μm for a commercial-available manually-actuated injection device from VisuailSonics. The utility of the robot was also demonstrated by performing in vivo injections into the lymph nodes of mice

Design, Development, and Evaluation of a Teleoperated Master-Slave Surgical System for Breast Biopsy under Continuous MRI Guidance

The goal of this project is to design and develop a teleoperated master-slave surgical system that can potentially assist the physician in performing breast biopsy with a magnetic resonance imaging (MRI) compatible robotic system. MRI provides superior soft-tissue contrast compared to other imaging modalities such as computed tomography or ultrasound and is used for both diagnostic and therapeutic procedures. The strong magnetic field and the limited space inside the MRI bore, however, restrict direct means of breast biopsy while performing real-time imaging. Therefore, current breast biopsy procedures employ a blind targeting approach based on magnetic resonance (MR) images obtained a priori. Due to possible patient involuntary motion or inaccurate insertion through the registration grid, such approach could lead to tool tip positioning errors thereby affecting diagnostic accuracy and leading to a long and painful process, if repeated procedures are required. Hence, it is desired to develop the aforementioned teleoperation system to take advantages of real-time MR imaging and avoid multiple biopsy needle insertions, improving the procedure accuracy as well as reducing the sampling errors. The design, implementation, and evaluation of the teleoperation system is presented in this dissertation. A MRI-compatible slave robot is implemented, which consists of a 1 degree of freedom (DOF) needle driver, a 3-DOF parallel mechanism, and a 2-DOF X-Y stage. This slave robot is actuated with pneumatic cylinders through long transmission lines except the 1-DOF needle driver is actuated with a piezo motor. Pneumatic actuation through long transmission lines is then investigated using proportional pressure valves and controllers based on sliding mode control are presented. A dedicated master robot is also developed, and the kinematic map between the master and the slave robot is established. The two robots are integrated into a teleoperation system and a graphical user interface is developed to provide visual feedback to the physician. MRI experiment shows that the slave robot is MRI-compatible, and the ex vivo test shows over 85%success rate in targeting with the MRI-compatible robotic system. The success in performing in vivo animal experiments further confirm the potential of further developing the proposed robotic system for clinical applications

CMUT based chemical sensor for classification and quantification with machine learning in a real-world application

In a quest for further enhancing human senses, chemical sensors are developed. Chemical sensors are proved to diagnose diseases, classify and quantify chemical warfare agents as well as measuring air pollution down to parts per billion [1-3]. Connecting multiple devices in large networks can help authorities and governments respond faster and make better decisions considering the release of emissions and/or dangerous gases. In order to create such networks, an inexpensive, robust and portable sensor must be developed. The chemical capacitive micromachined ultrasonic transducer (CMUT) might be such a sensor. This thesis demonstrates a proof of concept for a CMUT based chemical sensor as a gas detecting unit that can classify and quantify chemicals with machine learning in a real-world application. The CMUT is a sensor consisting of an array of polymer coated cells adsorbing different gases. Adsorption causes a frequency shift in the sensor output. This shift can be correlated to chemicals and their concentrations through machine learning. Reference data collected for the machine learning models was identified as a time-consuming process. An autosampler was devised, reducing time and cost related to the data collection. The CMUT sensor was tested in a greenhouse for 4 weeks to measure CO2 concentration in a plant bed under varying conditions. Testing the following statement: If the sensor can detect low concentrations of CO2 in ambient air it can also detect other compounds. The machine learning models were trained on the collected samples, and later compared to find the best model. The results showed that the CMUT sensor successfully measured CO2 down to 120 ppm in ambient air, the machine learning models could classify between high and low concentrations. For classification purposes the neural network with relu activation showed the best results, with a 15% error for both high and low concentrations. Quantification of the data had poor performance due to sensor drift. Large RMSE scores was found for all quantification models. The drift is most likely caused by the breakdown of the polymer, causing a frequency shift. The dataset was unbalanced and had a higher distribution on lower concentrations. Which to some extent undermine the results from the machine learning, although giving an indication of sensor performance. Further research is recommended to assess the polymer coating on the CMUT as well as removing drift. Reducing the size of the sensor and equipment, as well as connecting the sensor to a cloud database, is recommended and identified as important steps for creating a sensor network.I søken etter å forbedre menneskets sanser ønsker man å utvikle kjemiske sensorer. Kjemiske sensorer har blitt brukt til å diagnostisere sykdommer, klassifisere og kvantifisere nervegass i tillegg til å måle luftforurensing som har svært lav oppløsning. Ved å sette sammen flere elektroniske neser i større nettverk vil det bidra med økt informasjon om utslipp i byer. Dette vil hjelpe myndigheter med å ta bedre og raskere beslutninger for å unngå spredning av farlige kjemikalier og/eller forurensning. For å lage slike nettverk må sensorene som benyttes være pålitelige, kostnadseffektive og robuste. En sensor som oppfyller disse kravene er den kjemiske kapasitive mikromaskinerte ultralyd transduceren (CMUT).M-MP

Cable-driven parallel mechanisms for minimally invasive robotic surgery

Minimally invasive surgery (MIS) has revolutionised surgery by providing faster recovery times, less post-operative complications, improved cosmesis and reduced pain for the patient. Surgical robotics are used to further decrease the invasiveness of procedures, by using yet smaller and fewer incisions or using natural orifices as entry point. However, many robotic systems still suffer from technical challenges such as sufficient instrument dexterity and payloads, leading to limited adoption in clinical practice. Cable-driven parallel mechanisms (CDPMs) have unique properties, which can be used to overcome existing challenges in surgical robotics. These beneficial properties include high end-effector payloads, efficient force transmission and a large configurable instrument workspace. However, the use of CDPMs in MIS is largely unexplored. This research presents the first structured exploration of CDPMs for MIS and demonstrates the potential of this type of mechanism through the development of multiple prototypes: the ESD CYCLOPS, CDAQS, SIMPLE, neuroCYCLOPS and microCYCLOPS. One key challenge for MIS is the access method used to introduce CDPMs into the body. Three different access methods are presented by the prototypes. By focusing on the minimally invasive access method in which CDPMs are introduced into the body, the thesis provides a framework, which can be used by researchers, engineers and clinicians to identify future opportunities of CDPMs in MIS. Additionally, through user studies and pre-clinical studies, these prototypes demonstrate that this type of mechanism has several key advantages for surgical applications in which haptic feedback, safe automation or a high payload are required. These advantages, combined with the different access methods, demonstrate that CDPMs can have a key role in the advancement of MIS technology.Open Acces

Force-Sensing-Based Multi-Platform Robotic Assistance for Vitreoretinal Surgery

Vitreoretinal surgery aims to treat disorders of the retina, vitreous body, and macula, such as retinal detachment, diabetic retinopathy, macular hole, epiretinal membrane and retinal vein occlusion. Challenged by several technical and human limitations, vitreoretinal practice currently ranks amongst the most demanding fields in ophthalmic surgery. Of vitreoretinal procedures, membrane peeling is the most common to be performed, over 0.5 million times annually, and among the most prone to complications. It requires an extremely delicate tissue manipulation by various micron scale maneuvers near the retina despite the physiological hand tremor of the operator. In addition, to avoid injuries, the applied forces on the retina need to be kept at a very fine level, which is often well below the tactile sensory threshold of the surgeon. Retinal vein cannulation is another demanding procedure where therapeutic agents are injected into occluded retinal veins. The feasibility of this treatment is limited due to challenges in identifying the moment of venous puncture, achieving cannulation and maintaining it throughout the drug delivery period. Recent advancements in medical robotics have significant potential to address most of the challenges in vitreoretinal practice, and therefore to prevent traumas, lessen complications, minimize intra-operative surgeon effort, maximize surgeon comfort, and promote patient safety. This dissertation presents the development of novel force-sensing tools that can easily be used on various robotic platforms, and robot control methods to produce integrated assistive surgical systems that work in partnership with surgeons against the current limitations in vitreoretinal surgery, specifically focusing on membrane peeling and vein cannulation procedures. Integrating high sensitivity force sensing into the ophthalmic instruments enables precise quantitative monitoring of applied forces. Auditory feedback based upon the measured forces can inform (and warn) the surgeon quickly during the surgery and help prevent injury due to excessive forces. Using these tools on a robotic platform can attenuate hand tremor of the surgeon, which effectively promotes tool manipulation accuracy. In addition, based upon certain force signatures, the robotic system can precisely identify critical instants, such as the venous puncture in retinal vein cannulation, and actively guide the tool towards clinical targets, compensate any involuntary motion of the surgeon, or generate additional motion that will make the surgical task easier. The experimental results using two distinct robotic platforms, the Steady-Hand Eye Robot and Micron, in combination with the force-sensing ophthalmic instruments, show significant performance improvement in artificial dry phantoms and ex vivo biological tissues