Objective localisation of oral mucosal lesions using optical coherence tomography.

PhDIdentification of the most representative location for biopsy is critical in establishing the definitive diagnosis of oral mucosal lesions. Currently, this process involves visual evaluation of the colour characteristics of tissue aided by topical application of contrast enhancing agents. Although, this approach is widely practiced, it remains limited by its lack of objectivity in identifying and delineating suspicious areas for biopsy. To overcome this drawback there is a need to introduce a technique that would provide macroscopic guidance based on microscopic imaging and analysis. Optical Coherence Tomography is an emerging high resolution biomedical imaging modality that can potentially be used as an in vivo tool for selection of the most appropriate site for biopsy. This thesis investigates the use of OCT for qualitative and quantitative mapping of oral mucosal lesions. Feasibility studies were performed on patient biopsy samples prior to histopathological processing using a commercial OCT microscope. Qualitative imaging results examining a variety of normal, benign, inflammatory and premalignant lesions of the oral mucosa will be presented. Furthermore, the identification and utilisation of a common quantifiable parameter in OCT and histology of images of normal and dysplastic oral epithelium will be explored thus ensuring objective and reproducible mapping of the progression of oral carcinogenesis. Finally, the selection of the most representative biopsy site of oral epithelial dysplasia would be investigated using a novel approach, scattering attenuation microscopy. It is hoped this approach may help convey more clinical meaning than the conventional visualisation of OCT images

Efficient design of precision medical robotics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 106-114).Medical robotics is increasingly demonstrating the potential to improve patient care through more precise interventions. However, taking inspiration from industrial robotics has often resulted in large, sometimes cumbersome designs, which represent high capital and per procedure expenditures, as well as increased procedure times. This thesis proposes and demonstrates an alternative model and method for developing economical, appropriately scaled medical robots that improve care and efficiency, while moderating costs. Key to this approach is a structured design process that actively reduces complexity. A selected medical procedure is decomposed into discrete tasks which are then separated into those that are conducted satisfactorily and those where the clinician encounters limitations, often where robots' strengths would be complimentary. Then by following deterministic principles and with continual user participation, prototyping and testing, a system can be designed that integrates into and assists with current procedures, rather than requiring a completely new protocol. This model is expected to lay the groundwork for increasing the use of hands-on technology in interventional medicine.by Nevan Clancy Hanumara.Ph.D

Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

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

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Medical robots for MRI guided diagnosis and therapy

Magnetic Resonance Imaging (MRI) provides the capability of imaging tissue with fine resolution and superior soft tissue contrast, when compared with conventional ultrasound and CT imaging, which makes it an important tool for clinicians to perform more accurate diagnosis and image guided therapy. Medical robotic devices combining the high resolution anatomical images with real-time navigation, are ideal for precise and repeatable interventions. Despite these advantages, the MR environment imposes constraints on mechatronic devices operating within it. This thesis presents a study on the design and development of robotic systems for particular MR interventions, in which the issue of testing the MR compatibility of mechatronic components, actuation control, kinematics and workspace analysis, and mechanical and electrical design of the robot have been investigated. Two types of robotic systems have therefore been developed and evaluated along the above aspects. (i) A device for MR guided transrectal prostate biopsy: The system was designed from components which are proven to be MR compatible, actuated by pneumatic motors and ultrasonic motors, and tracked by optical position sensors and ducial markers. Clinical trials have been performed with the device on three patients, and the results reported have demonstrated its capability to perform needle positioning under MR guidance, with a procedure time of around 40mins and with no compromised image quality, which achieved our system speci cations. (ii) Limb positioning devices to facilitate the magic angle effect for diagnosis of tendinous injuries: Two systems were designed particularly for lower and upper limb positioning, which are actuated and tracked by the similar methods as the first device. A group of volunteers were recruited to conduct tests to verify the functionality of the systems. The results demonstrate the clear enhancement of the image quality with an increase in signal intensity up to 24 times in the tendon tissue caused by the magic angle effect, showing the feasibility of the proposed devices to be applied in clinical diagnosis

Re-localisation of microscopic lesions in their macroscopic context for surgical instrument guidance

Optical biopsies interrogate microscopic structure in vivo with a 2mm diameter miniprobe placed in contact with the tissue for detection of lesions and assessment of disease progression. After detection, instruments are guided to the lesion location for a new optical interrogation, or for treatment, or for tissue excision during the same or a future examination. As the optical measurement can be considered as a point source of information at the surface of the tissue of interest, accurate guidance can be difficult. A method for re-localisation of the sampling point is, therefore, needed. The method presented in this thesis has been developed for biopsy site re-localisation during a surveillance examination of Barrett’s Oesophagus. The biopsy site, invisible macroscopically during conventional endoscopy, is re-localised in the target endoscopic image using epipolar lines derived from its locations given by the tip of the miniprobe visible in a series of reference endoscopic images. A confidence region can be drawn around the relocalised biopsy site from its uncertainty that is derived analytically. This thesis also presents a method to improve the accuracy of the epipolar lines derived for the biopsy site relocalisation using an electromagnetic tracking system. Simulations and tests on patient data identified the cases when the analytical uncertainty is a good approximation of the confidence region and showed that biopsy sites can be re-localised with accuracies better than 1mm. Studies on phantom and on porcine excised tissue demonstrated that an electromagnetic tracking system contributes to more accurate epipolar lines and re-localised biopsy sites for an endoscope displacement greater than 5mm. The re-localisation method can be applied to images acquired during different endoscopic examinations. It may also be useful for pulmonary applications. Finally, it can be combined with a Magnetic Resonance scanner which can steer cells to the biopsy site for tissue treatment