Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques

Investigating Ultrasound-Guided Autonomous Assistance during Robotic Minimally Invasive Surgery

Despite it being over twenty years since the first introduction of robotic surgical systems in common surgical practice, they are still far from widespread across all healthcare systems, surgical disciplines and procedures. At the same time, the systems that are used act as mere tele-manipulators with motion scaling and have yet to make use of the immense potential of their sensory data in providing autonomous assistance during surgery or perform tasks themselves in a semi-autonomous fashion. Equivalently, the potential of using intracorporeal imaging, particularly Ultrasound (US) during surgery for improved tumour localisation remains largely unused. Aside from the cost factors, this also has to do with the necessity of adequate training for scan interpretation and the difficulty of handling an US probe near the surgical sight. Additionally, the potential for automation that is being explored in extracorporeal US using serial manipulators does not yet translate into ultrasound-enabled autonomous assistance in a surgical robotic setting. Motivated by this research gap, this work explores means to enable autonomous intracorporeal ultrasound in a surgical robotic setting. Based around the the da Vinci Research Kit (dVRK), it first develops a surgical robotics platform that allows for precise evaluation of the robot’s performance using Infrared (IR) tracking technology. Based on this initial work, it then explores the possibility to provide autonomous ultrasound guidance during surgery. Therefore, it develops and assesses means to improve kinematic accuracy despite manipulator backlash as well as enabling adequate probe position with respect to the tissue surface and anatomy. Founded on the acquired anatomical information, this thesis explores the integration of a second robotic arm and its usage for autonomous assistance. Starting with an autonomously acquired tumor scan, the setup is extended and methods devised to enable the autonomous marking of margined tumor boundaries on the tissue surface both in a phantom as well as in an ex-vivo experiment on porcine liver. Moving towards increased autonomy, a novel minimally invasive High Intensity Focused Ultrasound (HIFUS) transducer is integrated into the robotic setup including a sensorised, water-filled membrane for sensing interaction forces with the tissue surface. For this purpose an extensive material characterisation is caried out, exploring different surface material pairings. Finally, the proposed system, including trajectory planning and a hybrid-force position control scheme are evaluated in a benchtop ultrasound phantom trial

XXII International Conference on Mechanics in Medicine and Biology - Abstracts Book

This book contain the abstracts presented the XXII ICMMB, held in Bologna in September 2022. The abstracts are divided following the sessions scheduled during the conference

Improving Clinical Diagnosis of Melanocytic Skin Lesions by Raman Spectroscopy

High-quality Raman signals from melanocytic lesions compatible with a possible clinical application have not been demonstrated yet. The objectives of the work described in this thesis were: I: The development of a Raman spectroscopic prototype for objective and fast assessment of melanocytic skin lesions clinically suspicious for melanoma; II: Identification of the main spectroscopic features of melanoma and benign melanocytic lesions suspicious for melanoma; III: Assessment of the feasibility of Raman spectroscopy as an adjunct technique to improve clinical diagnosis of melanocytic skin lesions

Intraoperative Navigation Systems for Image-Guided Surgery

Recent technological advancements in medical imaging equipment have resulted in a dramatic improvement of image accuracy, now capable of providing useful information previously not available to clinicians. In the surgical context, intraoperative imaging provides a crucial value for the success of the operation. Many nontrivial scientific and technical problems need to be addressed in order to efficiently exploit the different information sources nowadays available in advanced operating rooms. In particular, it is necessary to provide: (i) accurate tracking of surgical instruments, (ii) real-time matching of images from different modalities, and (iii) reliable guidance toward the surgical target. Satisfying all of these requisites is needed to realize effective intraoperative navigation systems for image-guided surgery. Various solutions have been proposed and successfully tested in the field of image navigation systems in the last ten years; nevertheless several problems still arise in most of the applications regarding precision, usability and capabilities of the existing systems. Identifying and solving these issues represents an urgent scientific challenge. This thesis investigates the current state of the art in the field of intraoperative navigation systems, focusing in particular on the challenges related to efficient and effective usage of ultrasound imaging during surgery. The main contribution of this thesis to the state of the art are related to: Techniques for automatic motion compensation and therapy monitoring applied to a novel ultrasound-guided surgical robotic platform in the context of abdominal tumor thermoablation. Novel image-fusion based navigation systems for ultrasound-guided neurosurgery in the context of brain tumor resection, highlighting their applicability as off-line surgical training instruments. The proposed systems, which were designed and developed in the framework of two international research projects, have been tested in real or simulated surgical scenarios, showing promising results toward their application in clinical practice

Ultrasonic differentiation of healthy and cancerous neural tissue

It is well documented that intraoperative ultrasound offers improvements to the extent of tumour resected in neurosurgery but currently fails to depict the boundaries of more invasive tumours. Quantitative ultrasound (QUS) is a technique that models ultrasound scattering in tissue mathematically. It can act as a quantitative tool to identify cancerous regions and be used to define features which can train a machine learning (ML) classifier. The use of QUS to differentiate healthy and malignant brain tissue is the objective of this thesis. This work began with a proof of concept study which saw the effective implementation of QUS with a linear array transducer, at conventional frequencies, on phantom materials. The results were then used to train a K-nearest neighbours (KNN) binary classifier to differentiate between two soft tissues. Insight into the most practical parameters for near real time tissue identification was achieved, as well as the opportunity to produce parametric images for various QUS parameters. The effects of freezing and fixation of tissue on QUS results were also considered. The experimental design was developed to obtain a higher lateral spatial resolution before applying it to ex vivo human samples of ten healthy and eight high-grade glioma (HGG) tissues. This was accomplished with both a linear array and a single element scanning system, at centre frequencies of 25 and 74 MHz, respectively. The SoS and attenuation were found to be higher, on average, in the tumour samples than in the healthy tissue. The homodyned K-distribution (HK) parameters alone could distinguish between healthy and HGG tissue to 96% accuracy at 74 MHz, suggesting this is a viable solution for residual HGG detection. To explore the potential of ML with a larger data set, and to extend the study to low grade glioma (LGG) tissue, acoustic impedance maps based on 300 previously recorded microscope histology images of each tissue type were created. The interaction with high frequency (HF) ultrasound was explored using finite element analysis and QUS parameters were obtained. A classification algorithm was able to differentiate healthy and HGG to near perfect accuracy, but a significantly lower accuracy of 79% was found when distinguishing LGG from healthy tissue maps. This research represents a step forward in the otherwise unexplored landscape of HF QUS in brain tissue which necessitates further work to transition from laboratory based experiments to in vivo QUS to aid intraoperative glioma detection

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome