Objectives
EDEN2020 aims to provide a step change in the treatment of brain disease by delivering an integrated technology platform for minimally invasive neurosurgery. On the basis of unique foundations in surgical robotics, clinical research, and a clear business case, EDEN2020 focuses on the integration of 5 key technologies, namely (1) pre-operative MRI and diffusion-MRI imaging, (2) intra-operative ultrasounds, (3) robotic assisted catheter steering, (4) brain diffusion modelling, and (5) a robotics assisted neurosurgical robotic product (the Neuromate), into a pre-commercial prototype which meets the pressing demand for better and less invasive neurosurgery. The project will have a significant societal, economic and technological impact, helping to consolidate Europe’s position as a leading healthcare provider. The consortium will demonstrate efficacy in the context of convection enhanced drug delivery to treat cancer, with a focussed set of ex vivo and in vivo trials on sheep, while also catering for a range of keyhole neurosurgical interventions (multi targeting, localised ablative therapy, stem cell therapy, etc.).
In doing so, EDEN2020 aims to achieve the following specific objectives:
O1: To engineer a family of steerable catheters for chronic neuro-oncological disease management that can be robotically deployed and kept in situ for extended periods.
Brain disease represents a €750B drain on the economy and patient numbers are growing fast due to an aging population. Better imaging and a broader understanding of brain anatomy and function have helped to highlight the deep interconnectedness between tissue and the Central Nervous System (CNS), and results to date demonstrate significantly improved patient outcomes when this delicate balance is only minimally disrupted. Yet, today’s neurosurgical instruments for diagnostics and therapy are inserted via rigid cannulas, which cannot be deployed along procedure-optimised trajectories which are respectful of tissue structures and exploit tissue anisotropy to maximise therapeutic effectiveness. A new family of steerable, robotic catheters will bridge this gap.
O2: To control robotic, steerable catheters with enhanced autonomy, surgeon cooperation, targeting proficiency and fault tolerance.
The field of surgical robotics, which is a €2.5B business and growing, has experienced a slow uptake over the years, due to the difficulty of striking the right balance between autonomy and surgeon involvement, and the added risks associated to an active system in the operating theatre. Cooperative control for enhanced surgical navigation of miniaturised end effectors represents the gold standard in robotic assisted surgery today, but commercial applications of this type of control are still limited to interactions with rigid structures, such as bone (e.g. Mako and Navio), since sensing and control strategies lack the required level of proficiency to handle deformations. Surgical robots for soft tissue surgery rely on classical tele-manipulation schemes (e.g. daVinci, IMRIS), where the complexities of tissue deformation and tool-tissue dynamics are handled directly by the surgeon. EDEN2020 will provide a new cooperative control scheme for catheter insertion, which will exploit a rich array of intraoperative sensors to manage risk and achieve accuracy.
O3: To sense and perceive intraoperative, continuously deforming, brain anatomy at unmatched accuracy, precision and update rates.
The field of intra-operative imaging has evolved substantially over the past decade, both in terms of image resolution and update rates. This technological progress has already impacted current surgical practice on many fronts, with applications ranging from interventional Magnetic Resonance Imaging (iMRI) for cancer treatment to intra-operative surgical navigation. In neurosurgery, it is now possible to image the extent of tissue deformation outside of an iMRI suite with intra-operative ultrasound, but the live image feed suffers from poor image resolution and imaging artefacts. EDEN2020 will beat the state of the art thanks to novel parallel processing schemes for image capture, exchange and processing, new deformable registration algorithms, and by fusing high fidelity MRI images to a live three-dimensional ultrasound volume, exploiting the geometrical properties of an embedded flexible catheter.
O4: To model, understand and predict drug diffusion properties within brain tissue with unprecedented resolution and comprehensiveness of factors.
The brain is a complex, biphasic, poro-visco-elastic, heterogeneous structure, with anisotropic flows which result from intracranial pressures and inhomogeneity in extra-cellular water content distribution. To date, few of these characteristics are taken into account when defining a surgical plan for localised drug delivery and the results on drug diffusion are poor. EDEN2020 will use state-of-the-art imaging, histological and microstructural analysis to build computational models encompassing both flow and structure, which will provide an unprecedented degree of predictive accuracy.
O5: To study in vivo diagnostic sensing in flexible access surgery.
There exist a number of promising diagnostic sensors at different stages of development, with the potential to enrich minimally invasive neurosurgical procedure execution by providing the surgeon with additional information about e.g. tumour boundaries, current diffusion gradients, pathology and anatomical landmarks. The application of some of these has been explored in the context of needle insertion, but there is no study to date on diagnostic sensors embedded within flexible catheters, a process which would broaden the impact and application of in vivo diagnostics techniques to complex surgical scenarios, ranging from multi-targeting to self-honing systems.
O6: To build a unique database of paired clinical datasets (human and ovine) that includes registered information regarding anatomy, white matter tracts, histology and microstructure.
Due to the complexity and effort required to run a comprehensive in vivo study involving multi-modal information, there are no publicly available databases which provide the range of data needed for neurosurgical drug diffusion modelling. In EDEN2020, such a database will be developed.
O7: To create a pre-commercial technology platform for neurosurgical catheter insertion that exploits the technological and clinical outputs of all other objectives.
The European surgical robotics market is modest, but growing steadily, with a number of key players with a significant presence on the global scene. Amongst these, the project partner RENISHAW is a leader in the field of neurosurgical robotics, with one of two commercial systems in Europe and over 50% of the global market. EDEN2020 will enhance the capabilities of their Neuromate system by extending the range of applicable procedures and augmenting their software ecosystem.
Impact
EDEN2020 has the ambition to set the standard for one step neurosurgical diagnostics and therapy through a minimally invasive approach. It will do so by leveraging existing track records and technologies which are primed for translation and complement these with pioneering research on key scientific questions which have the potential for disruptive clinical impact. The consortium partners have been handpicked due to their unique set of expertise and prior achievements in order to reach the appropriate Technology Readiness Level on all fronts to achieve this impact.
Additionally, EDEN2020 will impact the field of in vivo diagnostics by exploring the deployment of a number of promising imaging modalities via the flexible optical fibres embedded within each catheter segment.
Finally, EDEN2020 will collect, process and make available a unique matched database of human (first) and ovine (second) measurements, which will feature brain topology, connectivity information and diffusion data on a statistically significant sample size. The database will be an unparalleled source of information for scientists, fuelling research into the next generation of diagnostic and therapeutic technologies.