PhD Research Studentships
Versatile Soft Catheter System for Advanced Endoluminal and Tissue Navigation
| Supervisor: | S.M.Hadi SADATI |
| Apply by: | 30 June 2025 |
| Start in: | September (Semester 1) |
Description
Abstract:
This project envisions revolutionizing endoluminal catheterization through the integration of soft robotics, embodied intelligence, and artificial intelligence. This innovation aims to enable minimally invasive interventions deep within the body via automated multi-modal tissue and endoluminal navigation.
Typically, medical access to vital organs is achieved through direct open surgery, laparoscopy, needle-sized incisions, or navigating body lumens. While a close entry site is often preferred to reduce surgery time, remote entry sites, such as the groin for endovascular interventions, can be used due to limited haemorrhage and recurrent stroke risks. These body lumens act as highways to the intervention site, minimizing risks like secondary stroke, excessive arterial bleeding, and damage to proximal nerves.
Safe access and effective navigation within body lumens, such as blood vessels and cerebrospinal fluid pathways, provide comprehensive access to most organs. Imagine a stable, leakage-proof incision to exit the body lumen and continue navigation in surrounding tissue for even deeper access, reaching areas not immediately adjacent to blood vessels.
The vision for this project is to make such multi-modal navigation for deep body access possible. Robotic Mechanical Thrombectomy and Neuroendoluminal catheterization are key clinical applications that underpin this vision.
Introduction:
The most common underlying causes of stroke are cardiac rhythm abnormalities and carotid artery disease, which generate blood clots that can block brain arteries. Recent evidence indicates that using mechanical guidewires and catheters to unblock brain arteries within six hours of a blockage onset significantly benefits patients [1]. Timely treatment is associated with patients living independently without requiring support.
However, few operators have the expertise to perform this procedure—fewer than 80 in the UK as of 2019—and these experts are concentrated in a small number of geographically disproportionate specialist centers (24 in the UK as of 2021 [2]). Consequently, only 29% of eligible patients received the treatment in 2022/23 [3].
Even for expert operators, one to two-thirds of the surgery time is spent navigating blood vessels from the groin to the carotid arteries [4], which increases patient exposure to harmful x-rays and causes staff fatigue. A carotid artery incision, despite the shorter path to the stroke site, is considered unsafe due to significant haemorrhage risks.
Finally, a mechanical thrombectomy procedure requires multiple additional on-site specialized teams, such as the Stroke Unit team, to manage possible post-procedure complications like bleeding and swelling in the brain (hemorrhage transformation and elevated intracranial pressure), recurrent stroke, and more.
This project has been shaped through extensive interviews with clinicians, feedback from Patients and Public Involvement sessions, and demonstrations at scientific events. The overarching aim of this project is to follow an end-user and patient-driven research pathway to develop an AI-assisted, semi-autonomous robotic unblocking procedure. This innovation will enable remote robotic intervention, thereby reducing the critical time gap from diagnosis to procedure.
Research Questions/Objectives:
You will build upon our ongoing research over the past three years, which were supported by multiple short-term fellowships and seed grants.
In this project, you will explore novel ideas for bleeding-free incisions, contact sensing, steerable needle-sized access, and autonomous tissue navigation. These innovations will be based on concepts such as soft eversion growing, push-pull concentric tubes, and artificial and embodied intelligence in soft robotics.
To accelerate the pathway towards clinical impact, we propose initial translational steps, including cadaver studies, close collaboration with the host institutes' technology transfer teams, and training in key translational skills.
Required Skills:
- Strong interest in bioinspired and medical soft robotics
- Extensive experience in mechatronics system development (including CAD, hardware development, control electronics, sensing, and programming)
- Solid analytical and simulation skills
- Keen interest and foundational knowledge in machine learning
Literature Review:
Flexible devices like medical catheters and guidewires, operating under fluoroscopic guidance, have demonstrated clear benefits in endovascular interventions [5]. Device robotization can unlock the potential of medical applications, especially when innovative concepts such as growing robots, smart and absorbable materials [6], [7], [8], structural impedance-based sensing techniques with minimal footprint [9], [10], and data-driven control techniques [8], [11], [12] are considered. These research ideas are in their early exploration and translational stages, with many challenges remaining unsolved. State-of-the-art vascular robotic systems in industry [5] (e.g., Siemens CorPath, Hansen Medical Sensei, Niobe Magnetic System) rely on trained experts and equipped operating rooms and have minimal autonomous functionality. The systems under development in academia (e.g., magnetic guidewires [13], modular units such as Cardiexplorer [14],) usually address niche problems related to catheter navigation (e.g., planning or steering) and lack a holistic view of the overall procedure and the diversity of the medical teams involved.
During the past three years and as a part of a 1-year CME fellowship (2021-22), an NIHR Cardiovascular MedTech Co-operative Pump Prime grant (2021-23), and an MRC Impact Accelerator Fund (2024-25), Dr Sadati and Dr Booth, with the help of a team of experts at KCL, KCH, and University of Erlangen, Germany, have developed an affordable robotic thrombectomy system (see Figure 2). The system features micro-actuation precision via direct drive & lead screw-driven stepper actuation units [12], a bi-directional force sensing sleeve [10] for off-the-shelf neurovascular interventional devices, an Artificial Intelligence (AI)-based controller [8], [11], [15] (in collaboration with PDRA candidate and University of Erlangen, Germany), integration with a haptic interface [16] (in collaboration with co-lead’s team at KCL), real-time simulation and force observation of the catheter interaction with the vascular system in SOFA Framework [17], and a realistic in-vitro testing environment [11] (in collaboration with our partners at KCL). The current system benefits from a control scheme with seamless switching between nine layers of autonomy to share the navigation task between an AI autonomous control agent and a human operator (via a joystick or a haptic interface). The system is designed for semi-autonomous robotic neurovascular intervention where clinicians can collaborate with an AI-based autonomous control framework. The proof-of-concept system has exhibited repeatable performance in our realistic in-vitro phantom experiments with deformable 3D-printed vasculatures based on patient data [unpublished data].
Furthermore, Our preliminary studies have shown that miniature sensor sleeves based on Electrical Impedance measurements and Force Sensitive Resistor (FSR) layers can be used to detect, measure, and identify contacting forces and tissue types in phantom studies with realistic electrophysiological properties similar to human blood [10]. I have also supervised the development and ex-vivo porcine tissue benchmark of a multi-purpose catheter tooltip consisting of a suction cup for gripping the intervention site with an in-built needle for precise therapeutics delivery [18], [19].
Finally, using the above technology, I have conducted a preliminary in-vitro study of thrombectomy device navigation by three neurovascular interventional surgeons with our realistic vascular phantom. Despite the diverse techniques being used, this study highlighted the importance of primitive actions, such as retracting guidewires while advancing the catheter for increased pathway stiffness [unpublished data]. These results will guide you in developing the right level of autonomy desired for clinicians. See Figure 1 for a summary of current developments around this project.
Research group:
You will be a member of the newly established ACEi lab (Artificial, Embodied, and Collective Intelligence lab), directed by Dr. S.M. Hadi Sadati. ACEi is part of the Centre for Advanced Robotics at QMUL (ARQ). The lab focuses on developing autonomous and intelligent systems through computational frameworks, smart embodiment design, and collective behavioral emergence. Bioinspired and biohybrid design concepts are central to the research conducted at ACEi. We prioritize real-world, end-user-driven research with a fast route to impact as the ultimate goal.
Dr. Sadati is recognized in the Soft Robotics community for his work on Reduced-Order Modelling (ROM) of continuum robots through shape interpolation and for implementing these models in the open-source software TMTDyn. His research, published in prestigious journals such as IJRR, SoRo, and IEEE T-RO, is the result of collaborations with an extensive network of international scientists and research labs. Dr. Sadati has managed and played a key role in several significant grants. He received recognition for his contributions to the ART (Autonomous Robotic Thrombectomy in acute stroke) project, which has been showcased at major events. Additionally, Dr. Sadati has fostered a dynamic community around Reduced-Order Modeling and software development for soft robotics research by organizing various workshops and lecturing at various institutions.
Dr. Sadati's expertise in theoretical studies and smart embodiment design for soft robots has laid the foundation for his research on autonomous and intelligent soft robots at ACEi, with applications ranging from medical intervention to industrial inspection.
References:
[1] G. J. Hankey, ‘Stroke’, The Lancet, vol. 389, no. 10069, pp. 641–654, Feb. 2017, doi: 10.1016/S0140-6736(16)30962-X.
[2] L. Zhang et al., ‘Hub-and-spoke model for thrombectomy service in UK NHS practice’, Clin. Med., vol. 21, no. 1, pp. e26–e31, Jan. 2021, doi: 10.7861/clinmed.2020-0579.
[3] ‘SSNAP Annual Report 2023’, 2023. Accessed: Jan. 09, 2024. [Online]. Available: https://www.strokeaudit.org/Documents/National/Clinical/Apr2022Mar2023/Apr2022Mar2023-AnnualReport.aspx
[4] J. Lim, M. Waqas, K. Vakharia, and A. H. Siddiqui, ‘Challenges in Thrombectomy: Impossible Aortic Arches and Tortuous Vessels’, in 12 Strokes: A Case-based Guide to Acute Ischemic Stroke Management, F. K. Hui, A. M. Spiotta, M. J. Alexander, R. A. Hanel, and B. W. Baxter, Eds., Cham: Springer International Publishing, 2021, pp. 311–327. doi: 10.1007/978-3-030-56857-3_23.
[5] W. Crinnion et al., ‘Robotics in neurointerventional surgery: a systematic review of the literature’, J. NeuroInterventional Surg., p. neurintsurg-2021-018096, Nov. 2021, doi: 10.1136/neurintsurg-2021-018096.
[6] P. Berthet-Rayne, S. M. H. Sadati, N. Patel, S. Giannarou, D. R. Leff, and C. Bergeles, ‘MAMMOBOT: A Miniature Steerable Soft Growing Robot for Early Breast Cancer Detection’, IEEE Robot. Autom. Lett., p. 8, 2021, doi: 10.1109/LRA.2021.3068676.
[7] D. Rus and M. T. Tolley, ‘Design, fabrication and control of soft robots’, Nature, vol. 521, no. 7553, pp. 467–475, 2015, doi: 10.1038/nature14543.
[8] H. Robertshaw et al., ‘Artificial intelligence in the autonomous navigation of endovascular interventions: a systematic review’, Front. Hum. Neurosci., vol. 17, 2023, Accessed: Feb. 24, 2024. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fnhum.2023.1239374
[9] J. Avery, M. Runciman, A. Darzi, and G. P. Mylonas, ‘Shape Sensing of Variable Stiffness Soft Robots using Electrical Impedance Tomography’, in 2019 International Conference on Robotics and Automation (ICRA), May 2019, pp. 9066–9072. doi: 10.1109/ICRA.2019.8793862.
[10] J. Sogunro and et al., ‘Multi-axis Force and Tactile Sensor Sleeves for Micro Catheters & Cannulas’, presented at the TAROS, 2023.
[11] L. Karstensen et al., ‘Learning-Based Autonomous Navigation, Benchmark Environments and Simulation Framework for Endovascular Interventions’, Oct. 02, 2024, arXiv: arXiv:2410.01956. doi: 10.48550/arXiv.2410.01956.
[12] K. Iyengar and et al., ‘Sim2Real Transfer of Reinforcement Learning for Concentric Tube Robots”,’ Rev..
[13] Y. Kim et al., ‘Telerobotic neurovascular interventions with magnetic manipulation’, Sci. Robot., vol. 7, no. 65, p. eabg9907, Apr. 2022, doi: 10.1126/scirobotics.abg9907.
[14] Z. Xu et al., ‘CardioXplorer: An Open-Source Modular Teleoperative Robotic Catheter Ablation System’, Robotics, vol. 13, no. 5, Art. no. 5, May 2024, doi: 10.3390/robotics13050080.
[15] L. Karstensen, T. Behr, T. P. Pusch, F. Mathis-Ullrich, and J. Stallkamp, ‘Autonomous guidewire navigation in a two dimensional vascular phantom’, Curr. Dir. Biomed. Eng., vol. 6, no. 1, May 2020, doi: 10.1515/cdbme-2020-0007.
[16] B. Jackson et al., ‘Comparative verification of control methodology for robotic interventional neuroradiology procedures’, Int. J. Comput. Assist. Radiol. Surg., Jul. 2023, doi: 10.1007/s11548-023-02991-2.
[17] K. Zuo, B. Jackson, R. Henry, C. Bergeles, and S. M. H. Sadati, ‘Finite Element Dynamics of a Concentric Tube Robot Motion and Interaction with Environment Using SOFA-framework’, in Hamlyn Symposium on Medical Robotics (HSMR), 2022, p. 2.
[18] K. Joymungul, Z. Mitros, L. da Cruz, C. Bergeles, and S. M. H. Sadati, ‘Gripe-Needle: A Sticky Suction Cup Gripper Equipped Needle for Targeted Therapeutics Delivery’, Front. Robot. AI, vol. 8, p. 752290, Nov. 2021, doi: 10.3389/frobt.2021.752290.
[19] J. Lovatt-Fraser, A. Liu, L. Da Cruz, C. Bergeles, S. M. Hadi Sadati, and NIHR Biomedical Research Centre at Moorfields Eye Hospital UCL, ‘Bioinspired Suction Cup Equipped Needle for Minimally Invasive ONSF’, in Proceedings of the 16th Hamlyn Symposium on Medical Robotics 2024, The Hamlyn Centre Imperial College London, Jun. 2024, pp. 29–30. doi: 10.31256/HSMR2024.15.
Funding
Funded by: SEMSUK students only
Eligibility
- The minimum requirement for this studentship opportunity is a good honours degree (minimum 2(i) honours or equivalent) or MSc/MRes in a relevant discipline.
- If English is not your first language, you will require a valid English certificate equivalent to IELTS 6.5+ overall with a minimum score of minimum score of 6.0 in each of Writing, Listening, Reading and Speaking).
- Candidates are expected to start in September (Semester 1).
Contact
For informal enquiries about this opportunity, please contact S.M.Hadi SADATI.
Apply
Start an application for this studentship and for entry onto the PhD Mechanical Engineering full-time programme (Semester 1 / September start):
Please be sure to quote the reference "SEMS-PHD-660" to associate your application with this studentship opportunity.
| Related website: | https://www.sems.qmul.ac.uk/staff/s.sadati | |
| SEMS Research Centre: | ||
| Keywords: | Artificial Intelligence, Machine Learning, Biomedical Engineering, Mechanical Engineering, Mechatronics, Robotics |