Available master thesis

 

New Thesis: THESIS-THEMES.pdf

Thesis Haptic guidance in robotized craniotomy
Supervisors Elena De Momi, Juan Sandoval
Collaborations Université de Poitiers

Description During craniotomy procedures, hand‐held drilling is commonly used to remove a section of the skull. Nevertheless, drilling vibration and hand shaking may lead to potential deviation from targeted drilling region, producing hemorrhage and other neurological problems. To mitigate these risks, a robotized craniotomy platform is proposed.

Goal of Thesis: develop a haptic guidance strategy to constraint the drilling tool movements into a desired region. Forbiddenregion virtual fixtures should be defined based on a 3D reconstruction of the patient’s skull.

Equipment: Franka robot, Falcon interface, 3D scan, Motion Capture system, etc.

Software: Robot Operating System (ROS), Matlab Simulink, Vrep.

Location: PPRIME Institute, University of Poitiers, Poitiers, France.

Thesis Laparoscope-holder forminimally invasive surgery
Supervisors Elena De Momi, Juan Sandoval
Collaborations Université de Poitiers

Description In minimally invasive surgical procedures, surgeon usually uses both hands to manipulate the surgical instruments during the task execution. In order to obtain a visual feedback of the surgical gestures, a laparoscope is inserted into the patient’s body, held by a medical staff assistant, who moves it accordingly to the surgeon orders. However, this method doesn’t filter assistant’s hand tremors, it generates a lack of precision, time delays in the surgical task execution as well as an increase of the stress suffered by the surgeon. To cope with, a collaborative laparoscope-holder robotic system is proposed.

Goal of Thesis: control the robot movements based on the detection of the surgical instruments in endoscopic images.

Equipment: Franka robot, endoscopy system.

Software: Robot Operating System (ROS).

Location: PPRIME Institute, University of Poitiers, Poitiers, France.

Thesis Multi-Robot Control for Logistics
Supervisors Elena De Momi, Arash Ajoudani
Collaborations Amazon

Description Possible topics:
– Multi-robot navigation control
– Role allocation for multi-agent systems
– Interaction control for robots
Thesis Augmented Reality Interfaces for Human-Robot Collaboration
Supervisors Elena De Momi, Arash Ajoudani
Collaborations Microsoft

Description Possible topics:
– AR feedback for improving task performance
– AR interface for human-robot communication
– AR interface for role allocation (with machine learning)
Thesis Ergonomic Human-Robot Interaction
Supervisors Elena De Momi, Arash Ajoudani
Collaborations Istituto Italiano di Tecnologia

Description Possible topics:
– Real-time monitoring of human kinodynamic states
– Real-time human fatigue models
– Development of feedback interfaces for ergonomic assessment
– Robot control to improve human ergonomics
Thesis Vessels reconstruction
Supervisors Elena De Momi, Pierangela Bruno
Collaborations Università della Calabria

Description Problem:
The vascular imaging techniques can reduce the quality of images (i.e. including various lighting effects, motion blur, noise) and hide important information.

Aim of the study:
Vasculature is known to be of key biological significance, especially in the study of therapeutic decisions and prognostic estimation. Unfortunately, the acquisition methods can not able to capture small information (i.e. microscopic vessel) that could be helpful in disease prediction.

The goal is to define a neural network able to reconstruct vessels that are hidden in the medical images.

Thesis DaVinci platform– Autonomous tissue retraction and ultrasound
Supervisors Elena De Momi, Pietro Valdastri
Collaborations University of Leeds

Description Available projects:
– Deep learning for surgical images segmentation
– Autonomous gesture execution
– 3D reconstruction of anatomical features from ultrasounds
– Selective soft tissues cutting with ultrasonics

Valuable knowledge acquired during work:
– Autonomous tasks in medical robotics
– Robotic laparoscopy manipulation
– Deep learning for image analysis
– Ultrasound imaging and ablation

Thesis Automating the fabrictaion of soft magnetically controlled robots
Supervisors Elena De Momi, Pietro Valdastri
Collaborations University of Leeds

Description Problem:
Fabrication of magnetically controlled soft materials has the potential to produce safe externally controllable robots for many applications, particularly in the medical field. However, the time taken to manufacture these intricate structures and their low repeatability limit their clinical translation. Throgh tranisitioning toward automated fabrication techniques, existing and new soft robot designs may be realised.Aim of the study
To develop an automated fabrication tool the production of 
magnetically actuated soft robots. Specific objects are:
– to critically evaluate existing fabrication methodologies,
– to propose design solutions based on application requirements,
– to build prototype fabrication system/s,
– to test and evaluate output designs and system limitations.
Thesis Magnetic colonoscopy: Towards first in human trials
Supervisors Elena De Momi, Pietro Valdastri
Collaborations University of Leeds

Description Available projects:
– Medical grade electronics design
– Development of testing rig for certification
– Development of medical grade UI
– Disturbance rejection under contact force constraints
– Advanced navigation with vision integrationValuable knowledge acquired during work:
– Medical grade electronics
– Medical product development (ISO 60601, 62304, 62366)
– Advanced magnetic manipulation
Thesis XoSoft actuation characterization
Supervisors Elena De Momi, Christian Di Natali, Jesús Ortiz
Collaborations XoLab @ IIT-ADVR

Description The student will join the multi-disciplinary research group carried on at the XoLab, ADVR, Istituto Italiano di Tecnologia (IIT), Genova, Italy. At the XoLab, we are working on developing the next generation of soft exoskeletons. The student will work on a robotic workbench representing a human knee and the soft exoskeleton actuation that has been created during the European Project XoSoft . Research topic will include the study of the platform system software management, developing position and/or torque control of the robotic knee to simulate real behavior of human limb. The student may work on the development of sensorization solutions for the exoskeleton actuators. The student will work on the low-level control of pneumatic system to improve the performances of the soft actuators. This thesis will allow the student focusing on ongoing important aspects of the actual worldwide research as human-robot interaction, gait analysis, real-time exoskeleton control, inertial and proprioceptive sensing. Possible interactions with XoSoft partners around Europe will offer an outstanding research experience.
Thesis Fractional Impedance Control for Force-Tracking Applications
Supervisors Elena De Momi, Loris Roveda
Collaborations SUPSI-IDSIA

Description Impedance control is commonly used in order to interact with a target environment. Outer force controller are also developed in order to track a target interaction force. Fractional impedance control can improve the performance of the controlled manipulator, due to the properties of fractional controllers. Theoretical and experimental results are expected. Expected applications belong to medical or industrial scenarios.
Thesis Passive Velocity Field Force Control for Human-Robot Collaboration
Supervisors Elena De Momi, Loris Roveda
Collaborations SUPSI-IDSIA

Description The thesis objective is to design an adaptive force controller for human-robot collaboration based on the passive velocity field concept. The interaction force between the human and the robot will deform the passive velocity field in order to assist the human during the task while guaranteeing the passivity of the system (i.e., the stability of the controller). Theoretical and experimental results are expected.
Thesis Method and tool for planning and executing minimally invasive procedure for in-vivo trajectory estimation
Supervisors Elena De Momi, Alice Segato, Alberto Favaro
Collaborations UNIMI

Description Objective:
This thesis aims at providing a clinical tool for the preoperative path planning of the in-vivo ovine tests in the context of a collaboration with the veterinary team of Università Statale di Milano and the clinicians of San Raffaele Hospital. The project is conducted in the framework of the European Project EDEN2020.Project phases:
• Development of an automatic segmentation tool for the 3D reconstruction of ovine brain structures.
• Development of a 3D Slicer module for the identification of optimal surgical trajectory.
Thesis Project 2
Supervisors Elena De Momi, Antonio Elia Forte
Collaborations Imperial College London, Harvard Medical School
Description To investigate how micro
and macro geometric structure can be tailored to push
the mechanical behaviour of materials to new limits.
Understanding the role of geometric periodic patterns
(achievable during the manufacturing stage) is key in order
to master the tuning of the mechanical properties of highly
stretchable compounds, non-linear and auxetic
behaviours, foldable and super compactable origami
structures that can be used to mimic the complex
mechanical behaviour of organic tissues. This will contribute to unravel the mechanisms behind e.g. the extreme expansion capabilities of lung tissue, or the laws that govern the solid-liquid interaction in organic porous structures. Therefore, the aim is to improve the mechanical properties of surrogate materials (i.e. hydrogels) widening the tuning capabilities of the compounds not only by chemical manipulation, but also by means of creating 3D periodic structures.
Tasks:
1) Preliminary 2D and 3D simulations of the mechanical capabilities (by means of commercial FEA software9). Study of stress mappings and structural failure under deformation. Detection and intervention on critical areas of the designs.
2) Study of manufacturing solutions for 3D patterned structures at macro and micro scale, including additive manufacturing, stereolithography, and electrospinning.
3) Manufacturing of macro and micro 3D structures using the designed materials and the most suitable techniques explored.
4) Correlation of (i) geometric patterns with mechanical properties of the materials; (ii) 3D scaling of structures with changes in biphasic 
response; (iii) number of cells repetitions with the arising of non-linear behaviours.
Thesis Project 1
Supervisors Elena De Momi, Antonio Elia Forte
Collaborations Imperial College London, Harvard Medical School
Description The aim of the project is to develop a theoretical approach aimed at investigating changes in the mechanical properties of hydrogels (4 natural and synthetic polymer families: Agar, Polyvinyl alcohol, Gellan gum, Gelatin gels) due to chemical variations in polymer ratios, compositions and network structures. Such investigation should account for the presence of multicomponent mixtures combined through coupled, phase separated or interpenetrating networks. This leads to the creation of different viscoelastic material models and the study of the material mechanical behaviour at different scales. The final aim of the project is the design of synthetic materials that can mechano-mimic human soft tissues such as skin, liver and lung. Due to the time-dependent characteristics typical of many soft tissues, an analysis aiming at characterising a large frequency range is needed. The current projects aims at providing a complete mapping of the mechanical properties of multicomponent natural-synthetic mixtures as a function of their chemical composition and structures. Finally, mathematical formulations will be developed in order to link chemical characteristics to mechanical properties, i.e. (i) concentrations ratios to toughness and viscoelasticity and, (ii) the effect of different network structures and crosslink agents on the non-linear rate-dependent response of the compounds.
Tasks:
1) To analyse the mechanical properties of human skin, liver and lung from the literature: study of the mechanical characteristics of the tissues: stiffness, porosity, viscoelasticity, range of deformations they are usually subjected to.
2) Study of single and multicomponent soft materials and their physical and chemical crosslinking capabilities.
3) Study of interpenetrating, phase separated and coupled polymeric networks.
4) Chemical and molecular analysis of the compounds (SEM, DSC, RAMAN spectroscopy and FTIR).
5) Mechanical testing (compression, tension, hysteresis and rate dependent properties).
6) Mathematical approach to link chemical changes and variations in the mechanical properties of the materials. Use of Darcy’s law and Terzaghi consolidation theory for biphasic properties theorizations; Maxwell, Generalized Maxwell, Kevin-Voigt and Zener 
viscoelastic models for matrix viscoelasticity; Ogden, Neo-Hookean and Polynomial models for hyperelasticity.
Thesis Brain vasculature segmentation via deep learning network
Supervisors Elena De Momi, Sara El Hadji
Collaborations Centro Munari Chirurgia dell’Epilessia e del Parkinson
Description Increasingly, computer-assisted surgery is being exploited in minimal invasive surgery (MIS) to provide three dimensional visualization of vascular structures for assisting clinicians in preoperative planning, intra-operative guidance, and post-operative decision-making. Stere-Electro-encephalography (SEEG) involves the implantation of multiple intracranial electrodes to map spatio-temporal dynamics of epileptic seizures. In SEEG procedures, brain vessels are among the most critical landmarks that need to be accurately localized for reducing surgical risks, such as intracranial hemorrhage. Thus, SEEG requires a careful preoperative planning to identify safe electrode trajectories that preserve vascular structures. The goal of the thesis is the development of a neural network approach for brain vascular segmentation.
Thesis Brain arteries and veins separation via spatio-temporal convolutional neural network
Supervisors Elena De Momi, Sara El Hadji
Collaborations Medtronic
Description In digital subtraction angiography (DSA), the contrast medium (CM) dynamics through the various districts is commonly overlooked but it can be informative beyond the pure angiographic morphology. Interest can be foreseen in various pathologies involving hemodynamic alterations such as arteriovenous malformations, aneurysms, stroke, tumor angiogenesis. Moreover, CM dynamics provides keys to distinguish arteries from veins (A/V classification), with a remarkable impact in surgical planning and navigation. This thesis work aims at investigating a spatio-temporal neural network to retrieve contrast dynamics information and encoding this information in terms of arteries and veins classification.
Thesis Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network
Supervisors Elena De Momi, Sara El Hadji
Collaborations Medtronic
Description Several deep learning algorithms were proposed to improve image quality using different network models. In particular, deep convolutional neural networks (DCNN) demonstrated to be powerful techniques for feature extraction and were applied to image denoising, deblurring and super-resolution. In this work, we propose a DCNN method for producing high-quality CBCT images. In particular head couch artifact is addressed. The proposed thesis aims at extending CNN architecture to artifact reduction and sinogram denoising.
Thesis Early-stage laryngeal-cancer segmentation through deep-learning strategies
Supervisors Elena De Momi, Sara Moccia
Collaborations Università Politecnica delle Marche
Description Early-stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Early-stage SCC is associated with the presence of intrapapillary capillary loops (IPCL) and hypertrophic vessels (Hbv), as well as with the presence of pre-cancerous tissue conditions, such as leukoplakia (Le) (Fig. 1).
Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in real-time computer-assisted diagnosis.The goal of the thesis will be to investigate deep-learning strategies to perform fast and accurate SCC segmentation from endoscopic video-frames in narrow-band imaging (NBI) as to (i) support diagnosis and (ii) identifying tumor margin for safe laryngeal surgery.
Thesis Adaptive training (with ML) for robotic surgery
Supervisors Elena De Momi
Collaborations Niguarda, Medtronic
Description Aim:

To develop an adaptive training paradigm for surgical task, tailoring the exercise to elicit needed motor control capabilities to the user.

Machine learning techniques will allow the design of a personalized training schema.

Thesis Vessel segmentation in X-Ray Angiograms using Convolutional Neural Network
Supervisors Elena De Momi, Francesco Calimeri, Pierangela Bruno
Collaborations Università della Calabria
Description The Assessment of vascular complexity in the lower limbs provides important information about peripheral artery diseases, with a relevant impact on both therapeutic decisions and on prognostic estimation. An automatic image analysis could offer a fast and more reliable technique to support physicians with the clinical management of these patients.

This thesis will be focus on:
-perform a “Top-bottom Hat transformation” technique for vessel enhancement
-define convolutional neural network(CNN) for detecting vessel regions in angiography images

Thesis Multiple sclerosis segmentation using Autoencoder neural networks
Supervisors Elena De Momi, Francesco Calimeri
Collaborations Università della Calabria
Description Automatic classification of biomedical imaging became an important field of research within the scientific community, in the latest years. Indeed, advances in image acquisition and processing techniques, along with the success of novel deep learning methods and architectures, represented a considerable support in providing better biomarkers for the characterization of several diseases, and brain diseases in particular. In this work we propose a novel neural network approach that is applied to graphs generated from MRI data in order to make predictions about the clinical status of a patient. Results show high performances in classification tasks and open interesting perspectives in the field.
Thesis Declarative reasoning over ontologies and big data
Supervisors Elena De Momi, Francesco Calimeri, Simona Perri
Collaborations Università della Calabria
Description The talk presents DLV, an advanced AI system from the area of Answer Set Programming (ASP), showing its high potential for reasoning over ontologies. Ontological reasoning services represent fundamental features in the development of the Semantic Web. Among them, scientists are focusing their attention on the so-called ontology-based query answering (OBQA) task where a (conjunctive) query has to be evaluated over a logical theory (a.k.a. Knowledge Base, or simply KB) consisting of an extensional database (a.k.a. ABox) paired with an ontology (a.k.a. TBox). From a theoretical viewpoint, much has been done. Indeed, Description logics and Datalog /- have been recognised as the two main families of formal ontology specification languages to specify KBs, while OWL has been identified as the official W3C standard language to physically represent and share them; moreover sophisticated algorithms and techniques have been proposed. Conversely, from a practical point of view, only a few systems for solving complex ontological reasoning services such as OBQA have been developed, and no official standard has been identified yet. The talk will illustrate the applicability of the well-known ASP system DLV for powerful ontology-based reasoning tasks including OBQA.
Thesis Surgical Scene Semantic Segmentation
Supervisors Elena De Momi, Veronica Penza
Collaborations Istituto Italiano di Tecnologia
Description Augmented Reality (AR) tools in combination with robotic systems, can greatly help in enhancing the surgeons’ capabilities during Minimally Invasive Surgery (MIS), providing direct patient and process-specific support to surgeons with different degrees of experience.
The awareness of the surgical scene plays a fundamental role in an AR system, allowing to discriminate the position of different tissues and instruments at run-time.This project will be focused on developing a real time and accurate semantic segmentation of the surgical scene, exploiting deep learning techniques.
Thesis Predict segmentation accuracy only by looking to anatomical images
Supervisors Elena De Momi
Collaborations Università degli Studi “Magna Graecia” di Catanzaro
Thesis Development of a deformable anatomical 
model for simulation of laparoscopic surgery.
Supervisors Elena De Momi, Eleonora Tagliabue
Collaborations Università di Verona
Description The aim of this project is the development of a framework where the user can interact with a deformable model of the anatomy through virtual da Vinci instruments, for simulation purposes.
Either the SOFA framework1 or NVIDIA FleX will be used to obtain the deformations.
Thesis Development of a novel angular sensor for medical tracking systems.
Supervisors Elena De Momi
Collaborations University of Basel
Description The goal of this project is to design, realize and characterize an innovative angular sensor.

Description:

The flagship project MIRACLE, short for Minimally Invasive Robot-Assisted Computer-guided LaserosteotomE, aims to develop a robotic endoscope to perform contact-free bone surgery with laser light.

Laserosteotomy offers several advantages over conventional mechanical bone surgery such as precise and small cuts based on pre-operative planning, functional cut geometry (so-called smart cuts), accelerated healing, and less trauma.

Thesis Develop a tele-operation system with haptic feedback integrating a bespoke upper body exoskeleton and a COTS cobot from the ground up.
Supervisors Elena De Momi
Collaborations Space Applications Services NV/SA
Description Using ROS and the existing control software for the two systems the candidate will develop the low level controllers to achieve low-latency control of the arm dynamics. The system will provide life-like haptic feedback to enable minimally-trained operators to perform complex tasks precisely and accurately.

The successful candidates will join the Robotics, Mechanisms & Structures Team in the Brussels office.

Qualifications:
-Good programming skills level (C++ or Python)
-Hands-on experience in robotics
-Experience with ROS & Movelt will be considered as an asset
-Excellent written and spoken English.

Tasks and Responsibilities:
-Real-time control
-High fidelity haptic feedback

What Do We Offer?
-A 6 months’ internship
-An experience in the space industry
-A monthly Lump Sum Allowance

Thesis Dataset Retrieval and Validation on da Vinci Research Kit
Supervisors Elena De Momi
Collaborations University of Leeds
Description Objectives:
-Retrieve data from surgeons trial on human cadavers for automation of surgical tasks
-Building the first dataset for analysis and assessment of surgical gestures on human trialsRequirements:
-Programming skills (C++, Python)
-Basic knowledge in Machine Learning (Neural Networks, Hidden Markov Models)Personal achievements:
-Work in collaboration with clinicians
-Learn how to build a public dataset
Thesis Magnetic tethered capsule for painless autonomous colonoscopy
Supervisors Elena De Momi
Collaborations University of Leeds
Description Main goal of the project: semi autonomous colonoscopy.

Available projects for master students:
– MPC path planning and control of the capsule motion
– Development of a high-level strategy for autonomous locomotion
– Autonomous biopsy
– Nonlinear dynamic control – capsule levitation
– Robot/clinician collaboration: integration of external vision system and user interface

Thesis Actuation/stiffening of soft robots with low melting point material
Supervisors Elena De Momi, Helge Wurdemann
Collaborations University College London
Description The aim of the project is to develop an actuation system that allows to use low melting point material to actuate soft robotic manipulators.

This project will consider wax and alloy material that will liquefy at about 50oC. Integrated nichrome wires will allow to control the temperature inside the robotic actuation system and the actuation chambers.

Thesis Understanding vessel properties from p-v curves
Supervisors Elena De Momi, Helge Wurdemann
Collaborations University College London
Description The aim of the project is to develop a valvuloplasty robotic balloon catheter, capable of determining the size and the mechanical properties of the aortic annulus from intra-balloon volume and pressure measurements.
Thesis Hydraulic haptic feedback for prosthetic upper limb
Supervisors Elena De Momi, Helge Wurdemann
Collaborations University College London
Description Without the sense of touch, amputees with prosthetic hands can have di culty holding and manipulating objects, especially when a task requires some degree of skill and tactile feedback to perform. To equip prosthetic hand users with touch sensing and tactile feedback, researchers have been experimenting with various types of force and/or tactile sensors together with various methods for delivering the tactile information to the brain. Although some success has been achieved recently with force sensors and implanted electrodes, these systems are expensive, surgically invasive and can represent an infection risk where cables emerge through the skin. Also, non-invasive tactile feedback methods involving temperature, vibrations or electro-mechanical force feedbacks, can be somewhat awkward and ine ective due to being cumbersome or unable to deliver appropriate sensations. To address some of these issues we have been developing an electro- tactile feedback system for prosthetic hands. Our proposed system is comprised of force sensors that can be placed almost anywhere on a prosthetic hand, and TENS electrodes that can be placed on the wearer’s arm. Our system is inexpensive, multi-channel and easily ed to existing prosthetic hands. Experimental results are provided that show how this form of tactile feedback can enable a user to feel various objects touched or gripped with a robotic humanoid hand. Keywords: prosthetic hand, electro-tactile feedback.
Thesis Implementation and control of an elbow joint of a surgical instrument in a Virtual Surgical Environment
Supervisors Elena De Momi, Antonia Tzemanaki, Sanja Dogramadzi
Collaborations University of Bristol
Description Investigate requirements for added dexterity in minimally invasive surgical (MIS) instruments. This can be achieved by creating a virtual surgical environment (e.g. Unity or CHAI3D) and a surgical instrument with an added joint (elbow). The required number of degrees of freedom of the joint can be determined in the project with testing. The position and orientation of the virtual instrument will be controlled by a user operating a robotic haptic arm (e.g. Virtuose 6d desktop, HAPTION), which will help test if the additional joint is beneficial in avoiding collisions with (virtual) abdominal walls.
Thesis Are 3 better than 2? Mapping between human
and robot hands for surgical tele-operation
Supervisors Elena De Momi, Antonia Tzemanaki, Sanja Dogramadzi
Collaborations University of Bristol
Description Investigate, develop and implement one or more algorithms that map a human hand and its fingers to those of a robotic end-effector. Examples of end-effectors can vary between robot grippers, hand or even surgical instruments. The ultimate goal would be to use the developed algorithms in a series of tests that compare two-finger and three-finger grasping and manipulation.
Thesis Investigation of hand and finger motions using a
robotic hand exoskeleton interface
Supervisors Elena De Momi, Antonia Tzemanaki, Sanja Dogramadzi
Collaborations University of Bristol
Description Develop algorithms for hand motion tracking and recording and compare a custom-made robotic hand exoskeleton with other methods of tracking (e.g. Leap motion). This can be used in investigating hand motions of users when attempting various grasping tasks, varying from every-day objects to surgical suturing.
Thesis Virtual fixtures for haptic guidance in robotized teleoperated craniotomy
Supervisors Elena De Momi
Collaborations University of Poitiers, PPRIME Institute (Poitiers, France)
Description During craniotomy procedures, hand‐held drilling is commonly used to remove a section of the skull. Nevertheless, drilling torsion and hand shaking may lead to potential deviation from targeted drilling region, producing hemorrhage and other neurological problems. To mitigate these risks, a teleoperated craniotomy platform is proposed.

The aim of this MSc thesis is to develop haptic guidance strategies to constraint the drilling tool movements into a desired region. Forbidden-region virtual fixtures should be defined based on a 3D reconstruction of the patient’s skull. Moreover, a control strategy is expected to be implemented to prevent meninges damage (three membranes enveloping the brain), through the measures provided by the force sensor placed at the drilling tool.

The proposed thesis can be performed half-time in Milan and half-time in Poitiers, where it is expected to perform experimental tests.

Available equipments: Franka robot, Falcon interface, 3D scan, Motion Capture system, etc.

Proposed software: Robot Operating System (ROS), Matlab Simulink, Vrep

Thesis 3D path-following control of steerable needles
Supervisors Elena De Momi, Cameron Riviere
Collaborations The Robotics Institute Carnegie Mellon University
Description BACKGROUND: Intracerebral drug delivery using surgically placed microcatheters is a growing area of interest for potential treatment of a wide variety of neurological diseases, including tumors, neurodegenerative disorders, trauma, epilepsy, and stroke. Current catheter placement techniques are limited to straight trajectories. The de- velopment of an inexpensive system for flexible percutaneous intracranial navigation may be of significant clinical benefit.

OBJECTIVE: Utilizing duty-cycled spinning of a flexible bevel-tipped needle, the authors devised and tested a means of achieving nonlinear trajectories for the navigation of catheters in the brain, which may be applicable to a wide variety of neurological diseases.

METHODS: Exploiting the bending tendency of bevel-tipped needles due to their asymmetry, the authors devised and tested a means of generating curvilinear trajectories by spinning a needle with a variable duty cycle (ie, in on-off fashion). The technique can be performed using image guidance, and trajectories can be adjusted intraoperatively via joystick. Fifty-eight navigation trials were performed during cadaver testing to demonstrate the efficacy of the needle-steering system and to test its precision.

Thesis can be completed ½ in Milan and ½ in Pittsburgh

Thesis Virtual fixture for safer microneurosurgery
Supervisors Elena De Momi, Cameron Riviere
Collaborations The Robotics Institute Carnegie Mellon University
Description Microsurgical procedures, such as petroclival meningioma resection, require careful surgical actions in order to remove tumor tissue, while avoiding brain and vessel damaging. Such procedures are currently performed under microscope magnification. Robotic tools are emerging in order to filter surgeons’ unintended movements and prevent tools from entering forbidden regions such as vascular structures. The present work investigates the use of a handheld robotic tool (Micron) to automate vessel avoidance in microsurgery. In particular, we focused on vessel segmentation, implementing a deep-learning-based segmentation strategy in microscopy images, and its integration with a feature-based passive 3D reconstruction algorithm to obtain accurate and robust vessel position. We then implemented a virtual-fixture-based strategy to control the handheld robotic tool and perform vessel avoidance. Clay vascular phantoms, lying on a background obtained from microscopy images recorded during petroclival meningioma surgery, were used for testing the segmentation and control algorithms. When testing the segmentation algorithm on 100 different phantom images, a median Dice similarity coefficient equal to 0.96 was achieved. A set of 25 Micron trials of 80 s in duration, each involving the interaction of Micron with a different vascular phantom, were recorded, with a safety distance equal to 2 mm, which was comparable to the median vessel diameter. Micron’s tip entered the forbidden region 24% of the time when the control algorithm was active. However, the median penetration depth was 16.9 μm, which was two orders of magnitude lower than median vessel diameter. Results suggest the system can assist surgeons in performing safe vessel avoidance during neurosurgical procedures.

Thesis can be completed ½ in Milan and ½ in Pittsburgh

Thesis In-Loop Electromagnetic Tracker
to obviate tracking sightlines for handheld surgical robotics
Supervisors Elena De Momi, Cameron Riviere
Collaborations The Robotics Institute Carnegie Mellon University
Description Frequency domain multiplexing (FDM) is a useful for making multiple measurements simultaneously, such as in optical and electromagnetic position trackers. Much interference is periodic (e.g., AC power harmonics), and is rejected well by FDM, but impulse disturbances are also common. Impulses corrupt the entire spectrum for a short period, and are better rejected in the time domain. Nonlinear blanking is a simple way to suppress impulses, but cannot be easily realized when the required dynamic range is large, and problematic noise may be far smaller than the signal. The described multi-rate Kalman filter upsamples the prediction to the input rate so that impulse departures from the predicted signal are easily detected and blanked out. Also, noise levels in unused adjacent channels are used to estimate measurement noise so that the Kalman filter adapts more slowly when noise is high, keeping output noise roughly constant even in the presence of longer noise bursts.

Thesis can be completed ½ in Milan and ½ in Pittsburgh

Thesis Development of a novel angular sensor for medical tracking systems
Supervisors Elena De Momi, Lorenzo Lafolla, Philippe Cattin
Collaborations University of Basel
Description The flagship project MIRACLE, short for Minimally Invasive Robot-Assisted Computer-guided LaserosteotomE, aims to develop a robotic endoscope to perform contact-free bone surgery with laser light.

Laserosteotomy offers several advantages over conventional mechanical bone surgery such as precise and small cuts based on pre-operative planning, functional cut geometry (so-called smart cuts), accelerated healing, and less trauma.

Accurate and real-time tracking of the MIRACLE robotic endoscope is essential for its navigation and the realization of precise cuts. For minimally invasive interventions, state-of-the-art optical tracking systems are not applicable due to the required line-of-sight. Therefore, a major part of the research of MIRACLE is devoted to the development of innovative tracking concepts to monitor the position and the orientation of the laser tip and the entire shape of the endoscope inside the patient.

One of the proposed solutions is based on the use of a novel angular sensor called ASTRAS (Angular Sensor for TRAcking Systems) developed in the Planning and Navigation group of the MIRACLE project. This tracking concept works with articulated robots in which several ASTRAS measure the angles at each joint.

ASTRAS is an opto-mechanical system which includes an image sensor, a mirror, an LED.

Another important development, which is the topic of this master thesis, is to extend the range of measurement to 360° (the current version is limited to about 25°). This new version of ASTRAS is called ASTRAS360 and it is based on the use of several mirrors simultaneously.

The candidate will design, realize and characterize the first prototype of ASTRAS360.
She/He will improve her/his skills in designing and implementing small opto-mechanical systems, and in characterizing high precision measurement systems.

We offer
• an exciting international and collaborative research environment
• supervisors that will take time to support and teach you
• the opportunity to work on a multi-disciplinary project
• the opportunity to learn using tools such 3d-printer, machine tools, cnc, embedded electronic devices, etc.

Requirements
• Programming skills with MATLAB is desirable
• Designing skills with Solidworks is desirable
• Ability in assembling optomechanical systems is desirable
• Applicants are expected to have excellent language skills in English

Calendar
The starting date is negotiable.

Location
University of Basel
Department of Biomedical Engineering
Gewerbestrasse 14
CH-4123 Allschwil, Switzerland

Contact Details
For more information, please do not hesitate to contact Lorenzo Iafolla (lorenzo.iafolla@unibas.ch) or Prof. Philippe C. Cattin (philippe.cattin@unibas.ch).

Thesis A handheld robotic tool (Micron) to automate vessel avoidance in microsurgery
Supervisors Elena De Momi, Simone Foti, Cameron Riviere, Sara Moccia
Collaborations The Robotics Institute Carnegie Mellon University

[Micron’s trajectory evaluation. Reconstructed point cloud (represented in blue) with respect to the trajectories with and without control (green and orange respectively).]

Description Microsurgical procedures, such as petroclivial meningioma resection, require careful surgical actions in order to remove tumor tissue avoiding brain and vessel damaging. Previous work investigated the use of a handheld robotic tool (Micron) to automate vessel avoidance in microsurgery. In particular a deep-learning based segmentation algorithm localized vessels within planar images and a 3D reconstruction algorithm computed their point clouds. A simple control strategy controlled Micron to avoid the vessels point clouds.

Since the computational cost was still not negligible, the main goal of this thesis is to further reduce it to obtain real time performances. Reducing the time consumption will then allow in vivo analysis.

Thesis Manipulability Optimization of a serial redundant robot for tele-operated MIS
Supervisors Elena De Momi, Hang Su
Description The aim of this topic is to improve the manipulability indices of the serial robot during the tele-operated surgery. Based on the achieved teleported MIS demo, the redundancy of the robot arm will be utilized to improve the manipulability of the surgical tip. The experimental evaluation will be conducted on a lab setup environment using KUKA LWR4+ robot and Sigma 7 master device.

Reference:
Jin L, Li S, La H M, et al. Manipulability optimization of redundant manipulators using dynamic neural networks[J]. IEEE Transactions on Industrial Electronics, 2017, 64(6): 4710-4720.

 

Thesis Observer based adaptive control a serial redundant robot for tele-operated MIS
Supervisors Elena De Momi, Hang Su
Description Instead of using fuzzy adaptive control, neural network control will be introduced to work out the uncertain disturbance in the robot workspace using back-stepping design. The aim is to enhance the tool tip accuracy and constrain the RCM constraint error under uncertain physical disturbance in tele-operated surgery. The experimental evaluation will be conducted on a lab setup environment using KUKA LWR4+ robot and Sigma 7 master device.

Reference:
Zhang, Tao, Shuzhi Sam Ge, and Chang Chieh Hang. “Adaptive neural network control for strict-feedback nonlinear systems using backstepping design.” Automatica 36.12 (2000): 1835-1846.

Thesis Observer based adaptive control a serial redundant robot for tele-operated MIS
Supervisors Elena De Momi, Hang Su
Description Instead of using torque sensor, nonlinear observer will be utilized to estimate the extern force for the current Teleported Minimally Invasive surgery. In this way, the controllers do not need torque sensors and can be used for the same tele-operated MIS. The experimental evaluation will be conducted on a lab setup environment shown in the following picture.

Reference:
Sadeghian, H., Villani, L., Keshmiri, M., & Siciliano, B. (2014). Task-space control of robot manipulators with null-space compliance. IEEE Transactions on Robotics, 30(2), 493-506.

Thesis Enhancing human robot collaboration: a biomimetic controller for cooperative tasks
Supervisors Elena De Momi, Jacopo Buzzi, Prof. Arash Ajoudani
 Collaborations   Italian Institute of Technology
Description Over the last decades, robots applications have started to grow, permeating multiple aspects of everyday life, from industrial manufacturing, to healthcare and automotive. More and more often, humans face the necessity to interact with robotic devices, searching for the best way of transferring human skills to robotic applications.

The proposed thesis fits within the analysis of physical Human Robot Interaction and aims at developing innovative control algorithms to improve cooperative tasks execution performance while interacting with an industrial robotic arm. Users arm dynamic characteristics will be estimated and used to adapt the robotic arm proprieties during the execution of tasks in which the user directly manipulate the robot’s end-effector.

The thesis will provide students with insights in Motor Control theories, real-time estimation of arm kinematics and dynamics, robotic control, EMG signals conditioning and programming. Moreover, machine learning and deep learning techniques could be employed in the development of the novel impedance controller.

Thesis 2D Digital Subtraction Angiography obtained through CE-CBCT raw data subtraction
Supervisors Elena De Momi, Sara El Hadji
Description Brain vasculature visualization is gaining more and more importance in the fields of neuroimaging and neurosurgery. In the specific case of brain vessels segmentation, this task can be highly recommended for the detection of vascular neurological diseases such as ArteroVenous Malformations (AVMs), or for the preoperative planning of neurosurgery treatments such as Stereotactic RadioSurgery (SRS) [3], deep brain stimulation (DBS) implants [4], StereoElectroEncephaloGraphy (SEEG). At this purpose, we are developing a fully automatic method for the segmentation of arteries and veins obtained through the post-processing of cone-beam CT (CBCT) raw projection data, together with the angiogram obtained from a CBCT digital subtraction angiography (DSA). In this framework, we propose a thesis that aims at investigating new methods for 2D brain vessel segmentation from 2D Digital Subtraction Angiography obtained through CE-CBCT raw data subtraction. This information is crucial for our algorithm and currently it is one of the step that mostly needs to be investigated.
Thesis Unsupervised segmentation of surgical workflow using deep learning
Supervisors Elena De Momi Hirenkumar Nakawala
Collaborations University of Verona
Description Recent advances in computer-assisted surgeries would led to possible change in the surgical paradigm from a manually done mechanistic task to monitoring and steering task in the future. A large amount of intraoperative information has to be analysed, reasoned and applied by surgeons to comprehend the surgical situation. The progress in computer-assisted surgery is further hampered by a deficiency understanding operational tasks and know-how and focused information availability on surgical workflow.  Deep learning is currently emerged as important models to understand surgical workflows. However, supervised learning has been used which needs many labelled data, and it is also often associated with human errors, and less generalisable to different surgical procedures. Unsupervised learning could help to understand the patterns underlying the surgical data esp. surgical videos. This thesis investigates unsupervised segmentation of surgical workflow of robot-assisted partial nephrectomy using deep learning methods to recognise surgical steps and help in decision making.
Thesis Inductive logic programming and data mining for knowledge discovery of contextual information in surgery
Supervisors Elena De Momi, Hirenkumar Nakawala
Collaborations University of Verona
Description Knowledge discovery and data mining is an interdisciplinary area focusing upon methodologies for extracting useful information from data. With advancement of surgeries, there are abundant data generated by intraoperative sensors. The surgery is a process which combines different contextual information e.g. relations between steps, instruments and actions. Inductive logic programming is a data mining technique which could be used to create the knowledge which would be able to help to understand this surgical contextual information. The thesis aims at investigating inductive logic programming methods to understand surgical video, annotated with surgical entities, which could be helpful in context-awareness and decision making.
Thesis Automatic surgical process modeling based on data mining techniques
Supervisors Elena De Momi, Hirenkumar Nakawala
Collaborations University of Verona
Description The current methods of modelling surgical processes are based on top-down modelling approach i.e. modeler’s experience, information in the literature and interviews with the domain experts or any combination of these methods. Top-down approach is highly time consuming, expensive and biased due to human subjectivity. Moreover, surgical processes are dynamic and change constantly, where these process models could not be useful. Data mining techniques could be used to understand the data to discern patterns that would help to develop surgical process model automatically. The proposed thesis will be focused on creating surgical process model automatically from an annotated video dataset of robot-assisted partial nephrectomy, which could be helpful in context-awareness.
Thesis 3D Virtual environment on Samsung
Supervisors Elena De Momi, Vertemati Maurizio
Collaborations University of Milan, Italy
Description

Virtual Reality Environments (VREs) are increasingly used in medicine for didactic and clinical purposes, specially thanks to the delivery of low-cost Head-Mounted Displays (HMDs).

We developed an interactive VRE to visualize 3D patient-specific anatomical models, establishing a workflow to transfer 3D models from imaging datasets to immersive VR. The purpose of the project is to develop a tool useful to train novice surgeons, improve medical imaging analysis and provide students the experience a three-dimensional (3D) and patient-specific anatomy.

We employed the Samsung Oculus Gear VR, connected to the Samsung Galaxy S6 Edge.

The 3D scene was created from CT scans segmented using 3D Slicer, a software for imaging analysis; the models were post-processed using Blender and Unity 3D.

Once loaded, the scene can be zoomed in/out and rotated around the vertical axis using the integrated touchpad of the Gear VR. By swiping up/down the scene started to zoom in/out and by swiping forward/backward a counter-clockwise/clockwise rotation movement began. A simple tap stopped every motions.

We already collected an enthusiastic feedback from doctors, trainee doctors, medical students and professors.

Aims for the future:

· Develop a code to control the scene with the controller using hand gestures

· Implement an in-app menu to select and hide/show organs or change their transparence to make visible the inner lesions

· Implement the possibility to change 3D scene

· Develop a desktop software to load new models without re-encoding the .apk on Unity

 

Thesis Brain Diffusivity from Extra-Cellular Space (ECS) Geometry
Supervisors Elena De Momi, Marco Vidotto
 
Description
  • Clinical problem: Glioblastomas
  • Convection-Enhanced Delivery is a therapeutic treatment consisting in the injection under positive pressure of drugs directly in the brain tumour zone, so that pharmacological agents that would not normally cross the blood brain barrier (BBB) can be used.
  • References:Charles Nicholson, Padideh Kamali-Zare, and Lian Tao, Brain Extracellular Space as a Diffusion Barrier, 2013
Thesis Autonomous Robotic Surgery (ARS)
Supervisors Elena De Momi, Paolo Fiorini
Collaborations University of Verona, Italy
 
Description
  1. Analisi di Big-data sets riferiti alla chirurgia robotica
    • Definizione e calcolo di metriche di prestazione
    • Analisi e applicazione di metodi di Reinforcement Learning o di Deep Learning per:
      • Identificazione della scena anatomica
      • Identificazione del modello dell’intervento
  2. Applicazione di ontologie ai modelli di interventi
  3. Progettazione di strumenti chirurgici robotici usando tecniche di fabbricazione additiva
  4. Segmentazione automatica di fasi di intervento chirurgico
  5. Ragionamento automatico durante un intervento usando reti di Bayes e/o causali
  6. Modellazione grafica e/o biomeccanica di ambienti anatomici
  7. Analisi di un intervento chirurgico e sviluppo della corrispondente macchina a stati (automa ibrido)
  8. Analisi del grado di risoluzione nella rappresentazione di un intervento chirurgico
  9. Analisi della sicurezza in chirurgica robotica: applicazione del metodo STAMP
Thesis Please see the available theses titles in the description
Supervisors Elena De MomiRiccardo Muradore
Collaborations University of Verona, Italy
Description 1.Development of bilateral teleoperation algorithm for the da Vinci system
(no communication delay, constant and known delay, time-varying delay)2.Identification of the mathematical model of the da Vinci robot system3.Computation of dynamic 3D model of anatomical scenes4.Mathematical modeling of basic surgical tasks (needle insertion, suturing, cutting)
Thesis Please see the available theses titles in the description
Supervisors Elena De MomiRiccardo Muradore
Collaborations University of Verona, Italy
Description 1. Comparison of bilateral teleoperation algorithms in terms of human-centric metrics

2. Comparison of bilateral teleoperation algorithms in terms of control metrics (stability, Z-width, transparency)

3. Development of bilateral teleoperation algorithm for remote needle insertion

Thesis Robot Adaptation to Human Fatigue in Human-Robot Collaboration
Supervisors Elena De Momi, Arash Ajoudani
Collaborations Italian Institute of Technology, Italy
Description The aim here is to detect and process the human fatigue in human robot interaction and collaboration scenarios.The robot’s reactive behaviour will contribute to a reduction of the human physical fatigue.
Thesis Surgical Robot for Laser Osteotomy 
Supervisors Elena De Momi, Gabor Kosa
Collaborations University of Basel
Description Context: The Department of Biomedical Engineering (DBE) has recently been awarded a Flagship project from the Werner Siemens Foundation. The principal aim of this interdisciplinary project called Minimally Invasive Robot Assisted Computer-guided Laserosteotomy (MIRACLE) is the development of a minimally invasive robotic endoscope for cutting bone with a laser.

Task description: In this project, the Master candidate will participate in the development of a surgical robot for the manipulation of a semi-flexible endoscope. The robot has 6 DoF for the control of the position and orientation of the endoscopes tip and an additional DoF for the bending of the tip. An initial prototype, named GG-1, (see attached figure) of the robot is already operational and currently we are developing for it new control schemes and path planning, and improving the mechanical design and teleoperation. The candidate will be able to get involved in the design, dynamics and control aspects of GG-1-s development. Otherpossibletasksaretheutilizationoftherobotinasurgicalprocedure: characterization of the task, programming the robot and evaluation of its performance.
What we offer:

• You will be able to build your own robot.

• You will learn kinematics, dynamics and control of robotic arms.

• You will join a team of medical doctors and engineers developing novel medical devices.
Your Profile: Background in mechatronics, mechanical engineering, biomedical engineering or in a closely related discipline. Excellent skills and practical experience in one or more of the following research areas: robotics, mechatronics, control, mechanical design. Fluency in written and spoken English is required.

Thesis Synergy Driven Robotic Teleoperation: A Novel Control Approach
Supervisors Elena De Momi, Arash Ajoudani
Collaborations Italian Institute of Technology, Italy
Description The project exploits the use of reduced-complexity models of the human physical interaction behaviour in teleimpedance control.
Thesis Magnetic telemanipulation of a soft-tethered endoscopic capsule for painless colonoscopy
Supervisors Elena De Momi, Prof. Pietro Valdastri
Collaborations University of Leeds, UK

Description The student will join the multi-disciplinary research carried on at the Science and Technology of Robotics in Medicine (STORM) Lab at the University of Leeds, where we are developing a robotic platform for painless colonoscopy. This platform is based on the recently introduced KUKA lightweight robot certified for medical use (we are among the first research centers around the world having access to this technology). Research topic will include automation of robotic tasks in endoscopy, human-robot interaction, machine learning for manipulation planning and control, manipulation based on real-time position and force sensing, and platform validation in collaboration with our clinical partners. Frequent interactions with Vanderbilt University (TN, USA) and other partner Universities in the UK (http://sonopill.dundee.ac.uk/) will offer an outstanding research experience.
Thesis Background: Micro-robots can be used in applications such as microassembly, micromanipulation, and minimally invasive surgeries. Proper microscopic imaging is fundamental for the closed-loop control of these robots, especially when performing 3D reconstruction. For this purpose is fundamental to be aware of all parameters of the optical system, including the selected magnification.
Supervisors Elena De Momi, Prof. Sarthak Misra,  MSC Federico Ongaro
Collaborations Surgical Robotics Lab, University of Twente
 

Description Tasks: In this assignment you will be guided in the design of a nonlinear control for the exertion of electromagnetic torques. Subsequently, you will have to experimentally validate your approach and determine the effect of the control parameters on the propulsion and behavior of micro-robots.
Thesis Background: Micro-robots have the potential to revolutionalize the fields of microassembly, micromanipulation, and minimally invasive surgery. However, as their size becomes smaller higher levels of magnification are required. These often present the drawback of a smaller focal depth (volume in focus). Consequently, it is necessary to move the imaging device to maintain the tracked micro-robot in the focus plane.
Supervisors Elena De Momi, Prof. Sarthak Misra,  MSC Federico Ongaro
Collaborations Surgical Robotics Lab, University of Twente

 

Description Tasks: In this assignment you will control micron-precision linear stages to design and code an algorithm capable of dynamically correcting the position, and magnification of the cameras to maintain the micro-robot in focus. Information regarding the control output and expected position will be available to you for this purpose. Furthermore, the algorithm will have to be flexible and capable of operating with ultrasound imaging.
Thesis Background: Coils that used for magnetic manipulation can be driven at high frequencies to obtain low ripples in magnetic field lines and fast control response. In the literature, many studies were conducted on high frequency motor driving. Inspiring from these studies, control characteristics and driving methods of a coil driven at high frequency will be investigated.

 

Supervisors Elena De Momi, Prof. Sarthak Misra, Mert Kaya, Alper Denasi
Collaborations Surgical Robotics Lab, University of Twente
Description Tasks: In this project, aim of the research can be divided into three consecutive stages. In the first stage, the properties of magnetic cores will be investigated and a suitable magnetic core will be selected. In the second stage, a current amplifier which is capable of driving the coils up to 300 kHz will be designed. In the third stage, PCB schematic for the amplifier will be designed and fabricated. Further, experiments using magnetic micro agents will be carried out.

Prerequisite: The student who will work in this project should be able to work under flexible working hours.

Thesis Background: The current practice of endovascular procedures is limited by a number of factors. These factors include patient specific operation requirements, high-risk surgery procedures and time consuming operations. As a solution, continuum manipulators with a focus on magnetically actuated surgical catheters, have been introduced to the field of surgical robotics. The goal is to demonstrate the control of a magnetically-actuated steerable catheter in an experimental in vitro testbed with the aid of UR-robotic arms.
Supervisors Elena De Momi, C.M. Heunis & Prof. S. Misra
Collaborations Surgical Robotics Lab, University of Twente
Description Tasks: Design: Design an experimental testbed for clinically relevant cardiac / endovascular procedures;

Modelling: Model the experimental setup using Simulink and Matlab for control;

Calculation: construct algorithms to control the motions of the robot manipulator;

Simulation: reformulate dynamic equations so that acceleration is computed as a function of actuator torque

Thesis Virtual fixtures in robotic laser surgery
Supervisors Elena De Momi, Leonardo De Mattos
Collaborations Italian Institute of Technology, Italy
Description Augmented-reality dynamic constraints to improve safety during laser surgery

IIT contact: leonardo.demattos@iit.it

Thesis Real-time endoluminal cancer screening system
Supervisors Elena De Momi, Leonardo De Mattos
Collaborations Italian Institute of Technology, Italy
Description Computer vision algorithm optimization

FPGA implementation for real-time processing

IIT contact: leonardo.demattos@iit.it

Thesis Dexterous tool for endoscopic laser microsurgery
Supervisors Elena De Momi, Leonardo De Mattos
Collaborations Italian Institute of Technology, Italy
Description Device design, development and control
IIT contact: leonardo.demattos@iit.it
Thesis Robotic teleoperation of flexible needles with haptic feedback
Supervisors Elena De Momi
Collaborations IRISA and Inria Rennes Bretagne Atlantique research center, Rennes, France
Description Flexible needles and haptic feedback are two significant technological advancements in needle insertion. Flexible needles provide the clinician with enhanced steering capabilities, and haptic feedback enables the clinician to receive information about the forces exerted by the needle on the tissue being penetrated.

We propose to study innovative teleoperation systems for steering flexible needles, exploiting grounded and ungrounded haptic stimuli for our vision-based needle insertion system, with the final objective of maximizing the information provided, the clinician comfort, and the medical procedure’s safety and effectiveness.  The project will develop four main key aspects:

-Perception and combination of multiple haptic stimuli, focusing on force, vibrations, and skin stretch.

-New ways of assistance solutions, where the clinician can retain total or partial control of the needle positioning.

-Study of how to provide effective guiding haptic stimuli, e.g., enforcing active constrains.

-Improve existing stability control approaches to consider for the additional tactile stimuli.

Contacts: Claudio Pacchierotti (claudio.pacchierotti@irisa.fr),  Alexandre Krupa (alexandre.krupa@inria.fr)

Thesis Force feedback adaptive rehabilitation
Supervisors Elena De Momi
Collaborations Prof. Bernard Bayle , University of Strasbourg, CNRS, INSA Strasbourg
Description Research to:

  • understand the human manipulation ability
  • improve the quality of robotic systems dedicated to collaborative manipulation

Use of force feedback technology to improve manipulation skills and allow for adaptive rehabilitation

Thesis Human dynamic model identification from EMG measurements
Supervisors Elena De Momi
Collaborations Prof. Bernard Bayle , University of Strasbourg, CNRS, INSA Strasbourg
Description Research to:

  • understand the human manipulation ability
  • improve the quality of robotic systems dedicated to collaborative manipulation

Use automatic control methods to identify human models from EMG activity:

  • follows an internship in summer 2017, made by a French student in Milano
Thesis Make the laser cut and feel like a scalpel!
Supervisors Elena De Momi
Collaborations Loris Fichera, Worcester Polytechnic Institute, USA

WPI COMET Lab (COgnitive MEdical Technology and robotics laboratory)

 

Description
  • Laser cutting is an energy-based, contactless process
  • Control of laser parameters (power, exp. time) is not always intuitive
  • Would haptic feedback enhance the accuracy of laser incisions?

Goals of this thesis:

1.Develop software to interface a haptic device with a laser surgical system

2.Implement different haptic modalities (kinesthetic, vibrotactile, etc.)

3.Design and perform an experiment on real tissue to validate the system

More details at:

https://www.dropbox.com/s/1amqbx9ok3gd2va/Fichera-WPI-MS-theme.pptx?dl=0

Thesis Stiffness principles for soft robots
Supervisors Elena De Momi
Collaborations Dr Helge Wurdemann, University College London
Description This project aims at developing a new stiffness mechanism for soft robotic devices. Granular material will be pumped into an air-tight sleeve which is tendon-actuated.
Thesis Steerable colonoscope
Supervisors Elena De Momi
Collaborations Dr Helge Wurdemann, University College London
Description This project aims at creating a robotic device for colonoscopy. The new device will make use of soft, stiffness-controllable robots that have been developed in the lab.
Thesis Steerable catheter with 2 DoF
Supervisors Elena De Momi
Collaborations Dr Helge Wurdemann, University College London
Description This project aims at creating a robotic catheter that can steer around the aortic arch. The student would need to develop a mechanism and actuation principle. This device aims at transcatheter aortic valve replacement.
Thesis Building a vascular phantom
Supervisors Elena De Momi
Collaborations Dr Helge Wurdemann, University College London
Description This project aims at building a vascular phantom using silicone-like materials and integrating pulsatile pumps. This system will benefit the training of future clinicians and testing cardiovascular medical devices.
Thesis Raman spectroscopy to segregate between heatlhy and carcinogenic brain tissues.
Supervisors Elena De Momi
Collaborations Renishaw plc.Istituo clinico Humanitas

Description Raman spectroscopy is an emerging technique used to assess the biochemical component present in the analysed sample. Its use spreads from archaeology to pharmacology and only in recent years it has been applied in biology. By measuring the scattered light of a laser beam hitting the sample, it is possible to analyse the chemical bonds composing the latter, obtaining the so called fingerprint of the sample. When applied to pharmacology or to archaeology, fingerprints of different molecules can be well discriminated and the elements composing the samples can be identified almost completely. When applied to biology, anyway, the identification of the elements composing the tissue is not that straightforward and further mathematical analysis are required. The new frontier of Raman spectroscopy is to use it to differentiate among different tissues, in particular between healthy and tumor tissue. The project of this thesis is to use Raman spectroscopic singal to segregate between different brain tumors. The aim of this study, in particular, is to differentiate among fresh tissue rather than formalin fixed tissue, so to investigate possible use of Raman spectroscopy for on-line operative seesion.
Thesis Automatic trajectory planning for SEEG
Supervisors Elena De Momi
Co-Supervisors Davide Scorza
Collaborations Centro per l’epilessia Claudio Munari, Niguarda
Vicomtech, Spain
thesis7
Description
  • Aim:

To design and implement an automatic planner for keyhole neurosurgery

  • Project phases:

-Design and implement the plan
-Collect dataset and implement a database
-Validate the methodology on patient images

Thesis Motor learning during surgical tasks
Supervisors Elena De Momi
Collaborations Northeastern University, Boston, USA
Carnegie Mellon University, Pittsburgh, USA
Istituto Neurologico Carlo Besta
thesis4
Description
  • Aim:

To understand the motor control mechanisms involved in tremor compensation under microscope magnification in surgery

  • Project phases:

-Design an experimental protocol for task acquisition
-Develop models of motor learning
-Validate models using experimental data

Thesis Redundancy management in teleoperation for surgical applications
Supervisors Elena De Momi
Co-supervisors Hang Su
Collaborations Neuroengineering and Medical Robotics Lab
thesis3
Description
  • Aim:

To improve the robot control during tele-operation increasing the patient safety

  • Project phases:

-Robot redundancy management criteria
-Integration of optimization criteria in the controller
-Testing and validation using the Nearlab suite

Thesis Steerable needle path planning
Supervisors Elena De Momi
Co-supervisors Alberto Favaro
Collaborations Istituto Clinico Humanitas, Rozzano, Milan, Italy
Imperial College of London, London, UK
thesis1
Description
  • Aim:

To plan the optimal trajectory of drug delivery steerable needles in order to treat high grade gliomas

  • Project phases:

-Develop a model of the brain
-Implement path optimality criteria
-Implement kinematic control on the needle
-Perform validation tests on sheep models

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