Current PhD projects

PhD Candidate

Davide Scorza

Advisors

Elena De Momi, Luis Kabongo (Vicomtech)

Collaboration

Vicomtech-IK4 (Donostia/San Sebastian, Gipuzkoa – Spain)

Title

Automatic planning for intracranial electrode placement in SEEG

Description

Clinical Application and Current Limitations: Stereo-ElectrodeEncephaloGraphy (SEEG) is a minimally invasive technique which allows the exploration of brain’s activity in patients affected by focal epilepsy, helping the identification of the epileptogenic zone. The planning phase for electrode trajectories can be cumbersome and very time consuming, and actual commercial software do not provide any quantitative information regarding trajectory risks.

Purpose: The PhD thesis is focused on the development of an automated planner for intra-cranial electrodes insertion, based on the criteria that guide the intervention. The final aim is to assist surgeons during the electrodes’ trajectories planning phase and improve the results in terms of precision, safety and time consume.

Automated Planner: An automated planner has been developed as a 3D Slicer application to optimize the trajectories starting from an approximated input of the surgeon. The whole extension is composed by different modules

  • the electrode representation module is a simple module which allows the surgeon to represent the electrodes and add new models by defining their geometrical properties.
  • the image processing module to process the patient images before starting the optimization
  • the SEEG planning module, which allows electrode initialization and optimization by maximizing the distance from vessels and minimizing the insertion angle. In addition, the use of an atlas allows to keep the targeted brain zones during the exploration of feasible optimum trajectories. Finally, inter-electrode conflicts are checked to avoid collisions in the proposed trajectories.
  • the SEEG check trajectory module, that offers advanced tools to the surgeons to analyse the feasibility and viability of the proposed trajectories. Maximum Intensity Projection (MIP) of the vessels can be generated on the probe-eye-view plane, and an automatic segmentation method based on Gaussian Mixture Model and Markov Random Field assist the surgeon to understand the presence of vessel along the trajectory.

Figure 1 – on the left, the planning requirements that have been identified for SEEG intra-cranial electrode placement. In the centre The SEEG automated planner extension architecture that has been developed in 3D Slicer. On the right, three main constraints that are considered to guarantee the patient safety and the planning effectiveness (anatomy-driven exploration, vessel avoidance, perpendicular insertion angle).

Figure 2 – preliminary results of the 3D vessels model obtained by an automated segmentation algorithm based on Gaussian mixture model and Markov random fields.

To increase the cognitive capacities of our system, we collected a large amount of data regarding successfully past cases and building a database that will be used to extract information to be applied to new patients.

Figure 3 – Schematic representation of a cognitive decision support system for SEEG trajectory planning. The surgeon will provide basic inputs that will be matched with past cases in the database, and finally adapted to the anatomy of the current patient.

Such trajectories have been explored and analysed through machine learning approaches, allowing the identification of the most used trajectories used by our clinical centre. We built a new tool for interactive trajectory initialization based on the knowledge extracted from this data, in direct communication with the already developed planner.
This video provide a brief representation of the complete system.

On going work: we are actually conducting an extensive validation of the system in order to improve the algorithms already developed and to provide a more reliable system able to plan safe and effective trajectories.

PhD Period

XXXI cycle (Nov 2015 – Nov 2019)


PhD Candidate

Hang Su

Advisors

Prof. Giancarlo FerrignoElena De Momi

Title

Safety-enhanced Human-Robot Interaction Control of Redundant Robot for Teleoperated Minimally Invasive Surgery

Description

The PhD Thesis focuses on developing the Safety-enhanced control strategy for Human-robot Interaction in teleoperated Minimally Invasive Surgery (MIS). Although in the past decade, a lot of surgical systems has been developed to achieve greater accuracy and safety. In the future operating room, the robot should be more intelligent, flexible and human-friendly, not only to let the surgeon focus on the main surgical task, but also be able to work friendly with the nurse in the same workspace. Let the work of nurses and surgeons easier and enjoyable. The application scenario could be to ease the work for nurses sharing the same workspace. When there is a collision between nurse and robot arm in Fig. 1. The nurse could move the robot arm with hand without influence of the surgical tasks. As it is shown in Fig, 2, the user 2 could focus on the teleoperated MIS, and the user 1 could move the surgical robot arm by hand, simultaneously. Nonlinear adaptive controller is introduced to solve the uncertain disturbance from human-robot interaction, enhancing the precision and reliability of Surgical Robot systems. Prediction model and machine learning will also be applied to enhance the online performance of robot tele-operation so that the surgical robot can act intelligently in cooperation with nurse. Finally, experimental validation will be conducted to validate the performance of the developed real surgical robot system.

PhD Period

XXXI cycle (Nov 2015 – Nov 2018)


PhD Candidate

Alberto Favaro

Advisors

Elena De MomiGiancarlo Ferrigno; Feridnadno Rodriguez y Baena (Imperial College of London)

 Collaboration Imperial College London, U.K.
 Title Path planning for non-linear, non-holonomic systems

Description

The PhD thesis embraces different aspects of those keyhole neurosurgery procedures which involve the use of robotically-controlled flexible needles. In particular, path planning methods based on sampling-based techniques and smart workspace redefinition are investigated, aiming at computing the best trajectory for reach a desired target within the brain (e.g. for performing biopsy or drug delivery treatment) through the analysis and elaboration of MRI and CT images, guaranteeing the obstacle avoidance by maximising the distance from safety-critical anatomical structures and guaranteeing the compliance with needle’s kinematics. The latter represent a further field of study: the complex kinematics of non-holonomic robots, as a steerable needle for keyhole neurosurgery, is also investigated along with methods for predicting its behaviour inside soft tissues.
The work uses the EDEN2020 Programmable Bevel-tip Needle (PBN) as a case of study and defines surgical trajectories for brain tumor treatment in the context of the Convention Enhanced Delivery.

PhD Period

XXXI cycle (November 2015 – November 2018).


PhD Candidate

Marco Vidotto

Advisors

Elena De Momi; Daniele Dini (Imperial college, London)

 Title Convection Enhanced Delivery model for brain tumour treatment

Description

MarcothesisMarcothesis

Convection-Enhanced Delivery is a therapeutic treatment consisting in the injection under positive pressure of drugs directly in the brain tumorous zone. It has recently emerged as a promising delivery method because it allows to circumvent the blood-brain barrier, which is a major impediment in systemic delivery of macro-molecular therapeutic agents, by directly injecting into the parenchymal space. The treatment success depends on the CED protocol, e.g., infusion site, infusate concentration and infusate rate. Computational and analytical models of infusion may be used to predict drug distribution and optimize CED protocols. The PhD project focuses on the development of a new CFD (Computational Fluid Dynamics) model which exploits two types of information which are related to two different scales: a micro (µm) and a macro (mm) scale. Imaging techniques as the FIB-SEM (Focused Ion Beam – Scanning Electron Microscope) allow to access the geometrical organization of groups of single axons which is directly related to fundamental hydraulic parameters such as conductivity and tortuosity. On the other hand, on a macro scale, DTI (Diffusion Tensor imaging) provides essential information both on diffusivity and white matter orientation. This study aims to funnel micro and macro scale parameters in a unique and comprehensive model. These activities are conducted within the EDEN2020 EU project.

PhD Period

XXXII cycle (November 2016 – November 2019).

PhD Candidate

Sara El Hadji

Advisors

Elena De Momi; Giuseppe Baselli

Collaborations Dr.Francesco Cardinale, Epilepsy neurosurgeon at the Ospedale Ca’ Granda, Niguarda, in Milan; Medtronic, Littleton, Boston, USA
 Title ART 3.5D – a novel algorithm, to label arteries and veins from 3D angiography

Description

Segmentation and visualization of brain vessels are important in many neurological diagnostic and therapeutic applications: Embolization of cerebral arteriovenous malformations (AVMs), or their radio-surgical treatment, make the three-dimensional (3D) reconstruction of brain vasculature of utmost importance and several studies indicate that 3D visualization of brain vasculature within multimodal imaging proved to be useful in epilepsy surgery. In particular, StereoElectroEncephaloGraphy (SEEG) planning, in addition to an accurate reconstruction of the cerebral vascular tree, requires also a distinction of arteries and veins, in order to increase the safety of the intervention and improve the completeness of the pre-operative planning phase.
The main objective of this PhD thesis is the development of a novel method for the automatic classification as arteries or veins of previously segmented vessels, 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). The presented approach extends the iterative algebraic solution from a linear time invariant (ART) to a linear time variant system exploiting information about system dynamics during contrast-enhanced CBCT acquisitions. The Acronym ART was thus extended to the case of dynamic image reconstruction, accordingly named ART 3.5D. The strategy works on the 3-D angiography, previously segmented in the standard way, and reprocesses the dynamics hidden in the raw data to recover an approximate dynamic in each segmented voxel. Next, a classification algorithm labels the angiographic voxels and artery or vein.
Other topics investigated during the phd includes the application of deep learning technique for 3D brain vasculature segmentation, sinogram preprocessing and artifacts reduction and CBCT reconstruction.
This research study is conducted in collaboration with Dr. Francesco Cardinale, Epilepsy neurosurgeon at the Ospedale Ca’ Granda, Niguarda (Italy), and Medtronic (Littleton, Boston, USA).

PhD Period

XXXII cycle (February 2016 – F ebruary 2019).



PhD Candidate

Marta Lorenzini

Advisors

Elena De Momi, Dr. Arash Ajoudani

Collaborations

Italian Institute of Technology

Title

Improvement and evaluation of human ergonomics and workplace safety in human-robot collaboration

Description

The introduction of robots in human environment has countless benefits. Robots can reduce fatigue and stress as well as prevent injuries by assisting humans in industrial scenarios or in daily activities. This PhD project focuses on the development of assistive robotic strategies to guide and help humans to perform their tasks in the most comfortable and ergonomic body configurations to avoid pain and consequent injuries. To achieve this objective, robots must monitor human states in real-time and the profile of the interaction forces they exchange with each other and the environment. On the basis of this dynamic data, which can be collected using suitable sensory systems, it is possible to estimate the physical loading of the humans in terms of joints overloading. From this point, two complementary directions will  be explored: implementing feedback modalities to alert humans about their physical loading (modalities which range from simple screens showing which joints are too much loaded, to mechanisms that prevent improper body configurations); and secondly, developing control methods to make the robots anticipate and then guide the movements of the humans during the task to minimize the overloading joint torques. To this respect, thresholds to categorize joints overloading and methods to evaluate human ergonomics in the specific domain of human-robot collaboration will be developed.

PhD Period

XXXII cycle (May 2017 – May 2020).


PhD Candidate

Maria Lazzaroni

Advisors

Elena De Momi, Jesus Ortiz

Collaborations

Italian Institute of Technology

Title

Human exoskeleton system

Description

An exoskeleton is a wearable device that generates appropriate assistive forces on its user, both for medical and industrial applications. In industrial manufacturing processes, various manual work tasks are still difficult to automate due to their complexity. These  activities can significantly load workers lumber spine, resulting in musculoskeletal disorders. The aim of this project is to develop a full-body powered exoskeleton to assist human effort performing manual handling tasks in industrial environments. In order to develop flexible systems, adaptable for different applications, a modular approach will be adopted. Modular exoskeletons for upper limbs, lower limbs and full-body will be designed, starting from the trunk exoskeleton developed in the European project Robo-Mate. The design of these advanced exoskeletons has to take into account several issues: sensors, actuators, control systems and exoskeleton structure itself. Moreover, the human-exoskeleton interface should be studied.

In this context, my contribution will focus on human body surface analysis in order to enhance user’s comfort and guarantee a good coupling between the user and the device. Another important aspect that I will consider is the forces distribution on the human body surface. To evaluate forces distribution along with the effectiveness of the device, appropriate tests, performance evaluations and simulations will be regularly conducted while maintaining contact with users in order to answer their specific requirements and expectations.

PhD Period

XXXII cycle (May 2017 – May 2020).


PhD Candidate

Andrea Cimolato

Advisors

Elena De Momi, Leonardo De MattosMatteo Laffranchi

Collaborations

Italian Institute of Technology

Title

EMG-driven control in lower limb prosthesis

Description

Background: Neurorobotics leads human-technology interaction in the field of rehabilitation and prosthesis devices. Recent technology innovations made possible to design the modern generation of smart prostheses able to behave accordingly to different environmental interaction and user locomotion recognition. Thanks to hierarchical controllers (Fig 1), semi-active and powered prosthesis can change their joint impedance adapting to user necessities, avoiding undesired energy expenditures. In spite of this, lower limb microprocessor controlled prostheses stands far behind from upper limb solutions, those are able to decode volitional motion intent through the employment of Electromyographic (EMG) signals.

Purpose: The objective of this PhD project is to design a novel control approach for lower limb neuroprosthesis based on EMG signals and other mechanical variables such joint angle position and velocities, linear accelerations and load. The designed control should permit the user to both voluntarily and automatically control the prosthesis depending on the incoming sensor signals.

On Going Work:

1) The first intent of this study will be to exploit the great potentialities resilient in musculoskeletal models for the design of novel EMG-driven control. The use of musculoskeletal model results particular handy in situation where biological internal variables are not directly accessible such as muscle length and activation (Fig 2). In this way, it is possible to compare the model results with the EMG measurements in order to derive a particular correlation that will be employed for the prosthesis control.

2) Second perspective of this is to study and to understand the possible employment of muscle synergies in the design of a neuro-driven Human Machine Interface. Further analysis will investigate how these synergies decay or modify with the amputation of the limb in final users.

PhD Period

XXXII cycle (May 2017 – May 2020).


PhD Candidate

Matteo Sposito

Advisors

Elena De Momi, Jesus Ortiz

Collaborations

Italian Institute of Technology

Title

Advanced physical Human-Robot Interfaces for full body orthotic devices

Description

There is growing interest in devices that aim to relief workers while performing physically demanding tasks characterized by poor ergonomics (e.g. overhead assembly or load lifting). Force augmenting industrial exoskeletons are one of the solution that is more promising for the performances they are capable of. However, several aspects still need to be optimized for a widespread of those devices in factories. Physical Human-Robot Interfaces (pHRI) are one of those aspects; an interface is responsible of transmitting assistive force to the body and link the exoskeleton to wearer. Interfaces alone are a matter of safety, comfort and efficiency of the device itself.
This work aims at developing a full set of interfaces (such as cuffs, braces, shoulder straps or any other type of attachment) that are optimized for unloading and transmitting forces in a comfortable and efficient way. Design requirements are dictated by user safety and comfort metrics, force transmission and robot stability. This research project includes different activities such as the formal description of the problem of stability and force transmission of the whole exoskeleton with a free body diagram, evaluation of different pressure and F/T sensors for the attachment in order to monitor user comfort, safety and collect feedback for an iterative design process, attachment prototyping and assessment.

PhD Period

XXXIII cycle (May 2018 – May 2021).


PhD Candidate

Alice Segato

Advisors

Elena De Momi

Collaboration

San Raffaele Hospital

Title

3D Navigation and Path Planning using Reinforcement Learning for Steerable Needle.

Description


The PhD Thesis focuses on developing a new model based reinforcement learning method that efficiently combines path planning methods and learnable heuristics for keyhole neurosurgery.
The problem of finding the best trajectory for brain surgery intervention can be formulated as a decision process where at every visited node an optimal action has to be taken minimizing the total accumulated cost. Replacing cost by reward, any such algorithm that generates cost minimizing actions generates reward maximizing actions thus becoming a candidate solver for Markov Decision Processes (MDPs). The final aim is to assist surgeons during the trajectories planning phase and improve the results in terms of precision, safety and time consumption. The work uses the EDEN2020 steerable catheter as a case study and defines surgical trajectories in the context of Convention Enhanced Delivery.

PhD Period

XXXIV cycle (Nov 2018 – Nov 2021)


PhD Candidate

Zhen Li

Advisors

Elena De Momi, Jenny Dankelman

Collaboration

Delft University of Technology (TU Delft)

Title

Path planning and real-time re-planning

Description


Given the deformable nature of the surroundings, RT planning and control is needed in order to guarantee that a flexible robot reaches a target site with a certain desired pose. ESR 13 will implement an accurate kinematic and dynamic model of the flexible robot incorporating knowledge on the robot limitations right in the planning algorithm so that the best paths are executed pre-operatively, considering the constraints on allowable paths, the location of the anatomic target and intra-operatively including the uncertainties in the adopted (and identified) flexible robot model and of the collected sensor readings (ESR5).
Advanced exploration approaches will be adapted to each specific clinical scenario, its constraints as well as the robot constraints such as its manipulability. Specific clinically relevant optimality criteria will be identified and integrated. This methods could try to keep away sharp parts of the instrument (e.g. tip) from lumen edges. RT capabilities will grant the possibility to re-plan the path during the actual operation.

PhD Period

XXXV cycle (Nov 2019 – Nov 2022)


PhD Candidate

Jorge Lazo

Advisors

Elena De Momi

Collaboration

ATLAS

Title

Computer Vision and Machine Learning for Tissue Segmentation and Localization

Description


The ATLAS project will develop smart flexible robots that autonomously propel through complex deformable tubular structures. This calls for seamless integration of sensors, actuators, modelling and control.
While contributing to the state of the art, the candidates will become proficient in building, modelling, testing, interfacing in short in integrating basic building blocks into systems that display sophisticated behavior.
Each flexible robot will be equipped with extero- and proprioceptive sensors (such as FBG) in order to have information on position and orientation, as well as on-board miniaturized cameras. Additionally, RT image acquisition will be performed using US sensors externally placed in contact with the patient outer body. In order to track the position of the flexible robot and to simultaneously identify the environment conditions RT US image algorithms will be developed. Deep learning approaches combining Convolutional Neural Networks (CNNs) and automatic classification methods (e.g. SVM) will extract characteristic features from the images to automatically detect the:

1) Flexible robot shape
2) The hollow lumen edges positions (to be integrated with ESR5)
3) Information on surrounding soft tissue shape and location

RT performance will be achieved by parallel optimization loops.

PhD Period

XXXV cycle (Nov 2019 – Nov 2022)


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