PhD Candidate

Sara El Hadji

Advisors

Elena De Momi; Giuseppe Baselli

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 – February 2019).

PhD Candidate

Alessandro Casella

Advisors

Elena De Momi, Leonardo De Mattos, Sara Moccia

Collaboration

Italian Institute of Technology

Title

Computer Vision Technologies for Computer-Assisted Fetal Therapies.

Description

Twin-to-twin transfusion syndrome (TTTS) may occur, during identical twin pregnancies, when abnormal vascular anastomoses in the monochorionic pla-centa result in uneven blood flow between the fetuses. If not treated, the risk of perinatal mortality of one or both fetuses can exceed 90%. To recover the blood flow balance, the most effective treatment is minimally invasive laser surgery in fetoscopy. Limited field of view (FoV), poor visibility, fetuses’ movements, high illumination variability and limited maneuverability of the fetoscope directly impact on the complexity of the surgery. In the last decade, the medical field has seen a dramatic revolution thanks to the advances in surgical data science analysis such as Artificial Intelligence (AI) and in particular Deep Learning (DL). However, few efforts have been spent in fetal surgery applications, mainly to the reduced availability of datasets and the complexities of fetoscopic videos, which are of low resolution, and lack in texture and color contrast.
Considering these challenges and limitations in the state of the art, this PhD project focuses on the exploration of deep learning techniques for the development of tools for computer-assisted fetal surgery.

PhD Period

XXXIV cycle (May 2019 – May 2022)

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)

PhD Candidate

Luca Fortini

Advisors

Elena De Momi, Arash Ajoudani

Collaboration

Italian Institute of Technology

Title

Online Robotic and Feedback Strategies for Ergonomic Improvement in Industry

Description

Today manufacturing industries are faced with an increasingly competitive market. Production lines must be flexible and less structured to easily adapt to new workflows. As a result, industrial workers may be exposed to greater danger in terms of posture and load increasing the already dramatic statistics on Musculoskeletal Disorders (MSDs). My PhD project aims at improving the online monitoring of human body status to prevent wrong behaviours through the adoption of countermeasures either passive (feedback informative systems) and/or active (cobots, exoskeletons). To guarantee a continuous monitoring the subject is equipped with a set of sensors whose outputs are prerequisites for the estimation of custom ergonomic indexes. Depending on the task performed, a multi-objective ergonomic-based optimisation is run to establish the most suitable way of executing the duty. The outcome is then communicated to the feedback interface and/or to the robotic counterpart.

PhD Period

XXXV cycle (May 2020 – May 2023)

PhD Candidate

Luca Sestini

Advisors

Giancarlo Ferrigno, Nicolas Padoy

Collaboration

ATLAS

Title

Image-Based Tool-Tissue Interaction Estimation

Description

One of the keys to bring situational awareness to surgical robotics is the automatic recognition of the surgical workflow within the operating room (OR). Indeed, human-machine collaboration requires the understanding of the activities taking place both outside the patient and inside the patient.

This project will focus on the modelling and recognition of tool-tissue interactions during surgery to produce human–understandable information to be exploited during procedures inside the OR. The project will involve:
1)Developing a model to represent the actions performed by the endoscopic tools on the anatomy (e.g. through triplets: tool, action performed, anatomy acted upon)
2)Developing and training a deep-learning model to link the procedural knowledge describing the surgery to digital signals (such as endoscopic videos)
3)Exploiting robot model and kinematic information in order to limit the need for manual annotations for model training.

PhD Period

XXXV cycle (Nov 2019 – Nov 2022)

PhD Candidate

Chun-Feng Lai

Advisors

Elena De Momi, Jenny Dankelman

Collaboration

ATLAS

Title

Distributed follow-the leader control for minimizing tissue forces during soft-robotic endoscopic locomotion through fragile tubular environments

Description

Using prior experience on developing snake-like instruments for skull base surgery, the PhD candidate will elaborate these mechanical concepts into advanced soft-robotic endoscopes able to propel themselves forward though fragile tubular anatomic environments to hard-to-reach locations in the body. The ESR has two main goals:
• to use advanced 3D-printing to create novel snake-like endoscopic frame-structures that can be easily printed in one printing step without need for assembly, and that easily integrate cameras, actuators, biopsy channels and glass fibres. FEM-simulations to optimize a) shapes that are easy to bend yet hard to twist and compress (e.g. by using helical shapes), b) minimal distributions of actuators enabling complex and precisely controlled motion.
• to develop follow-the-leader locomotion schemes for moving through fragile tubular environments (e.g. colon or ureter) and evaluate this ex-vivo in anatomic tissue phantoms.

PhD Period

XXXV cycle (Nov 2019 – Nov 2022)

PhD Candidate

Emir Mobedi

Advisors

Elena De Momi, Arash Ajoudani

Collaboration

Italian Institute of Technology

Title

Design and Development of Assistive Robotic Technologies for Industrial Applications

Description

In this project, I will propose a novel one DoF underactuated exoskeleton device to allow the human workers achieve their tasks in an ergonomic way. Such a device can then provide assistance for the internal (resulting from the human body) and external (task-related payload) loading. Moreover, the controller frameworks also will be developed to include the aforementioned exoskeletons into the HRC frameworks. Consequently, robot responses will be formed to assist the worker to perform the intended manipulation task in configurations in which the effect of external stimuli on human factors are minimum. An experimental evaluation of the proposed framework will be carried out at HRI2 lab. The outcome of my PhD studies will drive radical improvements in adaptability and time-efficient reconfigurability of the HRC systems to the worker physical states and tasks, and will contribute to the reduction of musculoskeletal disorders (MSDs).

PhD Period

XXXVI cycle (May 2020 – May 2023)

PhD Candidate

Francesco Iodice

Advisors

Elena De Momi, Arash Ajoudani

Collaboration

Italian Institute of Technology

Title

Advance Human-Robot Interaction and Collaboration

Description

The collaboration between man and robot is one of the fundamental aspects of Industry 4.0. In this regard, the objective of this theme is to create advanced predictive control schemes that, integrated with computer vision techniques for detection and tracking, and algorithms for mapping and localization of the robot in the environment, will allow the real-time adaptation of collaborative robots to dynamic factors and human intentions. This flexibility will minimize the risk of injury to workers.

PhD Period

XXXVI cycle (May 2020 – May 2023)

PhD Candidate

Fabio Fusaro

Advisors

Elena De Momi, Arash Ajoudani

Collaboration

Italian Institute of Technology

Title

Robotic Human-Aware Task Planning And Allocation For Human Robot Collaboration

Description

The raising awareness of worker ergonomics and the flexibility requirements of the modern enterprises have called for a radical change in manufacturing processes. Thereupon, companies started to focus on the design of new production lines, rather than on corrective ex post interventions, which were much more expensive and not always effective. Several new tools such as collaborative robots (cobots) and wearable sensors and displays (e.g., augmented reality) were introduced, with the aim to provide better working conditions for human labour, while keeping high levels of productivity, flexibility, and cost-efficiency in manufacturing processes.
Cobots, in particular, have demonstrated the potential of pushing small-medium enterprises towards highly adaptive and flexible production paradigms. The idea is to exploit robots capabilities to plan and allocate tasks in industrial environments to enhance the efficiency of the production lines not only from an enterprise point of view, e.g. reducing cycle times, costs, but also to improve worker conditions and safety, e.g. reducing workload, improving ergonomics.

PhD Period

XXXVI cycle (May 2020 – May 2023)

PhD Candidate

Francesco Tassi

Advisors

Elena De Momi, Arash Ajoudani

Collaboration

Italian Institute of Technology

Title

Augmented Hierarchical Quadratic Programming based control algorithm for improved human-robot collaboration.

Description

Across many application domains, robots are expected to work in human environments, side by side with people. Interactions between robots and their users will take many forms, from a trained operator supervising several industrial robots, to an older adult receiving care from a rehabilitation robot, to a child safely practicing social, cognitive, or emotional skills with a readily available socially assistive robot. Robotic products are expected to be intuitive, easy to use, and responsive to the needs and states of their users, making human-robot interaction (HRI) a key area of research.
Some areas of robot control deal with very fast interactions with the environment, but HRI is unique in requiring a broad spectrum of temporal dynamics: interactions that happen very quickly (a wink or a twitch of the mouth), interactions that happen very slowly (gradually getting used to a pattern of behavior), and interactions that change unexpectedly (due to context or intent inaccessible to the robot).
The purpose of the PhD theme is to follow this direction in order to deal with the multiple open challenges that characterize the field of HRI. More in detail, the work considers an alternative control algorithm that does not strictly rely on pure motion planning in order to navigate and move in a realistic environment, but that instead allows to specify a desired behavior for the robot, that would be able to accomplish a series of tasks. This is done through the use of optimization-based techniques and Hierarchical Quadratic Programming (HQP), which permits to define a Stack of Tasks (SoT) to be performed by the robot in hierarchical manner. In this way, it possible to account also for human intentions and movements, adjusting the robot behavior on the fly. The aim is thus to improve collaboration between human and robot, increase confidence level and trust in the machine, and ease the human from the most physically and mentally stressful tasks and operations.

PhD Period

XXXVI cycle (May 2020 – May 2023)

PhD Candidate

Ziyang Chen

Advisors

Elena De Momi, Giancarlo Ferrigno, Hang Su

Collaboration

–

Title

Vision-Driven Automatic Path Planner for Da Vinci surgical robot

Description

For the traditional minimally invasive surgery, the surgeon needs to manually design the surgical path, which is time-consuming and not accurate. This project focuses on the design of a novel automatic path planner based on vision for Da Vinci surgical robot. Only through the real-time acquisition of depth images during the operation, a safe and feasible operation path can be automatically planned to solve the disadvantages of the traditional operation mode. The ultimate goal is to help surgeons complete automatic path planning, and improve the safety, accuracy and real-time characteristics of minimally invasive surgery. This research first analyzes the mechanical structure and kinematics of Da Vinci surgical robot. Then, research makes a detailed theoretical analysis of advanced computer vision algorithms. Considering the time-consuming problem of the deep learning model in motion decision-making training, this research also proposes a new working method of dual model selection to improve the training efficiency.

PhD Period

XXXVI cycle (November 2020 – October 2024)

PhD Candidate

Junhao Zhang

Advisors

Elena De Momi, Hang Su

Collaboration

–

Title

Human-robot intelligent collaborative control by multi-sensors based human motion prediction for minimally invasive surgery

Description

In some medical tasks, the medical robot should have the function of assisting human operators to complete some tasks, for example, assisting physicians, patient support, and so on, they provide a flexible workspace for nurses or other staff. In this case, it is necessary to ensure the safety and the flexibility. Therefore, in this project:
1)Firstly, some human motion intention estimation methods will be proposed. With the help of inertial sensors, visual sensors and force sensors, human motion trajectories and force trajectories can be modeled with probabilistic models, such as the Gaussian process, through which human motion intention can be predicted by regression.
2)Secondly, the robot can adapt its own control strategy based on human motion prediction. Different from trajectory planning, this work aims to solve how to design an adaptive control strategy with real-time human motion prediction ability for robots.

PhD Period

XXXVI cycle (November 2020 – October 2024)

PhD Candidate

Junling Fu

Advisors

Elena De Momi, Hang Su

Collaboration

–

Title

Human-robot skills transfer during robot-assisted minimally invasive surgery

Description

Robot-assisted minimally invasive surgery has great potential for improving the accuracy, dexterity of surgeons while minimizing trauma to the patients. However, surgeons are required to implement delicate and complex operations during RAMIS, including cutting, stitching, knotting. To release the complexity of surgical operations and reduce the workloads of surgeons, human-robot skill transferring is proposed to improve the practicability of surgical robots.
1) Learning the surgical skills from surgeon experts and reproduce these surgical operations is an effective way to enhance the autonomy of the surgical robot in RAMIS. Three stages are included in this process, namely, demonstration, learning, and reproduction.
2) To improve the flexibility of surgical manipulators and achieve human-like control, advanced control algorithms will be implemented, e.g. variable stiffness control. Besides, haptic feedback will be integrated to realize the realistic immersion and enhance safety during RAMIS.

PhD Period

XXXVI cycle (November 2020 – October 2024)

PhD Candidate

Ke Fan

Advisors

Elena De Momi, Giancarlo Ferrigno, Hang Su

Collaboration

–

Title

Augmented reality (AR) guidance of the surgeon based on real-time 3D reconstruction of the surgical field

Description

Augmented reality (surfaces reconstruction and feature tracking) can enhance the surgical safety in robotic-based interventions largely. Based on the real-time 3D reconstruction of the surgical area, using dynamic active constraints, wearable devices are used for augmented reality guidance for surgeons and restrict the instrument to a safe area. The demonstration platform is based on the da Vinci surgical system. The laboratory model will be tested with surgeons to make the technology easier to develop and validate the acceptance by clinicians.

PhD Period

XXXVI cycle (November 2020 – October 2024)

PhD Candidate

Valentina Corbetta

Advisors

Elena De Momi

Collaboration

IRCCS San Raffaele Hospital

Title

Machine learning approach for path planning and control for steerable catheters in endovascular procedures

Description

Endovascular catheter-based interventions have revolutionised the cardiological field. However, they still present some limitations. First of all, to manoeuvre the catheters and the guidewires through the vessels and arteries, the operator has to rely on 2D or 3D imaging, such as fluoroscopy and echocardiography, and on the small axial forces and torques sensed at the fingertips. There is, therefore, a lack of force or haptic feedback to the interventionist from the contact between the catheter and the vessel, which makes it difficult to perceive the contact, particularly for novices. Indeed, these type of procedures are characterised by a steep learning curve, which impacts on the duration of the operation and the quality of practice. Difficulties arise due to vessel tortuosity and angulation, which are the principal reasons for failure in endovascular procedures.
Moreover, the interaction of the catheter and the guidewire with the arterial wall can lead to embolisation, perforation, thrombosis, and dissection.
The aim of this thesis is to develop an algorithm for path-planning and control of a catheter for endovascular procedures.
The methodology would exploit the high level decision making of the interventionist, while granting the advantages of robotic control. It will exploit softwares for dynamic simulation, like SOFA and Gazebo to model catheter deformations.
As use case, this work will consider the catheter developed in the ARTERY project.

PhD Period

XXXVI cycle (May 2021 – May 2024)

PhD Candidate

Anna Bucchieri

Advisors

Elena De Momi, Pietro Cerveri

Collaboration

Italian Institute of Technology

Title

Novel controls for upper limbs exo-skeletons

Description

Context:
New efficient techniques of rehabilitation are needed to help post-stroke patients regain lost motor functions. Particularly, rehabilitation robotics have proven to be efficient in assisting different sensorimotor functions by allowing high-intensity and highly repeatable exercises.
Purpose:
Following a bottom-up approach, the PhD project proposes to design novel control strategies for upper-limb exoskeletons for the rehabilitation of stroke patients. In particular, the control strategies will provide Assistance-As-Needed and corrective feedback in an Augmented Reality environment.

PhD Period

XXXVI cycle (May 2021 – May 2024)