Current PhD projects

PhD Candidiate

Veronica Penza

Advisors

Elena De Momi, Leonardo Mattos (IIT)

Collaboration

Instituto Italiano di Tecnologia

Title

Enhanced Vision System to improve safety in robotic single incision surgery

Description

 

VeronicaThesisImage3 VeronicaThesisImage2
VeronicaThesisImage1
In the field of the abdominal surgery, new minimally invasive surgical procedures, such as Single Incision Laparoscopic Surgery (SILS), allow performing surgery introducing multiple instruments through only one access port, decreasing post-operative infections. However, SILS introduces some limitations in terms of surgeon maneuverability and vision of the surgical field.
The overall goal of the research is to enhance the visual information provided to the surgeon during robotic SILS procedures, based on the fusion of a pre-operative planning data with intra-operative information, such as 3D reconstruction and motion tracking, in order to improve the outcome of the surgery and the safety, helped by the definition of run-time updated safety constraints.

Intra-operative organ tracking

PhD Period

XXIX cycle (November 2013 – November 2016)


PhD Candidate

Nima Enayati

Advisors

Prof. Giancarlo Ferrigno, Elena De Momi

Title

Shared-control bilateral tele-operation for surgical robotics application

Description

Nima_project_2PhDNima_Enayati

The objective of this thesis is to develop a shared-control haptic interface for bilateral tele-operated surgical robotic systems that guarantees a maximum transparency and meets the requirements of surgical applications.
Development of such systems are based on reliable real-time implementations and robust kinematics methods such as timely handling of redundancies. The control design of these bilateral architectures includes control loops that ensure the proper display of the reaction force at the haptic interface side while tracking the commanded motion at the teleoperator side accurately. The presence of time-varying destabilizing factors such as hard contacts, relaxed user grasps, stiff control settings, and/or communication delays make the stability of these systems challenging and stabilizing controllers are needed to guarantee a stable behavior of the tele-operation system.
One of the most promising features of these systems is the possibility of overlaying pre- and intra-operative information over the operative workspace through different sensory modalities. Constraining the motion of the surgical tool is among the popular approaches that employ haptic capabilities of the operator to intuitively improve the outcome of the surgical procedure in terms of safety, accuracy and cognitive load. A novel approach is introduced in motion constraining that guides the surgeon in following desired trajectories during tele-operation, while causing less distraction compared to the state of the art methods. Finally, experimental validation is performed to evaluate various aspects of the system with appropriate setups and user populations.

PhD Period

XXIX cycle (November 2013 – November 2016)


PhD Candidate

Hirenkumar Nakawala

Advisors

Elena De Momi, Prof. Giancarlo Ferrigno

Title

Knowledge-driven context-aware framework for surgical assistance

Description

PhD_Hiren_Nakawala

The PhD project focuses on creating ontology-based surgical context-aware framework to aid surgical assistance. A large amount of data and information is produced by different sensors, e.g. endoscopes, in the surgical operative rooms during the surgical interventions. Effective utilization of sensors requires complex integration of data at various levels and in different serialization formats. Also, the amount of information produced in operation theatre outpace capability of surgeons to understand information effectively in time. Surgeries are also becoming complex with advent of new technologies such as robotic assistance which requires specialised training. An ontology-based context-aware system for surgical assistance was proposed using ontology engineering, computer vision techniques and semantic technologies. We believe, knowledge representation of surgical information and surgical workflow analysis has utmost importance to reduce iatrogenic complications by providing contextual information at a specific instance of time e.g. contextual information about a surgical instrument, which is required during the consecutive step, based on the step currently in progress. Aim of our work is to create a reliable and repeatable aid, such as context-aware system, to assist the clinician in performing surgical intervention and to therefore improve patient care, reduce medical errors and iatrogenic complications. Context-aware systems are expected to enhance decision making capability of the surgeons while operating in the complex surgical environments, during robotic surgery for instance, where specialized knowledge is required to perform the procedure. The framework could be possible to incorporate in the simulated surgical training environment and train surgeons to perform the surgical procedure consistently.

PhD Period

XXX cycle (November 2014 – November 2017)


PhD Candidate

Jacopo Buzzi

Advisors

Elena De Momi, Prof. Giancarlo Ferrigno

Title

Surgical gesture analysis in robotic teleoperation

Description

Jacopo_phd

The PhD project focuses on the analysis of the surgical movement during teleoperation. In the recent years, more and more surgical procedures have been enhanced by the use of teleoperated surgical robots. In this scenario, the surgeon interacts with a master controller, usually a multi degrees of freedom robot, to control an actuation unit (the slave) that directly manipulates the patient. The master controllers are usually designed to be able to reproduce all the movements the surgeon would do in free hand, nevertheless a sometimes extensive training phase is required to acquire familiarity with the teleoperation. The aim of this study is to understand if and how much the use of a master controller modifies the kinematic and musculoskeletal strategies adopted by the user, also trying to compare different controllers; in order to do so, the kinematic position of the user’s arm, and the muscular activation during specific tasks are studied. Thanks to this knowledge, it would be possible to potentially design better controllers, to create more specific training tasks and environment and to produce quantitative indexes to evaluate the surgeon’s ability in teleoperation.

PhD Period

XXX cycle (November 2014 – November 2017)


PhD Candidate

Sara Moccia

Advisors

Elena De Momi, Leonardo Mattos (IIT)

Collaboration

Istituto Italiano di Tecnologia

Title

Laryngeal disorder detection and classification in narrow-band endoscopic video

Description

Sara_PhD_pic

The early-stage laryngeal tumor detection is a primary goal in the current clinical scenario, since it dramatically reduces patient mortality or after-treatment morbidity. The distinction between malignant and healthy tissue can be done both exploiting the possible presence of macroscopic alteration and assessing the blood vessel features. While a macroscopic alteration can be found only in advanced-stage pathologies, the vascular tree change occurs since the first phase of the tumor growth. Narrow-band endoscopy has recently become the elective imaging modality in the diagnostic field, since allows obtaining a clear and contrasted vision of the vascular tree. Despite the continual technological advances, the difficulty in differentiating anomalies of vocal folds encourages physicians to research new strategies to aid the diagnostic process. The goal of this PhD project is to merge the well-established clinical knowledge in the field of tumor development with the most recent computer vision techniques, as to develop a new automatic tool able to offer a reliable early-stage diagnosis based on the analysis of the vascular tree. The main challenge deals with the extraction of meaningful frames, which is performed exploiting key-frame extraction strategies. For each frame, key-points are computed, according to a Hessian-based metrics. Both blurred frames (for which no key-points are detected) and redundant frames (for which the inter-frame key-point comparison shows negligible differences) are discarded. Fig. 1 show detected key-points for two consecutive endoscopic frames. Correspondences between the most significant key-points are depicted in Fig. 2. Deblurring and machine learning vessel segmentation algorithms are then exploited to extract the vasculature.

PhD Period

XXX cycle (May 2015 – May 2018)


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

Davide_PhDpic

Epilepsy represents a group of neurological diseases characterized by epileptic seizures and is possible to distinguish between two cases: general and focal epilepsy. The latter, sometimes, offers the possibility to remove the epilepsy focus through surgical intervention. Therefore, the identification of the seizures onset is a fundamental part. Stereo ElectroEncephaloGraphy (SEEG) is a technique that imply the insertion of intracranial electrodes in order to record brain activity at multiple levels and from multiple structures, allowing the identification of the focus. Surgeons need to plan electrodes trajectories manually, which could be a cumbersome task and very time consuming. The aim of this PhD thesis is to develop an automatic planner to assist surgeons during this phase, computing the optimal set of trajectories based on patient anatomy, surgical constraints and surgeon experience.

PhD Period

XXXI cycle (Nov 2015 – Nov 2019)


PhD Candidate

Hang Su

Advisors

Prof. Giancarlo FerrignoElena De Momi

Title

Adaptive Control of Surgical Robots for Neurosurgery Tele-operation Task

Description

Hang_PhDThesis_Pic

The PhD Thesis focuses on developing the adaptive control algorithm for tele-operated surgical robotic system sharing the workspace with people. Based on developed technology in surgical planning and navigation, the project involves a new paradigm for adaptive control algorithm in Medical Robots to secure its performance in manipulation, especially for neurosurgery. During the surgical tele-operation, redundancy optimization will be introduced to optimize the pose configuration for the surgical robot system. Then, adaptation and learning methods are proposed to enhance the precision and reliability of Surgical Robot systems, not only with force and position control, but also with redundancy optimization for the robot arm pose, which are all basic requirements for practical application of Medical Robot in surgery. Prediction model will also be applied to enhance the online performance of robot tele-operation so that the surgical robot can act in advance 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

Marco Vidotto

Advisors

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

 Title Convection Enhanced Delivery model for brain tumour treatment

Description

Marcothesis

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.

PhD Period

XXXII cycle (November 2016 – November 2019).