Current master thesis

M.Sc. Candidate Federico Franco
Advisor Elena De Momi; Sarthak Misra
Collaboration Surgical Robotics Lab, University of Twente
Title Remote sensing of magnetic field for enhanced control of steerable magnetic manipulators
Description Federico_MSThesis

Magnetic actuation of continuum manipulators is based on the interaction between an external magnetic field generated by an array of coils and a permanent magnet fixed to the device. The field of each coil can be controlled independently. Exploiting this property, highly non-homogeneous fields can be shaped.

For precise control of the continuum manipulators both the magnitude and direction of the magnetic field should be known at any location within the workspace.

The aim of this master’s thesis is therefore to perform an on-line estimation of the magnetic field.

In order to do so a sensory system has to be designed and tested. The data, gathered using a microcontroller, will be used in a state observer algorithm to estimate the magnitude and direction of the magnetic field in any desired region of the workspace. This information will be used then to close the current feedback loop to control the magnetic field intensity used to steer the continuum manipulators.

We will test the reliability of the state estimator algorithm with different kinds of magnetic fields: starting from a field generated by a single coil to more complex fields generated by an array of coils.

M.Sc. Candidate Eleonora Tagliabue
Advisor Elena De Momi; Cristian Luciano (UIC)
Co-Advisor Thomas Royson (UIC)
Collaboration University Of Illinois at Chicago
Title Visuo-haptic model of prostate cancer based on MRE
Description EleonoraThesisMagnetic Resonance Elastography (MRE) is an emerging imaging technique which provides a 3D stiffness map of the region of interest by analyzing the propagation of shear waves.
MRE shows promise as a potential non-invasive tool for prostate cancer detection and its stratification by aggressiveness, since prostate tumors becomes harder than the surrounding healthy tissues due to changes in mechanical properties.
Even though the visualization of 3D elastograms alone would allow to accurately locate different tumor masses, stiffness is a material property usually experienced with the touch sense. A possible integration of visual and tactile feedback has the potential to give more information about the tissue than just the visualization of the same data.
This research project aims at integrating elasticity maps obtained through Magnetic Resonance Elastography and haptics into a Haptic Virtual Reality (HVR) model. The surgeon will be able to both recognize tumor masses visually in a 3D colormap and feel their distinctive resistance through a haptic device. The haptic feedback aims at enhancing the surgeon experience in the exploration of the different areas of the prostate.
This virtual manual palpation of the prostate will help in identifying and localizing CaP tumors within the gland with greater accuracy with respect to current methods, improving diagnosis and pre-surgical planning.
M.Sc. Candidate Cecilia Gatti
Advisor Elena De Momi; Cristian Luciano 
Collaboration University of Illinois at Chicago – Mixed Reality Lab, UIC Department of Urology
Title Effect of Haptic Feedback in Motor Learning
Description CeciliaThesisHaptic feedbacks are tactile (cutaneous) and kinesthetic (forces) information, characterized by the unique bidirectional property: the haptic sense is the only one able to provide information of the environment around us, and to sense these interactions. In the last decade, the advent of robotic surgery has brought new challenges in terms of teaching and training, due to different task execution and lack of tactile feedback in the systems with respect to traditional surgery.

Both laparoscopic instrumentations and teleoperation controllers are not able to provide haptic feedback to the surgeon; however, tactile feedback are fundamental in surgery field for tissue characterization and palpation, and for all that tasks involving tissue-tool interaction, where it is necessary to not create damages, or create internal bleeding.

Nevertheless, the absence of this feature in the actual robot teleoperation system is controversial in terms of procedure efficiency and accuracy: visual clues, provided by high definition displays, can efficiently counterbalance the absence of haptic feedback.

However, in terms of motor learning, haptic is the fundamental sense used by human beings to understand and learn the physical world around themselves.

The aim of this master thesis is to understand if the use of haptic feedback in learning a follow-the-trajectory task (which is very common in motor learning) can lead to faster and more complete skill acquisition for tasks that do not normally provide haptic feedback. The study takes into consideration force fields application, focusing on an error augmentation approach.

M.Sc. Candidate Marco Pirotta
Advisor Elena De Momi; Pietro Valdastri
Collaboration Vanderbilt University – STORM Lab
Title Real-time Localization of a Magnetic Surgical Capsule
Description MarcoPirotta'sthesis
Colonoscopy is a screening test used to check for cancer or precancerous growths in the human colon. Since this technique is not well perceived by patients, due to the pain associated with the procedure, new devices based on robotics are being developed to make colonoscopy less painful and encourage people to undergo the test.An external robotic manipulator is used to move an endoscopic capsule inside the patient’s colon, thanks to the magnetic coupling between an external permanent magnet attached to the robot end-effector and an internal permanent magnet onboard of the capsule. To make the test easier from the doctor’s point of view, the direct control of the capsule is preferred to the control of the robot movements, so a closed-loop method, that takes advantage of the knowledge of the capsule pose, is applied to accomplish this purpose. This thesis aims at developing a real-time pose detection of the capsule in the robot workspace, so that the closed-loop control is achieved. The localization method that is studied in this work starts on the base of an already existing algorithm that finds the pose on the base of the magnetic field sensed by the capsule, and develops it to solve the problems of infinite solutions, related to the symmetrical shape of the detected magnetic field, provided by the algorithm. Final step of the work is to prove the proper functionality of the device through experimental testing.
M.Sc. Candidate Andrea Faso
Advisor Elena De Momi; Cristian Luciano (UIC)
Collaboration UIC Bioengineering Department, UIC Department of Urology
Title Haptic and virtual reality surgical simulator for training of percutaneous renal puncture
Description Andrea_Faso_Thesis
Obtaining renal access (PCA) is one of the most challenging task in performing percutaneous renal surgery like nephrolithotomy (PCNL). This procedure is associated with the risk of adverse, or even fatal outcomes; the development of fine surgical skills as well as extensive training is crucial for reducing the incidence of major complications: it is executed by urologists or radiologists, and the main issue is to mentally convert a 2D fluoroscopy image into a 3D reconstruction of the patient’s anatomy to correctly puncture the calix of choice. Most of the competencies obtained by clinicians are due to situational events, nevertheless, the use of simulators can accelerate the learning curve in those individuals desiring to train in this procedure.Consequently, the need for an anatomically accurate and economically accessible preparation outside the operating room has occurred. Several training models have been developed and evaluated in literature, but each one of them presents some drawbacks: current virtual simulators are expensive, ex-vivo animal organs are physically inaccurate and non-biological models are usually degradable,  high-priced and anatomically unreliable. Computer-based, virtual reality platforms offer the possibility to overcome limitations due to learning-by-opportunity and reproduction of human anatomy. The flexibility of the simulator and of its development could easily lead to patient-focused surgery preparation, targeted to a preliminary evaluation of possible drawbacks during the operation.The aim of this research is to develop and evaluate an anatomically accurate, low-cost haptics-based virtual reality surgical simulator for percutaneous renal access. Haptic emulated surgery is a novel training option, serving as a real case simulation platform for training, and preoperative preparation for difficult anatomical situations. Kidney puncture could be accurately simulated by haptic technology, providing the clinician with accurate anatomical orientation and a step-by-step tactile feedback ideal for the acquirement of puncture skills. By being patient specific, our model could provide a reliable source of puncture planning for the inexperienced clinician, offering a reasonably economic application that could be extensively used in the medical field.
M.Sc. Candidate Lorenzo Rapetti
Advisor Elena De Momi; Cristian Luciano (UIC)
Collaboration UIC Bioengineering Department, UIC Department of Urology
Title Augmented Reallity navigation system for prostate biopsy
Description Lorenzo Rapetti_Thesis
The most common procedure for prostate biopsy (12-core transrectal) consist in using a hollow needle that is inserted through the rectum to take 12 samples from different areas of the prostate under the guidance of real-time images provided by a transrectal ultrasound (TRUS). Despite this technique is considered as the “gold standard” of prostate biopsy, the absence of real-time images of tumor leads to sampling errors, unnecessary high number of samples and false negative results, while the presence of the TRUS increase the discomfort for the patient.The aim of this thesis is to implement an innovative solution for the prostate biopsy that, using a virtual reality environment (VRE), can improve the accuracy and safety of the prostate biopsy procedure removing the need of a TRUS. From MRI the anatomy of the prostate and surrounding tissues is reproduced in a 3D model while an electromagnetic tracking system (EMTS) is used to track the 3D position and orientation of the needle in the surgical field. In addiction innovative image techniques such as MR elastography and CEST magnetic resonance can be used to determine the cancer position. Registering the 3D model of tissues and the information regarding the position of the cancerous cells and the needle, a 3D virtual environment is provided to the urologist together with useful information that could help him in the navigation. This system can allows the urologist to perform a perineal targeted prostate biopsy by knowing the relative position of the instrument and the prostate without the need of capturing direct real-time images with the TRUS. What is provided to the surgeon is a 3D environment in which the internal anatomy of the patient is visible and the suspicious areas are highlighted so that a targeted biopsy can be performed.
M.Sc. Candidate Marco Guarnaschelli; Matteo Savazzi
Advisor Elena De Momi
Co-Advisor Sara Moccia
Collaboration Istituto Italiano di Tecnologia
Title Machine learning for early-stage laryngeal cancer classification in endoscopic images
Description sara_msstudentsthesis2017Angiogenesis is commonly recognize as a clear sign of tumor onset. In case of larynx disease, angiogenesis can be observed on the tissue surface even for early stage pathologies. Narrow band endoscopy is nowadays the most adopted imaging modality to diagnose laryngeal tumor because it better contrast the vascular tree with respect to classical white-light endoscopy.

The aim of this master thesis is to develop an algorithm that can distinguish pathological from healthy laryngeal tissues. Machine learning techniques are used to classify images, employing texture descriptors as input features. Specific goals are, first, to find the most informative texture descriptors and, second, to evaluate the performance of different classifiers. The figure (above) shows the workflow of the research: once the image is acquired, we first arrange a dataset of both healthy and pathological patch and, second, we extract texture descriptors from each sample; the last step consists in training the classifier.

M.Sc. Candidate Gabriele Omodeo Vanone
Advisor Elena De Momi
Co-Advisor Veronica Penza, Sara Moccia
Collaboration Istituto Italiano di Tecnologia
Title Laryngeal video stitching
Description Gabriele thesis
Narrow-band endoscopy has recently become the elective technique in the field of laryngeal disease diagnosis. The amount of data encoded in a patient endoscopic video is large and not always informative (e.g. blurred or redundant frames). Moreover, the video review process is time consuming, considering the large amount of examinations daily performed. The aim of this thesis is to automatically generate informative, condensed representation of the endoscopic examination through video stitching of the endoscopic frames (As shown in figure). An optimized strategy for informative frame selection and for image stitching is designed. It features: automatic frame selection based on non-perceptual blur content estimation and redundancy reduction, though frame similarity comparison; image enhancement, through anisotropic filtering; feature-based mosaic composition; vessel pattern analysis and identification of potential symptoms of early stage malignancies. The stitched panorama provides to clinicians a unique view of the explored larynx, which can be employed as reference for future treatment planning and follow-up.
M.Sc. Candidate Francesca Prudente
Advisor Elena De Momi
Co-Advisor Sara Moccia
Collaboration Carnegie Mellon University
Istituto Italiano di Tecnologia
Title Vessel tracking in neurosurgery
Description Francesca MSc thesis

Vestibular schwannoma (VS) is a benign primary intracranial tumor of the vestibulocochlear nerve. The vestibulocochlear nerve is very close to critical structures within the skull, so when the tumor grows can cause debilitating complications. VS resection is among the most technically difficult and tricky microneurosurgical procedures and, in order to decrease the occurrence of cranial nerve deficits, computer-assisted pre-operative planning, intra-operative navigation and robotic-assisted surgery can be employed to support surgeons duringthe surgical procedure. The definition of avoidance areas (e.g. through nerve and blood vessel segmentation) can help the surgeonsin performing safe and accurate surgical gestures. The aim of this project is to develop a hand-held robot able to automatically limit force and velocity byidentifying critical anatomical structures, such as vessels and nerves, in microscopic images (Fig. a). The first step deals with the implementation of an optimal tracking algorithm to segmentand track vessels and nerves during the surgical procedure. Vessels are segmented in the first microscopy video frame through minimal-path algorithm. Minimal-path algorithm found the minimum cost path between two manually defined seed points, according to a specific image-derived anisotropic metric (Fig. b, c). The segmented vessel is then tracked across other frames, exploiting recursive filtering.

M.Sc. Candidate Natalia Costanza Boffa
Advisor Prof. Dr.-Ing. Jessica Burgner-Kahrs, Elena De Momi
Co-Advisor Carolin Fellmann M.Sc.
Collaborations Leibniz universität hannover
Title Optimal Positioning of a Tubular Continuum Robot for Resection of Suprasellar Brain Tumors in Con-sideration of Task Constraints
Description M.ScThesis15-16-2

The main task of this master’s thesis is to develop an algorithm for optimal positioning a tubular continuum robot for performing a specific surgical task. In this master’s thesis, the transnasal resection of a pituitary tumor or other suprasellar brain tumors at the skullbase should be considered as the task.

In a first step, the surgical workspace and the anatomical constraints (e.g. geometry of the deployment passage, tumor location, surgical access etc.) should be modeled by medical image segmentation. Patient image datasets will be provided (at least 3). In the next step, optimal robot design parameters (e.g. tube lengths, curvatures etc.) should be determined for each patient case using existing algorithms. The algorithms should be adapted by adding task constraints, i.e. end-effector motion to remove the tumor tissue after deployment.

Based on this input data (patient anatomical constraints and robot parameters) an algorithm for optimal positioning of the robot base for performing the task of tumor removal should be developed. Criteria for optimization could be deployment with minimum soft tissue con-tact/collisions, avoidance of robot singularities, optimal manipulability of the robot at the tumor.

The optimal positioning algorithm should be evaluated in simulation. If time and resources permit, an evaluation with a real robot and a custom phantom (rapid prototyping from patient data) could be performed.

M.Sc. Candidate Andrea Ghilardi
Advisor Elena De Momi
Co-Advisor Christoph Spuhler
Title Medical image processing for robot assisted surgery
Description  Andea_MScthesis

The ROSA surgical device assists surgeons in cranial and spinal neurosurgery. The device is comprised of a 3D optical tracking device, a robotic arm and state-of-the-art planning and guidance software. The software performs registration of pre, per and post-operative images, allowing the surgeon to superpose anatomical structures, and to plan and execute the surgeries with millimetric accuracy.

The goal of this project is to improve the image processing algorithms for the spine and brain surgery applications, leading to improved ergonomics and a faster workflow.

For more information, visit the Medtech Surgical.

M.Sc. Candidate Stefano De Nigris
Advisor Elena De Momi, Digna M González-Otero (EHU-UPV), Jesús Ruiz(EHU-UPV), Sofía Ruiz de Gauna(EHU-UPV)
Title Application of accelerometer-based chest compression feedback devices in novel scenarios
Collaborations Laboratorio GSC (Grupo Señales y Comunicaciones), Universidad del País Vasco (EHU-UPV), Bilbao, Spain
Description StefanoNigrisThesis

Sudden cardiac arrest is the largest cause of natural death worldwide. Early cardiopulmonary resuscitation (CPR) is key for patient survival. Resuscitation guidelines emphasize the importance of providing high quality chest compressions, that is, with a rate of 100 compressions per minute and a depth of 5cm in adults and one third of the diameter of the chest in children. However, meeting these requirements is difficult, even for well-trained rescuers.

The use of feedback systems can help rescuers increase chest compression quality. Most of these devices measure the acceleration of the chest during compressions, and guide the rescuer towards the target rate and depth. However, these systems present two main limitations. First, they are designed for adult patients, and thus present a target depth of 5cm, which is inadequate for children. Second, they could be inaccurate when used in moving vehicles, such as a train or a plane, as they would register the acceleration of the vehicle along with that of the patient’s chest.

The work tackles those two limitations. It is divided in two main objectives: First, to develop and test an algorithm to measure the diameter of the chest using an accelerometer. This algorithm could be used to adapt feedback systems to be used in pediatric patients. Second, to propose and test an additive model to evaluate the accuracy of accelerometer-based CPR feedback devices in moving vehicles, and to apply it to the case of a plane and train.

The results of this thesis could extend the scenarios of application of feedback systems, allowing their use in pediatric cardiac arrests or in public transportation means.