|M.Sc. candidate||Anna Morelli|
|Thesis||Intra-operative deformable registration for Augmented Reality in nephrectomy|
|Supervisors||Elena De Momi, Sara Moccia|
|Description||Renal cell cancer is a kind of kidney tumor that affects 14,068,000 patients all over the world. The treatment for this disease is nephrectomy, a surgical procedure in which the entire kidney, or only a part of it, is removed. Nowadays the procedure can be performed in minimally invasive surgery, allowing reduction of bleeding, of pain and of recovery time; the drawbacks of this technique is, for the surgeon, the impairment of haptic feedback for the discrimination of different structures. To overcome this limitation, Augmented Reality (AR) systems have been proposed. In an AR operating environment the pre-operative kidney model, usually extracted from CT or MRI, is superimposed onto the surgical field of view. Potential clinical advantages are offered in two stages: 1) in the identificiation of important structures, such as vessels, tumor, healthy tissues, in the initial phase; 2) during tumor resection, fixing negative surgical margins for the instruments.
Focusing on the first advantage, the superimposition during the first phase can be obtained starting from a landmark-based initial alignment of the model to the intra-operative scene. The intra-operative scene is recorded with a stereo-camera to obtain the intra-operative 3D reconstruction, i.e. the intra-operative point cloud. Then, with a deformable registration algorithm, it is possible to find the transformation to map the pre-operative model into the intra-operative point cloud.
The aims of this project are: i) implementing an algorithm for the initial registration based on manual identified landmarks; ii) implementing a deformable registration algorithm to robustly deal with the intra-operative deformation of the kidney.
|M.Sc. candidate||Michele Gazzara|
|Thesis||Convolutional Neural Networks models for axon segmentation in EM images.|
|Supervisors||Elena De Momi, Marco Vidotto, Sara Moccia|
|Description||Convection Enhanced Delivery (CED) is a minimally invasive approach for the treatment of the brain tumors, such as Glioblastomas. In CED, drugs are injected applying a positive gradient pressure through cannula of small dimensions directly in the tumorous zone, circumventing the brain-blood-barrier. Infusion parameters (flow rate and duration) and tissues structure strongly affect the transport of drugs inside the brain and often bring to suboptimal results of CED.
A way to overcome the previous limitations is to implement a fluid dynamic model of the human brain, considering its porosity, anisotropy and diffusivity. The extraction of such properties requires the brain to be imaged at the micro-scale level to enhance the neurons distribution within the cerebral tissues. For this reason, brain samples are analysed through electronic microscopy (EM), a really high resolution imaging technique. This thesis aims to provide a method for the axon segmentation in brain EM images. Old school segmentation approaches, i.e. manual and semi-automatic segmentation, require a lot of time and are often inaccurate, for this reason an algorithm for the automatic segmentation is proposed.
The automatic segmentation algorithm will exploit deep learning models and, so, two different architectures of Convolutional Neural Networks (CNN) will be performed:
Reference: Ronneberger et al., U-Net: Convolutional Networks for Biomedical Image Segmentation (2015)
|M.Sc. Candidate||Leonardo Cerri|
|Advisor||Elena De Momi
|Title||Path planning with obstacle avoidance in 2D and 3D space for a steerable catheter|
Currently, non-invasive techniques do not exist in neurosurgery and oncological treatments of the brain still make use of rigid cannulas as instruments both for diagnosis and therapy (drug delivery). A less invasive procedure can be carried out by using flexible steerable needles especially when a straight path of the probe is not possible or enough safe and by trying to avoid “no-go” areas, such as important vessels, exploiting tissue anisotropy to maximize the therapeutic effectiveness.
The aim of this master thesis is focused on the development of a path planning algorithm for the pre-operative phase which provides to the surgeon a 3D feasible path for a steerable controlled catheter both from the point of view of its mechanical constraints (radius of curvature) and of the overall safety (distance from the obstacles). This work is a part of the European project EDEN2020 (http://www.eden2020.eu/) whose aim is to develop the golden standard for both diagnosis and treatment in neurosurgery.
By studying the Rapidly Exploring Random Tree (RRT) strategy in 2D, some variants of the basic paradigm have been developed. They led to the implementation of a suitable Bidirectional RRT* algorithm where the random generation of points starts both from the initial and the final positions. The next steps involve the conversion of such algorithm to a 3D space and the implementation of proper 3D Slicer module.
Later, the effort will be focused on the interpolation of the randomly generated points with a proper strategy (involving NURBS curves) and on the study of the non-holonomic behaviour of the catheter. The latter will provide a quantitative evaluation of the actual mechanical constraints to be fit and finally the research for the optimal solution (also in terms of robustness) will be implemented.
|M.Sc. Candidate||Andrea Panaccio|
|Advisor||Elena De Momi; Marc Garbey|
|Collaboration||Computational Surgery Department, Houston Methodist Research Institute|
|Title||Neuro-Ergonomy and Computational Model of Minimally Invasive Surgery Training|
Neuroergonomics addresses ergonomic concerns with an emphasis on the role of the human nervous system. It combines two disciplines: neuroscience, the study of brain function, and human factors, the study of how to match technology with the capabilities and limitations of people so they can work effectively and safely. One of the aims of neuro-ergonomy is to provide new training methods that enhance performance, expand capabilities, and optimize the fit between people and technology.
In the surgical field the implementation of laparoscopic surgery has had a significant impact on surgical outcomes, mainly by increasing the speed of postoperative recovery and subsequent discharge from the hospital. As a minimally invasive procedure, however, it does require a great deal of surgical skill.
The surgeons must, therefore, undergo skills and procedure-specific training to
obtain the level of proficiency that allows them to safely operate. To achieve this, “the Fundamentals of Laparoscopic Surgery” (FLS) have been developed.
These encompass several deconstructed surgical tasks which need to be completed within the allotted time span and without any mistakes for the trainee to be considered proficient. Time and quality of the completed product are therefore measures of proficiency.
In order to identify other possible measures of proficiency, a standalone mobile work station has been developed in the computational surgery department of HMRI. It can be brought to the user in order to test his/her technical skills and response to external stress in the context of laparoscopy. It encompasses several systems with which it can synchronously monitor:
The aim of this work is to develop a multimodal analysis and an efficient signal analysis chain to link minimally invasive monitoring with performance indicators and competence level.
To do that, data must be collected from a subset of surgeon which comprises different categories (e.g. Expert, Resident). The aim of the analysis of these data will be not only to evaluate the performance of the surgeon but also to see if it is possible to distinguish among the different categories (due to the different skills level) and measuring the ongoing progress of the trainee throughout their practice sessions.
|M.Sc. Candidate||Antonio Gallarello|
|Advisor||Elena De Momi; Helge Wurdemann|
|Collaboration||Mechanical Engineering Lab, University College London|
|Title||Patient Specific 3D printed test bed for Transcatheter Aortic Valve Implantation procedure|
Aortic stenosis (AoS) is a disorder characterized by the reduced cardiac output from the left ventricle (LV) due to a narrowing of the aortic valve. The causes of this diseases are both age-related calcifications and bicuspid valve. The most important aspect of this disease is that is associated with significant comorbidities in more than one-third of cases. The disease is similar to atherosclerosis; progressive deposition and valvular thickening results in the obstruction of the LV outflow tract. This leads to a LV hypertrophy, a reduced ventricular compliance and a diastolic dysfunction. The onset of symptoms of sever AoS is the starting point of a rapid decline with a mortality rate in the first year around 50% (8).
The surgical aortic valve replacement (AVR) is a procedure that is currently used, even for elderly patients, as long as they are suitable for that. Indeed, the European Society of Cardiology recommend TAVI (Transcatheter Aortic Valve Implantation) only for people who are unsuitable for the surgical procedure due to many comorbidities. Moreover, conventional AVR might be very challenging or prohibitive in patients with mediastinal fibrosis or adhesions following radiotherapy or previous surgery.
In 2002, the first TAVI procedure was performed (22). Since then, its rate has risen greatly with more than 50000 having been performed worldwide (23). This procedure allows the treatment of patients that cannot undergo surgical valve replacement. Moreover, it has better outcomes in terms of functional capacity and also the hospitalization have reduced.
The first TAVI was performed using a venous access (right femoral vein), with a trans septal puncture to reach the left ventricle . This high-risk procedure was soon replaced by the currently used which exploits an arterial insertion point (femoral or subclavian artery), with a retrograde approach. The use of peripheral vessels requires a favourable anatomy. In presence of small calibre, heavily calcified ort tortuous vessels some problems may arise. Another technique reckons on a direct cardiac access through the apex of the heart with a mini thoracotomy. This is more invasive than the ones previously explained.
The aim of this work is to develop a 3D printable, patient-specific, vascular phantom with mechanical properties similar to the human tissue so as to be used as a test bench for the developing of a new technology of TAVI catheter. Different steps are required: Image segmentation, Model refining, material and thickness evaluation and 3D printing. The overall device should be a made of different modular parts, independently printed but linkable.
|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|
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||Magnetic 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||Haptic 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|
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|
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|
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|
|Collaboration||Istituto Italiano di Tecnologia|
|Title||Machine learning for early-stage laryngeal cancer classification in endoscopic images|
|Description||Angiogenesis 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|
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|
|Collaboration||Carnegie Mellon University
Istituto Italiano di Tecnologia
|Title||Vessel tracking in neurosurgery|
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|
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|
|Title||Medical image processing for robot assisted surgery|
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|
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.