Multimodality integrated imaging for foetal intervention
Our proposal is centred in Twin-to-twin Transfusion Syndrome (TTTS), a condition that occurs in monochorionic twins where both fetuses are connected to one single placenta. In around one third of cases, a blood flow imbalance will develop that result in the death of both the twins in more than 90% of cases. When TTTS is established, the only option is to separate blood circulation of both fetuses by coagulation the blood vessels connections with a laser. In this context, the surgeon is under a great cognitive stress. Our aim is to give him a “superpower” sight sense that overcomes most of the limitations of the current clinical setting and enables him to deliver better care.
We propose to: (1) integrate multimodal medical imaging techniques such as MRI, US and endoscopic video feed to present the surgeon with an integrated view of the operating field inside the womb, (2) build a 3D map of the target vessels from endoscopic view and their real-time localisation with respect to the anatomy of the mother and the current position of the surgical instruments (3) present this information in a mixed reality solution and (4) test that the proposed solution has the capability to reduce the cognitive load of the surgeon and speed up the intervention.
This will be a great breakthrough that will change forever the way fetal surgeries are performed and will positively impact life conditions for these patients.
Real-time microwave imaging device for endoscopic explorations and interventions
This project is aimed to valorize and translate research on microwave imaging to the clinic and the industry.
The proposed project involves different stages of the technology pipeline, including knowledge/technology protection, pre-prototype design, initial manufacturing and testing. The proposed device will be a small endoscope head composed by several radio frequency (RF) sensors that will allow to form cross-sectional both anatomical and functional images of the interior of the gastro-intestinal tract as the endoscope travels along it.
This project is co-financed by FEDER Funds (2014-2020) from the European Union.
Application of Deep Learning technologies for the diagnosis of lung cancer
This project aims to develop a tool for the diagnosis of lung cancer by implementing an automatic system for the detection of lung nodules, which can study its evolution and growth over time and be integrable with the systems of visualization of the services of radiology. This tool will increase the rate of detection of this type of cancer and improve the productivity of the radiology services. This project is developed in collaboration with Eurecat under the Doctorat Industrial Program by Agaur.
Computational tools for investigating the morphology and blood flow dynamics after left atrial appendage occlusion interventions in atrial fibrillation patients
The main objective of the COMPILAAO project is to develop advanced computational tools to characterize the 3D morphology and investigate the blood flow dynamics after LAAO interventions to improve our knowledge about the pathophysiological mechanisms involving the LAA and how this is modulated by the intervention.
High-resolution image-based computational inner ear modelling for surgical planning of cochlear implantation
Cochlear implantation is a surgical procedure that aims to overcome hearing loss by direct electrical stimulation of the spiral ganglion cells in the cochlea of the inner ear. The surgical scenario of implantation surgery is very complex. It requires high clinical expertise in order to 1) efficiently access the surgical site, the cochlea, localize nearby critical structures (e.g. facial nerve) and 2) optimize the position of the implantable device (electrode array) inside the cochlea.
We hypothesize that a comprehensive understanding of the shape variability of the middle and inner ear among patients will enable the design improvement of hearing implants, and will be of assistance during surgical planning.
The euCanSHare H2020 project will develop the first centralised, secure and sustainable platform for enhanced cross-border data sharing and multi-study personalised medicine research in cardiology. Initially populated with 35 European and Canadian cohorts (corresponding to over one million records), the platform will include extensive functionalities for data deposition, data harmonisation and data analysis. The features of the platform will be demonstrated and adjusted through several use cases, including for investigating diabetic cardiomyopathy, myocardial infarction and stroke using multi-factorial and multi-omics integrative approaches. The data analysis module of the platform will offer tools for cardiac image segmentation, cardiac quantification, and data quality control, as well as bioinformatics and machine learning capabilities.
Surgical planning system for cochlear implants
This project aims at the development of a pre-commercial prototype software for cochlear implantation -- surgical procedure to restore hearing in patients who suffer from medium to severe hearing loss -- to achieve more predictable and controlled interventions. CIPlanner is a surgical planning software for specialists to computationally predict the outcomes of the intervention for a given patient according to a set of implant and surgical parameters. It provides computational results that support pre-operative decisions, such as the optimal implant position, and help hearing professionals to guide the post-interverventional procedure by providing the most favorable setting for the patient.
This project is co-financed by FEDER Funds (2014-2020) from the European Union.
Clinical and virtual examination of patients for holistic and objective description of the osteoarthritis progression mechanisms
The main motivation of this project is to design a predictive and exploratory model that takes into account the 3 main areas implicated in the OA pathology: pain, clinical/morphological patient characteristics and articular defects. Achievement of this goal requires the integration of: (i) cell function models in relation to the physical and mechanical cell environment; (ii) cartilage matrix component quantification models through images obtained with quantitative magnetic resonance; (iii) pain objectification models through clinical, inflammation and articular defects parameters. The expected result is a set of rational relationships between clinical descriptors, biological activity, and physical factors, which calls to coordinate and combine the efforts of clinicians, biologists, and engineers.
Mechanical and biological multiscale modelling of the mechanisms of advanced progression of chronic obstructive pulmonary disease based on clinical evidences
The main motivation of this project is focused in the prediction of the therapeutic responses thanks to computational models of pulmonary inflammation and their integration into clinical practice.
This project aims to create advanced computational models to improve the early detection of COPD. INSIPIRE will develop a model that integrates the different parameters involved in COPD to better characterize the disease and be able to offer personalized treatment to the patient. The description of a model based on medical images highly correlated with clinical trials will allow early diagnosis and enhanced monitoring of the disease, as well as being able to act in its early stages, thus facilitating the evaluation of the effectiveness of treatments in the short term.
Funded by the Spanish Ministry of the Economy and Competitiveness (Challenges-Research programme, FIS2017-89535-C2-2-R), in collaboration with the Barcelona Supercomputing Center-National Center for Supercomputing (BSC-CNS)
Electronic AXONs: wireless microstimulators based on electronic rectification of epidermically applied currents
We propose to explore an innovative method for performing electrical stimulation in which the implanted microstimulators will operate as rectifiers of bursts of innocuous high frequency current supplied through skin electrodes shaped as garments. This approach has the potential to reduce the diameter of the implants to one-fifth the diameter of current microstimulators and, more significantly, to allow that most of the implants’ volume consists of materials whose density and flexibility match those of neighbouring living tissues for minimizing invasiveness.
Antiseptic biopsy system
This project is aimed to transfer a technology able to eliminate the risk of infection after biopsy procedures. It is based on electrically releasing silver ions from a thin silver coating on the biopsy needle so that a long term bactericidal effect is obtained.
Prostate cancer affects one in seven men. Transrectal ultrasound-guided prostate biopsy stands out as the gold-standard for its diagnosis. But this procedure is not innocuous. Since the rectal wall is punctured during the biopsy, bacteria naturally confined within the rectum can produce infectious complications. Nowadays, the only way to prevent these harmful infections is by administering antibiotics. However, the increasing resistance of bacterial to antibiotics results in a rise in these complications. Currently it is reported between 2% and 5% of the patients undergoing a transrectal prostate biopsy develop a severe infection.
This project was co-financed in the past by FEDER Funds (2014-2020) from the European Union, and is currently funded by a CaixaImpulse grant.
CardioFunXion provides a platform for industrial-clinical-academic collaboration and will be an example of future partnerships in more efficient and auditable clinical image interpretation tools. All partners bring specific expertise: either they produce workstations (PHILIPS), use them (IDIBAPS, CHUC) or contribute to algorithms to be incorporated into them (UPF). By organising and accelerating exchange of knowledge, experience and tools between the different actors, CardioFunXion will consolidate sustainable partnerships.
The project will give early-stage researchers (ESRs) the opportunity to be exceptionally well-placed to become key future contributors to the development of advanced imaging techniques for the assessment of cardiovascular disorders, equipping them with multidisciplinary state-of-the-art skills plus awareness and experience in commercial developmen
Computer model derived indices for optimal patient-specific treatment selection and planning in Heart Failure
The primary aim of VP2HF is to bring together image and data processing tools with statistical and integrated biophysical models mainly developed in previous VPH projects, into a single clinical workflow to improve therapy selection and treatment optimisation in HF. The tools will be tested and validated in 200 patients (including 50 historical datasets) across 3 clinical sites, including a prospective clinical study in 50 patients in the last year of the project