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Bioresorbable Vascular Scaffolds (BVS), developed to allow drug deliver and mechanical support, followed by complete resorption, have revolutionized atherosclerosis treatment. InSilc is a Cloud platform for in silico clinical trials (ISCT) used in the design, development and evaluation pipeline of stents. The platform integrates beyond the state-of-the-art multi-disciplinary and multiscale models, which predict the scaffold's performance in the short/acute and medium/long term. In this study, a use case scenario of two Bioabsorbable Vascular Stents (BVSs) implanted in the same arterial anatomy is presented, allowing the whole InSilc in silico pipeline to be applied and predict how the different aspects of this intervention affect the success of stenting process.

Prostate cancer (PCa) is one of the most prevalent cancers in the male population. Current clinical practices lead to overdiagnosis and overtreatment necessitating more effective tools for improving diagnosis, thus the quality of life of patients. Recent advances in infrastructure, computing power and artificial intelligence enable the collection of tremendous amounts of clinical and imaging data that could assist towards this end. ProCAncer-I project aims to develop an AI platform integrating imaging data and models and hosting the largest collection of PCa (mp)MRI, anonymized image data worldwide. In this paper, we present an overview of the overall architecture focusing on the data ingestion part of the platform. We describe the workflow followed for uploading the data and the main repositories for storing imaging data, clinical data and their corresponding metadata.

Αντικείμενο της εργασίας είναι η αεροδυναμική και αεροακουστική ανάλυση στροφείων ανεμοκινητήρων οριζοντίου άξονα (α-ο-α). Ο υπολογισμός του πεδίου ροής και των αεροδυναμικών συντελεστών του στροφείου ενός ανεμοκινητήρα επιτυγχάνεται κατά δύο τρόπους, με σκοπό την άμεση σύγκριση των αποτελεσμάτων με κριτήρια αφενός την ακρίβεια και αφετέρου την ευκολία ή πρακτικότητα που προσδιορίζεται κύρια σε όρους χρόνου υπολογισμού και διαθεσιμότητας υπολογιστικών πόρων. Οι δύο επιλεγμένοι τρόποι που διαφοροποιούνται στην φυσικο-μαθηματική μοντελοποίηση του προβλήματος ροής γύρω από το στροφείο του ανεμοκινητήρα, αποτελούν δύο δοκιμασμένες μεθοδολογίες ή τεχνικές ανάλυσης και σχεδιασμού περιστρεφόμενων στροφείων, τα οποία μπορούν να λειτουργούν ως κινητήριες μηχανές ή ως εργομηχανές, είναι η μέθοδος των επιφανειακών στοιχείων και η αριθμητική επίλυση των εξισώσεων Navier-Stokes. Για την αξιολόγηση των υπολογιστικών αποτελεσμάτων επιλέχθηκε ως στροφείο αναφοράς, ο πειραματικός ανεμοκινητήρας NREL phase II. Ο αλγόριθμος των επιφανειακών στοιχείων συμπλέχτηκε με ολοκληρωτικά σχήματα πρόλεξης και υπολογισμού του οριακού στρώματος με σκοπό να συμπεριληφθούν τα φαινόμενα συνεκτικότητας της ροής. Πραγματοποιήθηκε παραμετρική ανάλυση του δρομέα του ανεμοκινητήρα για διαφορετικές συνθήκες λειτουργίας του. Η σύγκριση των αποτελεσμάτων των συντελεστών πίεσης των περιστρεφόμενων πτερυγίων για τέσσερις θέσεις κατά το εκπέτασμα του πτερυγίου με τα πειραματικά δεδομένα δείχνει ικανοποιητική συμφωνία. Για την ανάλυση του πεδίου ροής που παράγεται γύρω από περιστρεφόμενους δρομείς α-ο-α χρησιμοποιήθηκε η μέθοδος της υπολογιστικής ρευστοδυναμικής (CFD). Πραγματοποιήθηκαν RANS προσομοιώσεις για διαφορετικές συνθήκες λειτουργίας του ανεμοκινητήρα και για τέσσερα διαφορετικά μοντέλα τύρβης. Το k-ω SST μοντέλο τύρβης έχει τις μικρότερες αποκλίσεις με τα πειραματικά αποτελέσματα. Η αεροακουστική ανάλυση του στροφείου ενός ανεμοκινητήρα επιτυγχάνεται με την επίλυση της ακουστικής εξίσωσης Ffowcs-Williams Hawkings, μέσω ενός υπολογιστικού κώδικα που αναπτύχθηκε γι’ αυτό το σκοπό. Από τα αποτελέσματα των προσομοιώσεων, φάνηκε στα ροδογράμματα κατευθυντικότητας του ήχου, τα επίπεδα της ακουστικής πίεσης να είναι υψηλότερα για θέσεις παρατηρητή ανάντη και κατάντη του ανεμοκινητήρα.

Coronary artery disease (CAD) remains the leading cause of death in Europe and worldwide. One of the most common pathologic processes involved in CAD is atherosclerosis. Coronary stents are expandable scaffolds that are used to widen the occluded arteries and enable the blood flow restoration. To achieve an adequate delivery and placement of coronary stents different parameters play a significant role. Due to the strain that the stents are exposed to and the forces they should withstand, the stent design is dominant. This study focuses on investigating the effect of the stent design in two finite element models using two stents with difference in the strut thickness. The in silico deployment is performed in a reconstructed patient specific arterial segment. The results are analyzed in terms of stress in the stent and the arterial wall and demonstrate how stent expansion is extensively affected by the scaffold's design.

Coronary artery disease (CAD) is the leading cause of mortality in Europe and worldwide. Atherosclerosis is the most common pathologic process that is highly related with CAD, while the implantation of drug-eluting Bioresorbable Vascular Scaffolds (BVS) is the most widely performed procedure for treating patients with CAD. InSilc is an in silico clinical trial (ISCT) platform for the development and assessment of drug-eluting BVS. InSilc platform provides insight on the performance of drug-eluting BVS in their short term and medium/long term through the Mechanical Modelling Module, the Deployment Module, the Fluid Dynamics Module, the Myocardial Perfusion Module, the Drug-delivery Module and the Degradation Module. In order for the aforementioned modules to be developed, the utilization of the reconstructed patient specific arterial segment and the BVS design are required, which is achieved through the 3D reconstruction and plaque characterization tool. In this study, the overall architecture of the InSilc platform is presented with special emphasis on the 3D reconstruction and plaque characterization tool. The tool will be able to implement different medical image processing workflows. The workflows will require minimum user intervention in order to be used in large scale clinical trials.

In this work, the compatibility of four commercially available organic phase change materials, with melting points in the temperature range 44–58 _C and with engineering polymers high density polyethylene (HDPE) and polypropylene (PP), is investigated. These polymers are used for the design and manufacture of hot and cold thermal energy storage tanks or encapsulation media. The study involves interaction of polymer specimens with the four different phase change materials for a period of time up to 40 days under high temperature. The mass change, mechanical strength and properties of the polymers were tested. The wt% uptake reached 6.4 wt% for PP and 5.8 wt% for HDPE. The strength of HDPE is immediately decreased by Day 7 but at a significant level restored after Day 28. No such effect was found for PP. The surface wetting as well as thermal properties measured (DSC) on the specimens provided an insight on the interaction of the absorbed phase change materials with the polymer. An in depth distribution over time was observed with significant decrease in the mechanical strength of the polymers. An epoxybased resin was also evaluated under the same conditions and is suggested as a protective coating.

This work investigates melting and solidification processes of four different Phase Change Materials (PCM) used as latent heat thermal storage system. The experimental rig was consisted of an insulated tank, filled with the under investigation PCM, a staggered heat exchanger to supply or extract heat from the PCM cavity and a water pump to circulate Heat Transfer Fluid (HTF). Both charging (melting) and discharging (solidification) processes were conducted for two different HTF flow rates. The main scope of this work was to develop a first approach and to investigate the behaviour of PCM under various load conditions (different HTF flow rates). Results show that different HTF flow rates affect melting and solidification time periods; in both processes time was reduced while HTF flow rate was increased but in different ways due to the transition from conduction to convection heat transfer mechanisms.

This work reports the operation of a Latent Heat Thermal Energy Storage system (LHTES) utilizing a staggered heat exchanger (HE) and using various organic Phase Change Materials (PCMs). In a LHTES test rig set measurements regarding energy storage and release were performed in the working temperature range of each Phase Change Material. Nominal melting temperatures of the PCMs used were 40–53 °C. Computational Fluid Dynamics (CFD) simulation was applied to follow the operation of the test rig. The test rig consisted of a compact insulated tank, filled with PCM, a staggered heat exchanger to supply or extract thermal energy by the PCM and a water pump to circulate water as a Heat Transfer Fluid (HTF). Different HTF flow rates affect charging (melting) and discharging (solidification) processes but more significant was the effect of heat transfer mechanisms occurring. The latter was confirmed by inserting buoyancy currents created due to convection in a CFD simulation program where melting time was reduced compared to the same conditions with only conduction occurring. The suggested LHTES configuration is a promising compact unit despite the PCMs thermal resistance and solidification hysteresis phenomena, as well as the heat transfer mechanism strongly affecting the energy storage process.

Coronary artery disease (CAD) is the leading cause of mortality in Europe and worldwide. Atherosclerosis is the most common pathologic process that is highly related with CAD, while the implantation of drug-eluting Bioresorbable Vascular Scaffolds (BVS) is the most widely performed procedure for treating patients with CAD. InSilc is an in silico clinical trial (ISCT) platform for the development and assessment of drug-eluting BVS. InSilc platform provides insight on the performance of drug-eluting BVS in their short term and medium/long term through the Mechanical Modelling Module, the Deployment Module, the Fluid Dynamics Module, the Myocardial Perfusion Module, the Drug-delivery Module and the Degradation Module. In order for the aforementioned modules to be developed, the utilization of the reconstructed patient specific arterial segment and the BVS design are required, which is achieved through the 3D reconstruction and plaque characterization tool. In this study, the overall architecture of the InSilc platform is presented with special emphasis on the 3D reconstruction and plaque characterization tool. The tool will be able to implement different medical image processing workflows. The workflows will require minimum user intervention in order to be used in large scale clinical trials., "© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."

Coronary artery disease (CAD) is the leading cause of mortality in Europe and worldwide. Atherosclerosis is the most common pathologic process that is highly related with CAD, while the implantation of drug-eluting Bioresorbable Vascular Scaffolds (BVS) is the most widely performed procedure for treating patients with CAD. InSilc is an in silico clinical trial (ISCT) platform for the development and assessment of drug-eluting BVS. InSilc platform provides insight on the performance of drug-eluting BVS in their short term and medium/long term through the Mechanical Modelling Module, the Deployment Module, the Fluid Dynamics Module, the Myocardial Perfusion Module, the Drug-delivery Module and the Degradation Module. In order for the aforementioned modules to be developed, the utilization of the reconstructed patient specific arterial segment and the BVS design are required, which is achieved through the 3D reconstruction and plaque characterization tool. In this study, the overall architecture of the InSilc platform is presented with special emphasis on the 3D reconstruction and plaque characterization tool. The tool will be able to implement different medical image processing workflows. The workflows will require minimum user intervention in order to be used in large scale clinical trials.

Automatic segmentation of the prostate peripheral zone on Magnetic Resonance Images (MRI) is a necessary but challenging step for accurate prostate cancer diagnosis. Deep learning (DL) based methods, such as U-Net, have recently been developed to segment the prostate and its’ sub-regions. Nevertheless, the presence of class imbalance in the image labels, where the background pixels dominate over the region to be segmented, may severely hamper the segmentation performance. In the present work, we propose a DL-based preprocessing pipeline for segmenting the peripheral zone of the prostate by cropping unnecessary information without making a priori assumptions regarding the location of the region of interest. The effect of DL-cropping for improving the segmentation performance was compared to the standard center-cropping using three state-of-the-art DL networks, namely U-net, Bridged U-net and Dense U-net. The proposed method achieved an improvement of 24%, 12% and 15% for the U-net, Bridged U-net and Dense U-net, respectively, in terms of Dice score.

Prostate cancer (PCa) is one of the most prevalent cancers in the male population. Current clinical practices lead to overdiagnosis and overtreatment necessitating more effective tools for improving diagnosis, thus the quality of life of patients. Recent advances in infrastructure, computing power and artificial intelligence enable the collection of tremendous amounts of clinical and imaging data that could assist towards this end. ProCAncer-I project aims to develop an AI platform integrating imaging data and models and hosting the largest collection of PCa (mp)MRI, anonymized image data worldwide. In this paper, we present an overview of the overall architecture focusing on the data ingestion part of the platform. We describe the workflow followed for uploading the data and the main repositories for storing imaging data, clinical data and their corresponding metadata.

INTRODUCTION: Although accurate segmentation of the prostatic subregions is a crucial step for prostate cancer diagnosis, it remains a challenge. OBJECTIVES: To propose a deep learning (DL)-based cropping pipeline to improve the performance of DL networks for segmenting the prostate’s peripheral zone. METHODS: A U-net network was trained to crop the area around the peripheral zone on MRI in order to reduce the class imbalance between foreground and background pixels. The DL-cropping was compared with the standard center-cropping using three segmentation networks. RESULTS: The DL-cropping improved significantly the segmentation performance in terms of Dice score, Sensitivity, Hausdorff Distance, and Average Surface Distance, for all three networks. The improvement in Dice Score was 34%, 13% and 16% for the U-net, Dense U-net and Bridged U-net, respectively. CONCLUSION: For all the evaluated networks, the proposed DL-cropping technique outperformed the standard center-cropping.