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Alamir SH, Tufaro V, Trilli M, Kitslaar P, Mathur A, Baumbach A, Jacob J, Bourantas CV, Torii R. Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning. Front Bioeng Biotechnol 2024; 12:1360330. [PMID: 39188371 PMCID: PMC11345599 DOI: 10.3389/fbioe.2024.1360330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 07/12/2024] [Indexed: 08/28/2024] Open
Abstract
There is increasing evidence that coronary artery wall shear stress (WSS) measurement provides useful prognostic information that allows prediction of adverse cardiovascular events. Computational Fluid Dynamics (CFD) has been extensively used in research to measure vessel physiology and examine the role of the local haemodynamic forces on the evolution of atherosclerosis. Nonetheless, CFD modelling remains computationally expensive and time-consuming, making its direct use in clinical practice inconvenient. A number of studies have investigated the use of deep learning (DL) approaches for fast WSS prediction. However, in these reports, patient data were limited and most of them used synthetic data generation methods for developing the training set. In this paper, we implement 2 approaches for synthetic data generation and combine their output with real patient data in order to train a DL model with a U-net architecture for prediction of WSS in the coronary arteries. The model achieved 6.03% Normalised Mean Absolute Error (NMAE) with inference taking only 0.35 s; making this solution time-efficient and clinically relevant.
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Affiliation(s)
- Salwa Husam Alamir
- Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Vincenzo Tufaro
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Matilde Trilli
- Department of Mechanical Engineering, University College London, London, United Kingdom
| | | | - Anthony Mathur
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Andreas Baumbach
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Joseph Jacob
- Satsuma Lab, Centre for Medical Image Computing, University College London, London, United Kingdom
- UCL Respiratory, University College London, London, United Kingdom
| | - Christos V. Bourantas
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, United Kingdom
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2
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Villegas-Martinez M, de Villedon de Naide V, Muthurangu V, Bustin A. The beating heart: artificial intelligence for cardiovascular application in the clinic. MAGMA (NEW YORK, N.Y.) 2024; 37:369-382. [PMID: 38907767 DOI: 10.1007/s10334-024-01180-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/25/2024] [Accepted: 06/13/2024] [Indexed: 06/24/2024]
Abstract
Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.
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Affiliation(s)
- Manuel Villegas-Martinez
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Hôpital Xavier Arnozan, Université de Bordeaux-INSERM U1045, Avenue du Haut Lévêque, 33604, Pessac, France
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604, Pessac, France
| | - Victor de Villedon de Naide
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Hôpital Xavier Arnozan, Université de Bordeaux-INSERM U1045, Avenue du Haut Lévêque, 33604, Pessac, France
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604, Pessac, France
| | - Vivek Muthurangu
- Center for Cardiovascular Imaging, UCL Institute of Cardiovascular Science, University College London, London, WC1N 1EH, UK
| | - Aurélien Bustin
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Hôpital Xavier Arnozan, Université de Bordeaux-INSERM U1045, Avenue du Haut Lévêque, 33604, Pessac, France.
- Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604, Pessac, France.
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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3
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Yao T, Pajaziti E, Quail M, Schievano S, Steeden J, Muthurangu V. Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data. PLoS Comput Biol 2024; 20:e1012231. [PMID: 38900817 PMCID: PMC11218942 DOI: 10.1371/journal.pcbi.1012231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 07/02/2024] [Accepted: 06/06/2024] [Indexed: 06/22/2024] Open
Abstract
Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields. This proof-of-concept study used 135 3D cardiac MRIs from both a public and private dataset. The pulmonary arteries in the MRIs were manually segmented and converted into volume-meshes. CFD simulations were performed on ground truth meshes and interpolated onto point-point correspondent meshes to create the ground truth dataset. The dataset was split 110/10/15 for training, validation, and testing. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values, taking a specific inlet velocity as an additional input. Image2Flow was evaluated in terms of segmentation, and the accuracy of predicted CFD was assessed using node-wise comparisons. In addition, the ability of Image2Flow to respond to increasing inlet velocities was also evaluated. Image2Flow achieved excellent segmentation accuracy with a median Dice score of 0.91 (IQR: 0.86-0.92). The median node-wise normalized absolute error for pressure and velocity magnitude was 11.75% (IQR: 9.60-15.30%) and 9.90% (IQR: 8.47-11.90), respectively. Image2Flow also showed an expected response to increased inlet velocities with increasing pressure and velocity values. This proof-of-concept study has shown that it is possible to simultaneously perform patient-specific volume-mesh based segmentation and pressure and flow field estimation using Image2Flow. Image2Flow completes segmentation and CFD in ~330ms, which is ~5000 times faster than manual methods, making it more feasible in a clinical environment.
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Affiliation(s)
- Tina Yao
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Endrit Pajaziti
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Michael Quail
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Silvia Schievano
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Jennifer Steeden
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Vivek Muthurangu
- Institute of Cardiovascular Science, University College London, London, United Kingdom
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Sachdeva R, Armstrong AK, Arnaout R, Grosse-Wortmann L, Han BK, Mertens L, Moore RA, Olivieri LJ, Parthiban A, Powell AJ. Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement. J Am Coll Cardiol 2024; 83:63-81. [PMID: 38171712 PMCID: PMC10947556 DOI: 10.1016/j.jacc.2023.10.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/05/2023] [Accepted: 10/13/2023] [Indexed: 01/05/2024]
Abstract
Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation.
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Affiliation(s)
- Ritu Sachdeva
- Department of Pediatrics, Division of Pediatric Cardiology, Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, Georgia, USA.
| | - Aimee K Armstrong
- The Heart Center, Nationwide Children's Hospital, Department of Pediatrics, Division of Cardiology, Ohio State University, Columbus, Ohio, USA
| | - Rima Arnaout
- Division of Cardiology, Department of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Lars Grosse-Wortmann
- Division of Cardiology, Department of Pediatrics, Oregon Health and Science University, Portland, Oregon, USA
| | - B Kelly Han
- Division of Cardiology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Luc Mertens
- Division of Cardiology, Department of Pediatrics, University of Toronto and The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Ryan A Moore
- The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Laura J Olivieri
- Division of Cardiology, Department of Pediatrics, Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Anitha Parthiban
- Department of Cardiology, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA
| | - Andrew J Powell
- Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
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Verstraeten S, Hoeijmakers M, Tonino P, Brüning J, Capelli C, van de Vosse F, Huberts W. Generation of synthetic aortic valve stenosis geometries for in silico trials. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024; 40:e3778. [PMID: 37961993 DOI: 10.1002/cnm.3778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 09/01/2023] [Accepted: 09/17/2023] [Indexed: 11/15/2023]
Abstract
In silico trials are a promising way to increase the efficiency of the development, and the time to market of cardiovascular implantable devices. The development of transcatheter aortic valve implantation (TAVI) devices, could benefit from in silico trials to overcome frequently occurring complications such as paravalvular leakage and conduction problems. To be able to perform in silico TAVI trials virtual cohorts of TAVI patients are required. In a virtual cohort, individual patients are represented by computer models that usually require patient-specific aortic valve geometries. This study aimed to develop a virtual cohort generator that generates anatomically plausible, synthetic aortic valve stenosis geometries for in silico TAVI trials and allows for the selection of specific anatomical features that influence the occurrence of complications. To build the generator, a combination of non-parametrical statistical shape modeling and sampling from a copula distribution was used. The developed virtual cohort generator successfully generated synthetic aortic valve stenosis geometries that are comparable with a real cohort, and therefore, are considered as being anatomically plausible. Furthermore, we were able to select specific anatomical features with a sensitivity of around 90%. The virtual cohort generator has the potential to be used by TAVI manufacturers to test their devices. Future work will involve including calcifications to the synthetic geometries, and applying high-fidelity fluid-structure-interaction models to perform in silico trials.
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Affiliation(s)
- Sabine Verstraeten
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Pim Tonino
- Department of Cardiology, Catharina Hospital, Eindhoven, The Netherlands
| | - Jan Brüning
- Institute of Computer-assisted Cardiovascular Medicine, Charite Universitaetsmedizin, Berlin, Germany
| | - Claudio Capelli
- Institute of Cardiovascular Science, University College London, London, UK
| | - Frans van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Wouter Huberts
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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Morgan B, Murali AR, Preston G, Sima YA, Marcelo Chamorro LA, Bourantas C, Torii R, Mathur A, Baumbach A, Jacob MC, Karabasov S, Krams R. A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries. Front Cardiovasc Med 2023; 10:1221541. [PMID: 37840962 PMCID: PMC10570504 DOI: 10.3389/fcvm.2023.1221541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/11/2023] [Indexed: 10/17/2023] Open
Abstract
With the global rise of cardiovascular disease including atherosclerosis, there is a high demand for accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1,407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding (t -SNE) algorithm. The reduced dataset for t -SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on "unseen" meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 s.
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Affiliation(s)
- Benjamin Morgan
- Department of Science and Engineering, Queen Mary University of London, London, United Kingdom
| | - Amal Roy Murali
- Laboratoire de Mécanique des Fluides et d’Acoustique UMR5509, INSA Lyon, Ecole Centrale de Lyon, University of Lyon, Ecully, France
| | - George Preston
- Department of Science and Engineering, Queen Mary University of London, London, United Kingdom
| | - Yidnekachew Ayele Sima
- Department of Science and Engineering, Queen Mary University of London, London, United Kingdom
| | | | | | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, United Kingdom
| | | | | | - Marc C. Jacob
- Laboratoire de Mécanique des Fluides et d’Acoustique UMR5509, INSA Lyon, Ecole Centrale de Lyon, University of Lyon, Ecully, France
| | - Sergey Karabasov
- Department of Science and Engineering, Queen Mary University of London, London, United Kingdom
| | - Rob Krams
- Department of Science and Engineering, Queen Mary University of London, London, United Kingdom
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Chatpattanasiri C, Franzetti G, Bonfanti M, Diaz-Zuccarini V, Balabani S. Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics. J Biomech 2023; 158:111759. [PMID: 37657234 PMCID: PMC7615718 DOI: 10.1016/j.jbiomech.2023.111759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/19/2023] [Accepted: 08/07/2023] [Indexed: 09/03/2023]
Abstract
Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental haemodynamic models. In this work, we focus on the use of Reduced Order Models (ROMs) to reconstruct velocity fields in a patient-specific dissected aorta, with the objective being to compare the ROMs obtained from Robust Proper Orthogonal Decomposition (RPOD) to those obtained from the traditional Proper Orthogonal Decomposition (POD). POD and RPOD are applied to in vitro, haemodynamic data acquired by Particle Image Velocimetry and compare the decomposed flows to those derived from Computational Fluid Dynamics (CFD) data for the same geometry and flow conditions. In this work, PIV and CFD results act as surrogates for clinical haemodynamic data e.g. MR, helping to demonstrate the potential use of ROMS in real clinical scenarios. The flow is reconstructed using different numbers of POD modes and the flow features obtained throughout the cardiac cycle are compared to the original Full Order Models (FOMs). Robust Principal Component Analysis (RPCA), the first step of RPOD, has been found to enhance the quality of PIV data, allowing POD to capture most of the kinetic energy of the flow in just two modes similar to the numerical data that are free from measurement noise. The reconstruction errors differ along the cardiac cycle with diastolic flows requiring more modes for accurate reconstruction. In general, modes 1-10 are found sufficient to represent the flow field. The results demonstrate that the coherent structures that characterise this aortic dissection flow are described by the first few POD modes suggesting that it is possible to represent the macroscale behaviour of aortic flow in a low-dimensional space; thus significantly simplifying the problem, and allowing for more computationally efficient flow simulations or machine learning based flow predictions that can pave the way for translation of such models to the clinic.
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Affiliation(s)
| | - Gaia Franzetti
- Department of Mechanical Engineering, University College London, London, UK
| | - Mirko Bonfanti
- Department of Mechanical Engineering, University College London, London, UK
| | - Vanessa Diaz-Zuccarini
- Department of Mechanical Engineering, University College London, London, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Stavroula Balabani
- Department of Mechanical Engineering, University College London, London, UK.
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Stokes C, Ahmed D, Lind N, Haupt F, Becker D, Hamilton J, Muthurangu V, von Tengg-Kobligk H, Papadakis G, Balabani S, Díaz-Zuccarini V. Aneurysmal growth in type-B aortic dissection: assessing the impact of patient-specific inlet conditions on key haemodynamic indices. J R Soc Interface 2023; 20:20230281. [PMID: 37727072 PMCID: PMC10509589 DOI: 10.1098/rsif.2023.0281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/29/2023] [Indexed: 09/21/2023] Open
Abstract
Type-B aortic dissection is a cardiovascular disease in which a tear develops in the intimal layer of the descending aorta, allowing pressurized blood to delaminate the layers of the vessel wall. In medically managed patients, long-term aneurysmal dilatation of the false lumen (FL) is considered virtually inevitable and is associated with poorer disease outcomes. While the pathophysiological mechanisms driving FL dilatation are not yet understood, haemodynamic factors are believed to play a key role. Computational fluid dynamics (CFD) and 4D-flow MRI (4DMR) analyses have revealed correlations between flow helicity, oscillatory wall shear stress and aneurysmal dilatation of the FL. In this study, we compare CFD simulations using a patient-specific, three-dimensional, three-component inlet velocity profile (4D IVP) extracted from 4DMR data against simulations with flow rate-matched uniform and axial velocity profiles that remain widely used in the absence of 4DMR. We also evaluate the influence of measurement errors in 4DMR data by scaling the 4D IVP to the degree of imaging error detected in prior studies. We observe that oscillatory shear and helicity are highly sensitive to inlet velocity distribution and flow volume throughout the FL and conclude that the choice of IVP may greatly affect the future clinical value of simulations.
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Affiliation(s)
- C. Stokes
- Department of Mechanical Engineering, University College London, London, UK
- Wellcome-EPSRC Centre for Interventional Surgical Sciences, London, UK
| | - D. Ahmed
- Department of Aeronautics, Imperial College London, London, UK
| | - N. Lind
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland
| | - F. Haupt
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland
| | - D. Becker
- Clinic of Vascular Surgery, Inselspital, University of Bern, Bern, Switzerland
| | - J. Hamilton
- Department of Mechanical Engineering, University College London, London, UK
| | - V. Muthurangu
- Centre for Translational Cardiovascular Imaging, University College London, London, UK
| | - H. von Tengg-Kobligk
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland
| | - G. Papadakis
- Department of Aeronautics, Imperial College London, London, UK
| | - S. Balabani
- Department of Mechanical Engineering, University College London, London, UK
- Wellcome-EPSRC Centre for Interventional Surgical Sciences, London, UK
| | - V. Díaz-Zuccarini
- Department of Mechanical Engineering, University College London, London, UK
- Wellcome-EPSRC Centre for Interventional Surgical Sciences, London, UK
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