Dr Andrew King is a Reader in Medical Image Analysis in the Biomedical Engineering department at King’s College London (KCL). Dr King received a PhD degree in Computer Science from Warwick University in 1997 under the supervision of Professor Roland Wilson. From 2001-2005 he worked as an Assistant Professor in the Computer Science department at Mekelle University in Northern Ethiopia. Since 2006 he has worked in the Biomedical Engineering department at King’s College London, focusing on image analysis and machine learning in medical imaging.
ESTHER PUYOL ANTON
Dr Esther Puyol Anton completed her Bachelors and Masters of Science at the Polytechnic University of Catalonia (Spain) in Biomedical Signal Processing in 2014. During her studies in Spain, she enrolled in a double degree programme with Telecom Bretagne (France), where she obtained the French Masters of Engineering and a Research Masters in Medical Imaging in 2014. Between 2014-2018, she was a PhD student in the Biomedical Engineering department at King’s College London under the supervision of Dr Andrew King, Dr Paul Aljabar and Prof Julia Schnabel from KCL, and Dr Paolo Piro from Philips Healthcare. The main aim of her PhD was to develop a multimodal statistical atlas of heart function from cardiac MR and ultrasound imaging, which could be applied using only low cost ultrasound imaging. She currently combines a role as a Research Scientist at HeartFlow with supervisory roles with the group.
- Using Machine Learning to Identify Noninvasive Motion-Based Biomarkers of Cardiac Function.
- Automatic Quantification of Cardiac Function.
- Interpretable Machine Learning in Cardiology.
Mr Nicholas Byrne Byrne received his MPhys degree in Physics from the University of Warwick in 2011. Through 2014 he completed the Scientist Training Programme at King's College Hospital NHS Foundation Trust, obtaining an MSc in Medical Physics at KCL and gaining registration as a clinical scientist in medical physics. He continued studying at KCL obtaining an MRes in clinical research in 2015. During and since this time, Nick has been employed by the Medical Physics department at Guy's and St. Thomas' NHS Foundation Trust. He helped develop the department’s 3D printing facility, establishing research projects in paediatric cardiology and living donor renal transplant. In 2018, he began an NIHR-sponsored doctoral research fellowship under the supervision of Dr Andrew King and Prof Giovanni Montana and in conjunction with the School of Biomedical Engineering and Imaging Sciences. His work focuses on the use of deep learning methods to segment congenital heart anatomy from CMR images.
Ms Laia Humbert-Vidan received her BSc (Hons) in Physics at the University of Barcelona (Spain) in 2009 and received her MSc degree in Medical Physics from the University of Surrey in 2010. In 2014 she completed the Clinical Scientist Training Scheme within the North London Training Consortium and obtained registration within the Health Care Professionals Council (HCPC). Between 2012-2015 she worked as a Clinical Scientist within the Radiotherapy Department at St Luke’s Cancer Centre, Royal Surrey County Hospital. Since 2015 she has worked in the Radiotherapy Department at Guy’s and St Thomas’ NHS Foundation Trust (GSTT). In 2018, she became a PhD student under the supervision of Dr Teresa Guerrero Urbano from GSTT and Dr Andrew King from King’s College London. Her PhD is focused on the use of machine learning methods to develop prediction models for radiation-induced toxicities in head and neck cancer.
Mr Matthieu Ruthven received his MEng degree in Engineering Science from the University of Oxford in 2013. In 2016, he completed the NHS Scientist Training Programme and received his MSc in Clinical Sciences (Medical Physics) from King's College London. From 2016-19, Matthieu worked as an MRI Research Physicist in the MRI Physics team at Barts Health NHS Trust, leading three speech MRI projects. In 2019, Matthieu began an HEE/NIHR Clinical Doctoral Research Fellowship supervised by Dr Andrew King and Dr Marc Miquel. His PhD project aims to develop ways to create dynamic 3D computer models of the inside of the mouth during speech, in order to help clinicians to diagnose and treat speech problems.
Mr Zhen Yuan received his BEng degree in Medical Information Engineering from the Sichuan University, China in 2018 and received his MRes in Medical Physics and Biomedical Engineering from University College London (UCL) in 2019. In 2019, he was awarded a King's-China PhD scholarship, and is currently a PhD student under the supervision of Dr Andrew King and Professor Baba Inusa. His work focuses on the use of deep learning methods to segment the spleen in ultrasound images for improved management of sickle cell disease.
Dr Devran Ugurlu received his BSc in Mathematics Engineering in 2009 and MSc in Computational Science and Engineering in 2012 at Istanbul Technical University (Turkey). He received his PhD from Sabanci University (Turkey) in 2018 under the supervision of Professor Gozde Unal. His PhD focused on the development of novel mathematical methods for analysis of brain white matter fibers using diffusion MRI data. He continued working under Professor Gozde Unal as a postdoctoral researcher until 2020 at Istanbul Technical University on developing machine/deep learning methods for the classification of white matter streamlines in the brain. He worked with Prof Julia Schnabel and Dr Andrew King on domain adaptation for cardiac MR images, and is currently working on developing an efficient pipeline for forming digital twins from cardiac MR.
- Domain adaptation for cardiac MR segmentation
Ms Tareen Dawood received her BSc degree in Electrical Engineering from the University of Witwatersrand in 2010. In 2011, she began her career in the corporate world and worked in a variety of industries, notably defence and finance. However, in 2018 she decided to move into academics after her own illness made her want to use her technical background to improve healthcare with a focus on the field of medical imaging. From 2018-2020, Tareen went to study her MSc in Biomedical Engineering at the University of Cape Town. Her thesis focused on automated feature detection from ultrasound to improve the diagnostic pathway for Hodgkin’s Lymphoma as it is often mistaken for TB and HIV within Africa. In 2021, Tareen began her DRIVE-Health studentship supervised by Dr Andrew King, Prof Reza Razavi and Dr Esther Puyol Anton. Her PhD project aims to develop an artificial intelligence (AI) decision support tool that can use big data to assist cardiologists in making better decisions about treating heart failure patients.
Ms Maram Alqarni works as an academic lecturer at Imam Abdurhamn bin Faisal University in Saudi Arabia. She received her BSc degree in Biomedical Engineering from the University of Dammam in 2016, Saudi Arabia, and received her MSc in Medical Engineering & Physics from King’s College London in 2019. She is interested in medical image processing, data analysis, and machine learning. Her MSc project was focused on automatic segmentation of the left ventricle in 3-D echocardiography images and was awarded the best MSc project prize by IPEM. In 2021, she became a PhD student under the supervision of Dr Andrew King and Dr Teresa Guerrero Urbano from GSTT. Her PhD is focused on the use of Machine Learning for Brachytherapy Treatment of Prostate Cancer.
- Machine Learning for Brachytherapy Treatment of Prostate Cancer.
Ms Tiarna Lee received her MEng in Biomedical Engineering from King’s College London in 2021. During her studies, she worked on modelling ultrasound wave propagation when using microbubbles and the quantitative measurement of inhomogeneous magnetisation transfer parameters in brain MR images using artificial intelligence (AI). Her MEng project focussed on the classification of the season of conception of infants by AI using the children's facial landmarks. She is currently a PhD student under the supervision of Dr Andrew King and Dr Miaojing Shi. Her PhD project is focussed on researching racial and gender biases in AI-based segmentation and classification tools.
- Assessing and mitigating bias in AI for cardiac imaging.
Mr Nhat Phung received his BSc degree in medicine in 2018 and ultrasound technician in point-of-care ultrasound in 2019. He worked on clinical trials and ultrasound-related studies for 2 years at Oxford University Clinical Research Unit (OUCRU) then moved to research into artificial intelligence in medical imaging on the VITAL (Vietnam ICU Translational Applications Laboratory) project, focusing in particular on ultrasound image classification, segmentation, quantification and AI-guidance in ultrasound imaging. In 2020, he was awarded a King's College London-OUCRU scholarship funded by Wellcome Innovations Flagships and is currently a PhD student under the supervision of Dr Andrew King, Prof Reza Razavi and Dr Alberto Gomez. His PhD project aims at investigating the clinical utility and usability of AI-enabled ultrasound technology in a resource-limited ICU setting.
Dr Hamideh Kerdegari received her PhD degree in computer science from the University of Sheffield in 2017. After this, she was a research associate at Kingston University researching into video-based crowd anomaly behaviour detection from CCTV cameras. Since 2020, she has worked with Dr Andrew King and Dr Alberto Gomez on the VITAL project at KCL to investigate deep learning techniques for ultrasound video guidance and analysis of lung and muscle applications for critically ill patients in the ICU. Her research interests include computer vision, deep learning, and their applications in medical imaging analysis.
Shaheim Ogbomo-Harmitt received his BEng in Biomedical Engineering and MRes in Biomedical and Translation Sciences at King's College London (KCL). During his studies at KCL, Shaheim completed research projects on applying machine learning to perinatal neuroimaging for biomarker discovery and investigating the instability of Turing patterns on complex networks by analysing the eigenvalue distribution of the Laplacian matrix. Currently, Shaheim is a PhD student at KCL, undertaking research in combining image-based computational biophysical modelling with deep learning to optimise ablation therapy for Atrial Fibrillation under the supervision of Dr Oleg Aslanidi and Dr Andrew King.
- Optimising ablation therapy for AF.
Ms Dewmini Hasara Wickremasinghe received her BEng in Biomedical Engineering from King's College London (KCL) in 2021. During this time she explored the feasibility of employing deep learning methods to identify and further classify extra-cardiac findings in cardiac MRI, under the supervision of Dr Andrew King and Dr Esther Puyol Anton. In 2022, she completed her MSc in Medical Robotics and Artificial Intelligence at University College London. She is currently a PhD student under the supervision of Dr Andrew King, Prof Reza Razavi and Dr Esther Puyol Anton from KCL and Dr Paul Aljabar from Perspectum. Her project aims to detect the changes in the anatomical structures depicted across multiple cardiac MR scans of the same patient and exploit these changes to improve the assessment of the patient's health.
- Synergistic analysis of longitudinal CMR.
Iman received her BEng in Biomedical Engineering from King's College London (KCL) in 2022. Her BEng project focused on retrospectively correcting motion artefacts in CMR images using AI, supervised by Dr Sebastien Roujol and Alexander Neofytou. She is currently on the EPSRC Smart Medical Imaging CDT programme, completing an MRes in Healthcare Technology at KCL in preparation for her PhD project which is supervised by Dr Andrew King and Dr Miaojing Shi. Her PhD project centres on assessing cardiac function from echocardiogram images using AI.
- Cardiac quantification from echocardiography.
Dr Tom Young received his Bachelor of Medical Science (Hons) in 2010 and his Medical degree (MBChB (Hons)) in 2013, both at the University of Birmingham. He began his medical training in 2013, and achieved postgraduate medical qualification with MRCP in 2016. He has worked as a specialist registrar trainee in Clinical Oncology since 2018 at Guy’s Hospital, Kent Oncology Centre and the Royal Marsden Hospital and was awarded the postgraduate qualification of FRCR in 2022. He was awarded an NHS Topol Digital Fellowship in 2022. His PhD work under the supervision of Dr Teresa Guerrero Urbano from GSTT and Dr Andrew King from King’s College London will develop and evaluate AI applications for Head and Neck Cancer Radiotherapy, including autosegmentation tools within the radiotherapy planning process and mining of unstructured big oncological data.
- Machine learning for head and neck cancer radiotherapy.
Dr Miguel Xochicale works with Dr Andrew King and Dr Alberto Gomez on the VITAL project, researching into real-time AI-empowered biometrics from cardiac ultrasound. In July 2019, he was awarded a PhD degree in Computer Engineering from the University of Birmingham where he investigated Nonlinear Analysis to Quantify Movement Variability in Human-Humanoid Interaction under the supervision of Prof Chris Baber and Prof Martin Russell. Between April 2019 to August 2021, he worked with Prof Tom Vercauteren and Dr Wenfeng Xia on ultrasound-guided procedures in the GIFT-Surg project, where he contributed to new algorithms, software, hardware, medical device quality management systems and public engagement activities for ultrasound needle tracking procedures. Dr Xochicale's research interests include real-time AI-based signal and image processing and AI-based fetal biochemanics.
Dr Ines Prata Machado holds Bachelor's and Master’s degrees in Biomedical Engineering from the Faculty of Sciences and Technology at the New University of Lisbon, Portugal. Ines completed her Master’s thesis at Champalimaud Foundation under the supervision of Professors Hugo Gamboa, Vítor Paixão and Rui Costa. Between 2015-2019, Ines was a PhD candidate on the MIT Portugal Program at Instituto Superior Técnico and Harvard Medical School under the supervision of Professors Jorge Martins, Sarah Frisken and Polina Golland. Her PhD focused on developing new methods and technologies to improve precision in image-guided neurosurgery. She worked with Professor Julia Schnabel and Dr Andrew King on developing machine/deep learning algorithms for an integrated framework of image acquisition, reconstruction and analysis.
JORGE MARISCAL HARANA
Dr Jorge Mariscal Harana received his MEng degree in Aeronautical Engineering from Imperial College London in 2015 with a Master's thesis on computational models of arterial blood flow. He received his PhD from King's College London in 2019 under the supervision of Dr Jordi Alastruey, Professor Spencer Sherwin and Dr Peter Charlton. His PhD focused on the development of algorithms to measure aortic blood pressure non-invasively from medical imaging data using computational models of fluid mechanics. Between November 2019 and March 2020, as part of a PhD Industrial Placement at the Advanced Centre for Aerospace Technologies, he worked on the development of a deep learning pipeline for the real-time detection of small aircrafts using sound. In the MMAG, he worked as a Research Associate with Dr Andrew King and Dr Esther Puyol Anton on the generalisation of a cardiac functional analysis framework using deep learning and cardiac magnetic resonance data.
Mr Robin Andlauer received his BSc and MSc in Electrical Engineering and Information Technology from the Karlsruhe Institute of Technology (KIT). During his studies, he worked on computational cardiac modelling and autonomous driving. In his Master’s thesis, he developed a tool using CycleGANs to predict the post-operative face of patients after cranio-maxillofacial surgery. In the MMAG, he worked as a PhD student under the supervision of Dr Andrew King, Dr Bernhard Kainz, and Prof Aldo Rinaldi to research novel means to explain and understand the decision-making of AI in predicting the response of patients to cardiac resynchronization therapy (CRT).
Dr James Clough received his MSci in Theoretical Physics from Imperial College London in 2013. Staying at Imperial, in 2017 he completed his PhD in Physics under the supervision of Dr Tim Evans and Prof Kim Christensen at the Centre for Complexity Science. His research focused on networks and on embedding graphs in geometric spaces, and in particular, Lorentzian spacetime. He worked as a postdoctoral researcher in the MMAG on the development and application of machine learning methods to problems in medical imaging.
- Incorporating Topological Knowledge Into Deep Learning Based Segmentation.
- Interpretable Machine Learning in Cardiology.
- Dynamic MR Imaging Using Manifold Alignment.
Dr Ilkay Oksuz received his MSc degree in Electrical and Electronics Engineering from Bahçesehir University in 2011. He worked on vessel segmentation in CT images under the supervision of Assistant Prof Dr Devrim Unay during his MSc thesis. He studied for a PhD at the IMT Institute for Advanced Studies Lucca on Computer, Decision, and Systems Science under the supervision of Prof Sotirios Tsaftaris. His PhD thesis focused on joint registration and segmentation of the myocardium region in MR sequences. He joined the Diagnostic Radiology Group at Yale University in 2015 for 10 months as a Postgraduate Fellow, where he worked under the mentorship of Prof Xenios Papademetris. He also worked at the University of Edinburgh Institute for Digital Communications department for six months in 2017. In the MMAG his research focused on medical image segmentation, medical image registration and machine learning, with a focus on the automated analysis and quality control of cardiac MR.
Dr Daniel Balfour received a Master's Degree in Physics from the University of Manchester in 2012, with an experimental focus on attenuation correction in PET. He did his PhD under Prof Paul Marsden and Dr Andrew King in the Biomedical Engineering department at King’s College London on motion estimation in PET. His work in the MMAG focused on incorporating respiratory motion models into PET reconstruction.
- PET-MR Motion Correction Constrained by a Respiratory Motion Model.
- Dynamic MR Imaging Using Manifold Alignment.
Dr Matthew Sinclair received his BEng(Hons) in Biomedical Engineering from the University of Auckland in 2009, focusing on the analysis of body shape using PCA for his Honours project. He carried out an internship at INRIA under the supervision of Dr Dominique Chapelle before pursuing a PhD at King's College London under the supervision of Professors Nic Smith and Tobias Schaeffter. He completed his PhD in 2014, focusing on validation of quantitative perfusion MRI using microspheres, and modelling blood flow distribution in the coronary arterial circulation. His PhD involved the development of algorithms for computational cardiac mesh fitting, coronary network segmentation, 1D Navier-Stokes and Poiseuille flow modelling and statistical analysis comparing simulated flow with microsphere distribution in the coronary circulation. He continued working as a research associate at King's College London further developing coronary network models before moving into analysis of cardiac and aortic motion. His work in the MMAG focused on applying machine learning techniques to analyse cardiac motion models in relation to cardiac diseases.
Dr Xin Chen was a Research Associate in the group from 2015-2017. Dr Chen received his PhD degree from the University of Central Lancashire in medical imaging and worked as a post-doctoral researcher in the Centre for Imaging Sciences at the University of Manchester from 2010 to 2015. His research interests are medical image analysis and computer vision, particularly in image segmentation, image registration, statistical modelling and machine learning. With Dr Andrew King, his work focused on motion estimation in PET/MRI using manifold alignment of k-space data.
Dr Christian Baumgartner was a PhD student in the Biomedical Engineering department at King's College London from 2012-2016 under the supervision of Dr Andrew King and Prof Daniel Rueckert from Imperial College London. He was awarded a BSc in Electrical Engineering and Information Technology (2009) and a MSc in Biomedical Engineering (2012) from the Federal Technical Institute (ETH) in Zurich, Switzerland. The main focus of his PhD thesis was to apply and extend non-linear dimensionality reduction techniques for modelling and imaging of respiratory motion. In the MMAG he worked on dynamic MR imaging using manifold alignment and autoadaptive motion modelling.
Dr Devis Peressutti received his MSc and BSc in Biomedical Engineering from the Polytechnic University of Turin in 2011 and 2009, respectively. He completed his PhD in Biomedical Engineering within the MMAG in 2014 under the supervision of Dr Andrew King and Dr Graeme Penney. His PhD work included a Bayesian approach to incorporating ultrasound imaging data into cardiac respiratory motion correction and population-based respiratory motion modelling. Subsequently, he worked as a Research Associate on the analysis of cardiac cycle motion data using machine learning techniques. His main research interests are organ motion estimation, modelling and analysis, and the application of machine learning, pattern recognition and computer vision techniques in medical imaging.
- Using Machine Learning to Identify Noninvasive Motion-Based Biomarkers of Cardiac Function.
- Motion-Based Image Registration.
- Motion Correction for Cardiac Catheterisations.
Dr Christian Buerger received the degree "Diplom-Ingenieur" in Electrical Engineering and Information Technology from the RWTH Aachen University, Germany, in 2008. He completed his PhD in Motion Estimation from MR between 2008-2011 under the supervision of Prof Tobias Schaeffter and Dr Andrew King. In the MMAG, Dr Buerger worked on intensity-based nonrigid registration.