Academic Staff


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Room 8.21, Malet Place Engineering Building, University College London, 2 Malet Place
United Kingdom
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Miscellaneous Information

I have a BSc in Biomedical Engineering (2006) and an MSc in Medical Electronics and Signal Processing for Biomedical Engineering (2008) from the Universidade do Minho, Portugal, followed by a PhD (2008-2012) and PostDoc (2012-2015) in medical image analysis and biomarker development between CMIC and the Dementia Research Centre at UCL. In June 2015 I have been appointed Lecturer in Quantitative Neuroradiology at the Translational Imaging Group, part of CMIC, in collaboration with the National Hospital for Neurology and Neurosurgery, working on translating and integrating quantitative biomarkers and automated image analysis techniques within the clinical environment.



From a technical point of view, my research explores novel highly accurate and robust machine learning techniques to segment, parcellate and localize different types of tissues using anatomical, microstructural and functional images. I focus mostly on general purpose algorithms which provide a unified framework for image segmentation, parcellation, bias field correction and data synthesis. By solving for all these aspects jointly, these models are able to provide a coherent picture of human anatomy and change. Segmentations obtained using these joint models have been shown to improve the accuracy and power of imaging biomarkers in large-scale clinical trials and research studies, and enabling the estimation of improved microstructural characteristics from diffusion imaging, functional connectivity from fMRI, and blood perfusion and cerebral blood flow from ASL. These biomarkers are relevant for diagnosis, estimation of disease progression, and the monitoring of disease-modifying treatments.

Together with colleagues at the National Hospital for Neurology and Neurosurgery, we have been working on general purpose learning tools for Big Medical Data with support from the Wellcome Trust and the UCLH BRC Imaging Initiative. We are using Big Data to learn how human brains truly look like, thus encoding anatomical variability and pathological presentation. This learning can be further augmented by diagnostic and radiological report data available in clinical systems, providing an integrated view of the human interpretation of medical imaging data. These models can then be used as tools for precision medicine, part of the new NHNN Quantitative Neuroradiology Initiative (QNI), where we aim to translate advanced imaging technologies and biomarkers to clinical practice in order to streamline the clinical workflow and improve the quality of care. This process of technical translation requires deep algorithmic and model integration into the hospital PACS, fully automated image processing with associated quality control and assurance, extensive validation on clinical grade data, and the deployment of an automated reporting system that summarizes a complex set of imaging biomarkers against a healthy matched population. While we have initially focused on the cross-sectional and longitudinal analysis of imaging data from patients with neurological conditions, we aim to go beyond this by integrating clinical, genetic and cognitive data to provide a consolidated and efficient end-to-end clinical care platform. 


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Medical Image Computing, Information Processing in Medical Imaging 

Advanced Neuroimaging, Morphology & Volumetry 

Advanced Neuroimaging, Medical Image Processing (Matlab) 



Publication List

Number of publications: 152.










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