Vicente Grau is an RCUK Academic Fellow at the Department of Engineering Science and the Oxford e-Research Centre, University of Oxford. He is associated with the Institute of Biomedical Engineering (IBME). He is also a Senior Research Fellow at Mansfield College.
He received his undergraduate degree in Electrical Engineering from the Universidad Politecnica de Valencia, Spain, in 1994, and his PhD in Medical Image Analysis in 2001. After spending two years as a postdoc at Brigham and Women’s Hospital, Harvard University and the ONH Biomechanics Lab at LSU Health Sciences Center in New Orleans, he joined the Medical Vision Laboratory in Oxford. He became an RCUK Academic Fellow in 2007.
Vicente’s research focuses on the development of biomedical image analysis algorithms, with an emphasis on the combination with computational models, and applications on cardiac and pulmonary medicine.
Lung diseases are one of the main causes of mortality and disability worldwide. In recent years, hyperpolarized gas MRI has emerged as a promising new technology with the potential to improve diagnosis and potentially allow the development of new, better treatments. Before this promise is fulfilled, advances need to be made in both acquisition technology and image analysis. We are focusing in the image analysis side by using a combination of state-of-the-art image analysis methods and computational models, with the final aim of improving the understanding of image datasets and developing methods that link image values with the underlying lung function.
Computational models are becoming a standard tool in many biomedical applications, and in particular in cardiovascular medicine. In the last years we have developed a pipeline to build computational models from high-resolution multimodal images. This includes the development of 3D histology dataset through registration with MRI scans, segmentation of relevant structures and mesh generation and the application of ionic models to investigate the relevance of small structures in electrical simulation results. Current research includes the extension of these methods to quantify intersubject variability, through the use of a standardized reference frame.
Images are becoming ubiquitous in biological applications. Current image data volumes in biology labs no longer allow traditional visual analysis, and with the increasing use of high-throughput experiments there is a pressing need for robust, reusable biological image processing tools. Our current interests include the use of phase-based operation for curvilinear structure extraction in microscopy images.