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Your search for Keyword: 'CT' returned 16 Result(s)
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Dr Ankush Aggarwal
Computational cardiovascular mechanics
The aim of my research is to generate new scientific knowledge and methods using innovative ideas in numerical methods and biomechanics that contribute towards improved cardiovascular healthcare.
Dr Gordon Cowell
Pulmonary/pleural imaging with focus on pharmacokinetics, radiomics and imaging biomarkers. Venous thromboembolic disease. Whole body MRI imaging.
Sean Denham
Dr Lorna Gibson
Dr Ali Gooya
My research interest broadly lies in machine learning, computer vision and medical imaging. I am particularly keen on deep probabilistic learning with the target applications in cancer and cardiac image analysis, computer-aided decision support systems, prediction and marker discovery, statistical inference on populations, and computational anatomy.
Dr Calum Gray
Mr Khalid Hakami
We plan to use imaging to investigate the association of comorbidity of lung inflammatory disease with cardiovascular disease, determined by measuring biomarkers of cardiovascular disease.
Dr Tiziana Liuti
Computed Tomography (small animal, exotics, equine)
Contrast Enhanced Ultrasound (small animal)
Abdominal and Thoracic Ultrasound (small animal and exotics)
Radiology (small animal, exotics, equine)
MRI (small animal, equine)
Dr Grant Mair
My primary research interest is the imaging of ischaemic stroke.
Since 2012, I have been working on subgroup analyses of imaging from the Third International Stroke Trial (IST-3), a large randomised-controlled trial of intravenous alteplase versus control.
In particular, I am investigating whether imaging evidence of arterial obstruction (e.g. hyperdense artery sign as a surrogate marker or flow deficits on CT/MR angiography) and the results from advanced CT/MR perfusion techniques should influence our decision to treat patients with intravenous thrombolytic drugs. This is especially important in an era where ischaemic stroke treatment is changing with new evidence supporting the use of direct clot retrieval.
I am also developing interests in the use of computational systems for the automated analysis of CT imaging in stroke and natural language processing algorithms for identifying and classifying stroke patients from within very large radiology report datasets.
Dr Alessandro Perelli
My main research interests are primarily related to computational imaging, and machine learning, in particular for low-dose poly-energetic 3D/4D and spectral X-ray Computed Tomography (CT) reconstruction.
My particular specialism and passionate interests span to randomized linear algebra, stochastic iterative algorithms and variational inference models.
I am dealing with all aspects of the image reconstruction, from the theoretical mathematical modelling, through the algorithm development, to the handling and processing of experimental CT data together with physical modeling such as scattering.
