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Shedding New Light
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Research

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Research

Current Research

Computational Imaging in Oncology (Dave Hawkes) Click here for more information.

Dave Hawkes leads activity in medical image computing applied to oncology. He is co-director of the UCL/KCL Comprehensive Cancer Imaging Centre . CMIC's activity currently is centred around imaging for the detection and management of breast cancer, detection and management of colon cancer , image directed radiotherapy of lung cancer, diagnosis and image directed focal therapy of prostate cancer, image directed focal therapy in liver cancer

UCL Microstructure Imaging Group (Daniel Alexander) Click here for more information.

We work towards a goal of non-invasive histology. Traditional histology uses a microscope to visualize the cellular microstructure of tissue from a thinly sliced sample mounted on a slide. Histology provides the gold-standard diagnosis for a wide range of diseases from cancers to dementias. However, the traditional approach requires an tissue sample from a biopsy or taken post mortem. The idea of non-invasive histology is to obtain the same information that a histologist gets from microscope images from non-invasive imaging techniques such as magnetic resonance imaging (MRI). Our aim is to replace tissue biopsy with measurements from live in-situ tissue.

Vision and Imaging Science Group (Simon Arridge) Click here for more information.

 Computer vision and image processing aim to extract useful information from images and movies. Possible applications include face recognition, automated analysis of medical images, robotics, visual inspection for production lines and building 3D models of the real world. One reason that image analysis is an interesting research problem is the sheer amount of data involved. A VGA (640x480) camera collecting data at 30Hz provides 27 Mb of image data per second. Medical images tend to be of at least an order of magnitude larger. A perrenial issue is how to efficiently extract salient and useful parts of this huge datastream. Other research investigates the geometry of multiple cameras and how they relate to objects in the scene. A further area of work involves building statistical models of the appearance and shape of objects in the images - these can then be used for segmentation or object recognition. Vision and Imaging Science makes use of mathematical techniques including geometry, statistics, physics, statistical decision theory, signal processing, algorithmics and analysis/partial differential equations. Computer vision is closely related to several other academic fields, including computer graphics, neural networks and machine learning, robotics, biological vision and wearable computing.

Disease Progression Modelling (Click here for more information) 

We are developing new mathematical models and computational techniques to recover characteristic patterns of disease progression from large cross sectional data sets. We focus on dementias such as Alzheimer's disease, although our techniques have wider application to other diseases and developmental processes. The project is a collaboration with the Dementia Research Group at UCL's Institute of Neurology and builds on the seminal work of Hubert Fonteijn (NeuroImage 2012) on event-based models.

Motion correction, artefact reduction, sampling and reconstruction in MRI (David Atkinson). Click here for more information

Magnetic Resonance Imaging (MRI) produces images with good contrast between soft tissues and in any 3D orientation. It does not use ionizing radiation and has an excellent safety record. The imaging time can be long, especially for images with high resolution and good contrast. This means that patients may move during the acquisition which can have the effect of causing ghosting and blurring artefacts in the image. We look at way to reduce the effect os such motion.

 

 

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