Development of methods that describe the heterogeneity of multiparametric imaging data.
Background
Non-invasive imaging is essential for measuring the effects of new treatments in the clinic. Magnetic resonance imaging (MRI) is now a widely employed diagnostic method for the evaluation of patients with cancers. It is noted for remarkable soft tissue definition, the absence of ionising radiation, high spatial and temporal resolution. It also has the ability to generate images in any plane of the body. Technical improvements in MRI mean that we can obtain images very quickly, in a few seconds if required. This speed is being exploited to obtain several types of MRI image during a single patient examination and to calculate the kinetics of contrast agent uptake from a series of images.
Biological tissues, particularly tumours, exhibit a substantial degree of spatial heterogeneity within their physiological properties which include the function of blood vessels, oxygen delivery and the sensitivity of the tumour to varying types of drug and radiation treatments. There is currently no single strategy that is routinely used to describe the heterogeneity of tumour properties.
The aim of this project is to develop image processing techniques, in the form of a stand alone, simple to use software package, that will describe the heterogeneity of multi parametric imaging data, especially related to tumour physiology and biochemistry. For the Project various imaging techniques shall be used on various data to describe the heterogeneity. These will include histogram analysis, texture descriptions of the data and techniques from geostatistics, specifically the semi-variogram. Other image processing techniques will also be explored when they prove useful in transforming data into a more convenient state to perform analysis upon the heterogeneity. Imaging techniques that are also used widely within image processing will also be used when necessary. These include segmentation and intensity thresholding. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Intensity thresholding allows the removal of data based upon its intensity, which allows one to remove any extraneous, unwanted data before further processing.
Typically the sampled representation of the data will be an image acquired from medical instrumentation such as CT or MRI scanners with an eye for supporting future developments in molecular imaging with MRI and PET.
The information gathered from using these image processing techniques could help to translate new vascular targeted and radiation treatments into the clinic. The research throughout will closely involve the MRI group located at Mount Vernon Hospital, who will provide actual clinical data and interpretation experience.