| Zusammenfassung |
MRI scanners are used widely to diagnose disease and to understand the workings of the healthy body. However, while useful for some diagnoses, they do not capture tissue properties at microscopic length scales (thousandths of a millimetre) where important processes occur, e.g. in the 'axons' connecting different brain areas, or in cells in vital organs, e.g. liver. Such detailed examination usually requires an invasive 'biopsy' which is studied under a microscope. However, biopsies only provide information about small regions of an organ, are destructive and so cannot be performed repeatedly for monitoring, and can be risky to collect, e.g. in the brain. This project assembles engineers, physicists, mathematicians and computer scientists to develop new MRI methods for quantifying tissue structure at the microscopic scale. The principal approach looks at how fine tissue structure impedes the movement of water. Current MRI hardware restricts measurement to relatively large molecular displacements and from tissue components with a relatively strong and long-lived signal. This blurs our picture and prohibits us from quantifying important characteristics, such as individual cell dimensions, or packing of nerve fibres. The sensitivity of MRI to smaller molecular movements and weaker signals is mainly limited by the available magnetic field gradients (controlled alterations in the field strength within the scanner). We have persuaded MRI manufacturers to build a bespoke MRI system with ultra-strong gradients (7 times stronger than available on standard MRI scanners) to be situated in the new Cardiff University Brain Research Imaging Centre. One similar system currently exists (in Boston, USA) but is used predominantly to make qualitative pictures of the brain's wiring pattern. Our team has the unique combination of expertise to develop and exploit this hardware in completely new directions. By designing new physics methods to 'tune' the scanner to important (otherwise invisible) signals, developing new biophysical models to explain these signals, and suppressing unwanted signals, we will be able to quantify important tissue properties for the first time. Making such a system usable poses several key engineering challenges, such as modelling of electromagnetic fields, to deal with confounds that become significant with stronger gradients, and modelling of the effects on nerves/cardiac tissue, to impose safety constraints. However, the current work of the consortium of applicants provides strong starting points for overcoming these challenges. Established methods for accelerating MR data acquisition will be compromised with stronger gradients, requiring development of new physics methods for fast data collection. Once achieved, faster acquisition and access to newly-visible signal components will enable us to develop new mathematical models of microstructure incorporating finer length-scales to increase understanding of tissue structure in health and disease, and to make testable predictions on important biophysical parameters such as nerve conduction velocities in the brain. This will result in earlier and more accurate diagnoses, more specific and better-targeted therapy, improved treatment monitoring, and overall improved patient outcome. The ultimate goal is to develop the imaging software that brings this hardware to mass availability, in turn enabling a new generation of mainstream microstructure imaging and macrostructural connectivity mapping techniques to translate to frontline practice. |
