Bayesian reconstruction of low resolution magnetic resonance imaging modalities

Magnetic Resonance Imaging (MRI) data are available in a large variety of modalities that individually can provide anatomical, metabolic, physiological, and functional descriptions of the brain. Structural MRI (sMRI) is now standard for the diagnosis of soft tissue injury, especially for delineation of anatomical features in the brain. Unfortunately, although most Magnetic Resonance (MR) modalities have great potential for aiding clinical applications including diagnosis and surgical intervention, some are not attainable with the high signal-to-noise ratio (SNR) of sMRI, and hence are necessarily acquired at low spatial resolution. This research aims to utilise information from the high SNR modality of sMRI in order to boost the effective resolution of lower SNR MRI modalities. sMRI of the brain provides high resolution maps of signal intensity contrasts between different types of tissue, such as grey matter (neuronal cell bodies), white matter (axons) and cerebro-spinal fluid. The segmented sMRI maps are here employed as prior information to enable improved resolution reconstruction of lower resolution MRI modalities such as: Perfusion Imaging for measuring blood flow, Magnetic Resonance Spectroscopy Imaging (MRSI) for measuring metabolite levels and Diffusion Tensor Imaging (DTI) for measuring water diffusion in tissue.