Applications of Statistical Methods in Quantitative Magnetic Resonance Imaging

Magnetic resonance imaging, MRI, offers a wide range of imaging methods that can be employed in the characterization of tumors. MRI is generally used in a qualitative way, where radiologists interpret the images for e.g. diagnosis, follow-ups, or assessment of treatment response. In the past decade, there has been an increased interest in quantitative imaging, which give repeatable measurements of anatomy, biological processes or functions. Quantitative imaging allows for an objective analysis of the images, which are grounded in underlying physical properties of the tissues. The aim of this thesis was to improve quantitative measurements in dynamic contrast enhanced MRI (DCE-MRI), and to improve the texture analysis of diffusion weighted MRI (DW-MRI). DCE-MRI measures perfusion, which is the delivery of blood, oxygen and nutrients to the tissues. The examination involves continuously imaging the region of interest, e.g. a tumor, while injecting a contrast agent (CA) in the blood stream. By analyzing how fast and how much CA leaks out into the tissues, the cell density and the permeability of the capillaries can be estimated. Tumors often have an irregular and broken vasculature, and DCE-MRI can aid in tumor grading or treatment assessment. One step that is crucial when performing DCE-MRI analysis is the quantification of CA in the tissue. The CA concentration is difficult to measure accurately due to uncertainties in the imaging, properties of the CA, and models of the patient physiology. In Paper I, the possibility of using two aspects of the MRI data, phase and magnitude information, for improved CA