Signal processing tools for MRI perfusion-weighted imaging data analysis.

摘要

In dynamic susceptibility contrast (DSC) magnetic resonance (MR) approaches, by injecting a bolus of paramagnetic contrast agent intravenously, the measured MR signal is converted to a concentration time course to estimate hemodynamic parameters like cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT).; Before estimating hemodynamic parameters, recirculation effects need to be removed by a gamma-variate fit of the concentration curve. In this dissertation, however, it has been found and demonstrated by simulation that fitting may not discern recirculation from the first-pass in case of cerebral ischemia. A new methodology using temporal independent component analysis (ICA) to remove recirculation in both normal and ischemic brain tissues while preserving the first-pass is therefore proposed. This should improve hemodynamics accuracy particularly in ischemic lesions.; In DSC MR approaches, bolus delays between the arterial input function (AIF) and tissue curves may induce significant CBF quantification error. Our second contribution is using ICA to estimate bolus arrival time for each 5 x 5 region of interest (ROI) throughout the brain parenchyma. A global AIF measured from a major artery can then be shifted in accordance to define a local AIF for each ROI. The bolus delay may therefore be minimized, and the general shape of the AIF is preserved. This should improve the flow quantification.; Transfer function has been widely used to characterize an unknown system. In DSC MR approaches, vascular transfer function (VTF) represents the probability density function of the vascular transit time. Our third contribution is to propose a new tool to estimate intracranial VTF non-invasively. This should provide an alterative means of assessing tissue perfusion status, particularly in patients with cerebrovascular diseases.; Bolus dispersion between the AIF and tissue curves may induce flow quantification error, which cannot be minimized without the knowledge of vasculature. Our final contribution is to develop an extended cerebral vascular model to minimize delay and dispersion dependence by modelling flow heterogeneity in both bulk small arteries and capillary bed. This should yield more stable flow rates less sensitive to bolus delay and dispersion.