The development of a novel MRI based method for measuring blood perfusion in neurovascular damage

Diffusion-weighted magnetic resonance imaging (DWI) is a key neuroimaging technique. Multi b-value DWI is composed of an unknown number of exponential components which represent water movement in various compartments, notably tissue and blood vessels. The bi-exponential model, Intravoxel Incoherent Motion (IVIM), is commonly used to fit the perfusion component but does not take account of the multi-component nature of the data. In this work, a new fitting method, the Auto-Regressive Discrete Acquisition Points Transformation (ADAPT) was developed and evaluated on simulated, phantom, volunteer and clinical DWI data. ADAPT is based on the auto-regressive moving average model, making no prior assumptions about the data. ADAPT demonstrated that it could correctly identify the number of components within the diffusion signal. The ADAPT coefficients demonstrated a significant correlation with IVIM parameters and a significantly stronger correlation with cerebral blood volume derived from dynamic susceptibility contrast MRI. A reformulation of the ADAPT method allowed the IVIM parameters to be mathematically derived from the diffusion signal and demonstrated lower bias and more accuracy than currently implemented fitting methods, which are inherently biased. ADAPT provides a novel method for non-invasive determination of diffusion and perfusion biomarkers from complex tissues.