Quantitative evaluation of phase processing approaches in susceptibility weighted imaging

Abstract

Susceptibility weighted imaging (SWI) takes advantage of the local variation in susceptibility between different tissues to enable highly detailed visualization of the cerebral venous system and sensitive detection of intracranial hemorrhages. Thus, it has been increasingly used in magnetic resonance imaging studies of traumatic brain injury as well as other intracranial pathologies. In SWI, magnitude information is combined with phase information to enhance the susceptibility induced image contrast. Because of global susceptibility variations across the image, the rate of phase accumulation varies widely across the image resulting in phase wrapping artifacts that interfere with the local assessment of phase variation. Homodyne filtering is a common approach to eliminate this global phase variation. However, filter size requires careful selection in order to preserve image contrast and avoid errors resulting from residual phase wraps. An alternative approach is to apply phase unwrapping prior to high pass filtering. A suitable phase unwrapping algorithm guarantees no residual phase wraps but additional computational steps are required. In this work, we quantitatively evaluate these two phase processing approaches on both simulated and real data using different filters and cutoff frequencies. Our analysis leads to an improved understanding of the relationship between phase wraps, susceptibility effects, and acquisition parameters. Although homodyne filtering approaches are faster and more straightforward, phase unwrapping approaches perform more accurately in a wider variety of acquisition scenarios.