This study examines the spatial distribution of microhemorrhages defined using susceptibility weighted images (SWI) in 46 patients with Traumatic Brain Injury (TBI) and applying region of interest (ROI) analysis using a brain atlas. SWI and 3D T1-weighted images were acquired on a 3T clinical Siemens scanner. A neuroradiologist reviewed all SWI images and manually labeled all identified microhemorrhages. To characterize the spatial distribution of microhemorrhages in standard Montreal Neurological Institute (MNI) space, the T1-weighted images were nonlinearly registered to the MNI template. This transformation was then applied to the co-registered SWI images and to the microhemorrhage coordinates. The frequencies of microhemorrhages were determined in major structures from ROIs defined in the digital Talairach brain atlas and in white matter tracts defined using a diffusion tensor imaging atlas. A total of 629 microhemorrhages were found with an average of 22±42 (range=1-179) in the 24 positive TBI patients. Microhemorrhages mostly congregated around the periphery of the brain and were fairly symmetrically distributed, although a number were found in the corpus callosum. From Talairach ROI analysis, microhemorrhages were most prevalent in the frontal lobes (65.1%). Restricting the analysis to WM tracts, microhemorrhages were primarily found in the corpus callosum (56.9%).
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.