  Deviation of blood flow from an optimal range is known to be associated with
the initiation and progression of vascular pathologies. Important open
questions remain about how the abnormal flow drives specific wall changes in
pathologies such as cerebral aneurysms where the flow is highly heterogeneous
and complex. This knowledge gap precludes the clinical use of readily available
flow data to predict outcomes and improve treatment of these diseases. As both
flow and the pathological wall changes are spatially heterogeneous, a crucial
requirement for progress in this area is a methodology for co-mapping local
data from vascular wall biology with local hemodynamic data. In this study, we
developed an imaging pipeline to address this pressing need. A protocol that
employs scanning multiphoton microscopy was designed to obtain 3D data sets for
smooth muscle actin, collagen and elastin in intact vascular specimens. A
cluster analysis was developed to objectively categorize the smooth muscle
cells (SMC) across the vascular specimen based on SMC density. In the final
step in this pipeline, the location specific categorization of SMC, along with
wall thickness was co-mapped with patient specific hemodynamic results,
enabling direct quantitative comparison of local flow and wall biology in 3D
intact specimens.
