Spatial gradient based segmentation of vessels and quantitative measurement of the inner diameter and wall thickness from ICG fluorescence angiographies

Abstract

During neurovascular surgery the vascular function can be checked intraoperatively and qualitatively by observing the blood dynamics inside the vessel via Indocyanine Green (ICG) Fluorescence Angiography. This state-of-theart method provides the surgeon with valuable semi-quantitative information but needs to be improved towards a quantitative assessment of vascular volume flow. The precise measurement of volume flow rely on the assumption that both the inner geometry of the blood vessel and the blood flow velocity can be precisely obtained from Fluorescence Angiography. The correct reconstruction of the inner diameter of the vessel is essential in order to minimize the propagated error in the flow calculation. Although ICG binds specifically on blood plasma proteins the fluorescence light radiates also from outside the inner vessel volume due to multiple scattering in the vessel wall, causing a fading edge intensity contrast. A spatial gradient based segmentation method is proposed to reliably estimate the inner diameter of cerebral vessels from intraoperative Fluorescence Angiography images. As result the minimum of the second deviation of the intensity values perpendicular to the vessels edge was identified as the best feature to assess the inner diameter of artificial vessel phantoms. This method has been applied to cerebrovascular vessel images and the results, since no ground truth is available, comply with literature values.