Arterial input function segmentation based on a contour geodesic model for tissue at risk identification in ischemic stroke

Abstract

Purpose: Perfusion parameters such as cerebral blood flow (CBF) and T_max have been proven to be useful in the diagnosis and prognosis for ischemic stroke. Arterial input function (AIF) is required as an input to estimate perfusion parameters. This makes the AIF selection paradigm of clinical importance.

Methods: This study proposes a new technique to address the problem of AIF selection, based on a variational segmentation model that combines geometric constraint in a distance function. The modified model uses discrete total variation in the distance term and via minimizing energy locates the arterial regions. Matrix analysis is utilized to identify the AIF with maximum peak height within the segmented region.

Results: Group mean differences indicate that overall the AIF selected by the purposed method has better arterial features of higher peak position (16.7 and 26.1 a.u.) and fast attenuation (1.08 s and 0.9 s) as compared to the other state-of-the-art methods. Utilizing the selected AIF, mean CBF, and T_max values were estimated higher than the traditional methods. Ischemic regions were precisely located through the perfusion maps.

Conclusions: This AIF segmentation framework worked on perfusion images at levels superior to the current clinical state of the art. Consequently, the perfusion parameters derived from AIF selected by the purposed method were more accurate and reliable. The proposed method could potentially be considered as part of the calculation for perfusion imaging in general.

Keywords: arterial input function measurements; cerebral blood flow; cerebral perfusion imaging; dynamic susceptibility contrast; variation model.