Input Function Detection in MR Brain Perfusion Using a Blood Circulatory Model Based Algorithm

Introduction: The arterial input function (AIF) plays a crucial role in mapping Cerebral Blood Volume, Mean Transit Time, and Cerebral Blood Flow (CBV, MTT, and CBF) in MR perfusion studies. Automation of the process of selecting an AIF for use in further calculation could substantially reduce bias in hemodynamic parameters introduced by operator dependency and accelerate perfusion analysis. While a variety of approaches have emerged to predict AIF profile, many of them are too complex to be implemented in practical experiments [2]. In order to be useful, a model based algorithm needs to be easy to assemble, and practical [3, 4]. Thus, an analytical model-based algorithm for detection of AIF would be beneficial to MR perfusion analysis. This study uses a blood circulatory model previously introduced by our research group 5] to construct an automatic algorithm for AIF detection in MR perfusion studies [5]. The aim of this study is to construct an objective function, based on the parameters in the blood circulatory model, for detecting the AIF in MR perfusion studies. The proposed algorithm is applied to 19 slices of four patients with Dynamic Susceptibility Contrast (DSC) MR perfusion studies. Results imply that the proposed algorithm is capable of detecting the AIF with a success rate of 84%, suggesting that this method is a reliable method for the analysis of MR perfusion studies.