Robust Semi-Automated Arterial Input Function Identification Using Self Organizing Maps

J. J. Jain, W. E. Reddick Radiological Sciences, St. Jude Children's Research Hospital, Memphis, TN, United States PURPOSE Absolute quantification of cerebral blood flow (CBF) and volume (CBV) using dynamic-susceptibility contrast MRI relies on deconvolution of the arterial input function (AIF) commonly estimated from signal changes in a major artery. However, the presence of bolus delay and dispersion between the artery and the tissue can be a significant source of error in the estimation of the kinetic parameters. These effects can be minimized if a local AIF is used, although, its measurement is nontrivial. We present an automated technique to determine the local AIF by the selection of best-candidate arterial pixels using a Kohonen Self-Organizing Map (SOM). The technique is validated across five patients with three exams each.