Title: A Comparison of Computational Perfusion Imaging Techniques.

Abstract: Perfusion imaging is valuable because it is used to help grade tumors; differentiate between tumor types; differentiate tumors from nonneoplastic lesions; guide intraoperative sampling; most importantly, determine the efficacy of treatment. Computational techniques combined with the imaged data can help identify important biological parameters. For example, key parameters include cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) . These parameters can help distinguish between the likely salvageable tissue and irreversiblydamaged infarctcore.Theparametersarecalculateddeconvolvingcontrast-time curves with the arterial inlet input function. A common approach employed with the deconvolution method is a singular value decomposition (SVD). However, these algorithms are very sensitive to noise and artifacts in the source image which may introduce additional distortions in the output parameters. For this reason, we will employ machine learning algorithms to aid in the measurements of perfusion parameters from CT imaging and compare to parameter measurement using SVD with regularization.