Publication

Abstract : Magnetic Resonance Imaging (MRI) using Arterial Spin Labeling (ASL) is a quantitative Imaging technique which is used to quantify Cerebral Blood Flow (CBF) and it plays a vital role as a bio-marker for various neuro-degenerative diseases and brain tumour. The ASL images suffer from low Signal-to-Noise Ratio (SNR) and low resolution, which can be improved by acquiring a number of ASL raw images called label and control images. Acquiring large number of images, results in prolonged scanning time, which in turn leads to different artifacts in ASL images. Hence different image preprocessing techniques are essential for the accurate quantification of CBF values. Moreover, there is no standard procedure for processing ASL data due to the large number of assumptions and various parameters involved in CBF quantification. The proposed research work analyses the effects of different preprocessing stages on CBF quantification on pulsed ASL (PASL) and Pseudo continuous ASL (PCASL) data. The use of an outlier detection SCORE+ algorithm with and without preprocessing stages are also examined.