Introducing prior knowledge through the non-local means filter in model-based reconstructions improves ASL perfusion imaging

Introduction: The arterial spin labeling (ASL) technique is an attractive alternative to contrast-enhanced methods for perfusion imaging [1]. The major disadvantage for ASL is low SNR and low spatial resolution of the resulting images, stemming from the fact that ASL is a subtractive technique with the perfusion signal typically amounting to about 1% of the component signal. The hypothesis of this work is that the SNR and spatial resolution of perfusion images acquired with ASL can be improved by incorporating side information from high-SNR anatomical images into model-based reconstructions of the data.