Multispectral MRI De-noising Using Non-Local Means

Clinical MRI data is normally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. For this reason, de-noising methods are often applied to increase the SNR and improve image quality. Most of these methods work on single channel images by correcting each grey level using an implicit model of the surrounding region, but without taking into consideration the potential multispectral nature of MR images. In this paper we present an extension of a recently proposed filter to reduce random noise in multispectral MR images and test it on synthetic and real images. We compare performance to a multispectral approach based upon the imaging physics and published previously at this conference using real data. We conclude from our results that these methods can be used for de-noising of MR data.