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Adaptive filtering for high resolution magnetic resonance images

We present a study of least mean square (LMS) based adaptive filters for high resolution magnetic resonance (MR) images to improve signal‐to‐noise ratio (SNR) while maintaining sharp edges. Five variations of a new technique that senses the type of noise or the presence of an edge in the filtering window, called adaptive filtering with noise estimation (AFEN) are presented and compared with the basic two‐dimensional LMS (TDLMS) algorithm, adaptive filtering with a mean estimator (AFLME), a two‐dimensional averaged LMS (TDALMS) algorithm, and a two‐dimensional median weighted LMS (TDMLMS) algorithm. Although TDLMS, TDALMS, and TDMLMS filters give better SNR improvement when applied uniformly to an image, they significantly blur edges. The AFLME and AFEN filters both show approximately a factor of 2 SNR improvement with much better retention of edges, with AFEN showing slightly better performance for both SNR and edge sharpness in phantom and in vivo inner ear images.