De-noising of real-time dynamic magnetic resonance images by the combined application of karhunen-loeve transform (KLT) and wavelet filtering

摘要

A hybrid filtering method called Karhunen Loeve Transform-Wavelet (KW) filtering is presented to de-noise dynamic cardiac magnetic resonance images that simultaneously takes advantage of the intrinsic spatial and temporal redundancies of real-time cardiac cine. This filtering technique combines a temporal Karhunen-Loeve transform (KLT) and spatial adaptive wavelet filtering. KW filtering has four steps. The first is applying the KLT along the temporal direction, generating a series of “eigenimages”. The second is applying Marcenko-Pastur (MP) law to identify and discard the noise-only eigenimages. The third applying a 2-D spatial wavelet filter with adaptive threshold to each eigenimage to define the wavelet filter strength for each of the eigenimages based on the noise variance and standard deviation of the signal. Lastly, the inverse KLT is applied to the filtered eigenimages to generate a new series of cine images with reduced image noise.

主权项

1. A method of providing a Karhunen-Love Transform Wavelet (KW) Filter to suppress random noise in an image series, comprising:
applying a Karhunen-Loeve Transform (KLT) to the image series to compress signal information into a finite set of frames, the KLT being applied to a temporal domain to convert a temporal series of frames into a series of eigenimages having eigenvalues, wherein the signal information is concentrated into a subset of eigenimages in accordance with their respective eigenvalues; discarding substantially noise-only frames; applying a Wavelet filter with an adaptive threshold to remaining frames; and applying an inverse KLT to obtain the de-noised image series.