ABSTRACT
RF inhomogeneities are illumination artifacts in MR images which manifest as a multiplicative bias field. To measure the quality of an MR image with respect to RF inhomogeneities, existing multi-valued criteria are in use. Here we propose a useful conversion of these multi-valued criteria into a single measure of quality which simplifies image quality evaluation and comparison. Next, to remove such a bias field, a novel wavelet based approach is employed, that extends a previous 1D wavelet design methodology to a 2D setting. This method is found to perform well on images with strong small details. The results for brain MR images are subject to improvement, however our results hint to a future scenario for improved image quality.
Subject(s)
Brain/pathology , Image Enhancement/methods , Image Processing, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Algorithms , Computer Simulation , Humans , Magnetic Resonance Imaging/instrumentation , Models, Statistical , Phantoms, Imaging , Quality Control , Radio Waves , Subtraction TechniqueABSTRACT
An approach for designing multiwavelets is introduced, for use in cardiac signal processing. The parameterization of the class of multiwavelets is in terms of associated FIR polyphase all-pass filters. Orthogonality and a balanced vanishing moment of order 1 are built into the parameterization. An optimization criterion is developed to associate the wavelets with different meaningful segments of a signal. This approach is demonstrated on the simultaneous detection of QRS-complexes and T-peaks in ECG signals.
