Superiority of 3D wavelet-packet denoising in MR microscopy

Three dimensional Magnetic Resonance Imaging (MRI) datasets are becoming increasingly important in clinical and research applications because of their inherent signal to noise (SNR) advantages, high resolution and isotropic voxels. Despite SNR advantages, some 3D acquisitions may be SNR-limited, particularly in MR microscopy. Historically, both classic filtering and wavelet-based denoising techniques have been performed on a slice-by-slice basis. In principle, adaptive techniques such as best- basis wavelet-packet denoising might offer inherent advantages when performed in 3D, instead of 2D, by tracking through plane “structure” and suppressing noise “pseudostructure.” This hypothesis was tested in 10 volumetric MR microscopy datasets from several different MR microscopy atlas projects. 3D wavelet-packet denoised images consistently yielded lower minimum mean-square error and subjectively perceived noise power than corresponding 2D denoised images using otherwise identical algorithms and parameters. MR microscopy researchers preferred the denoised images to the unprocessed images for their atlas projects.