MRI denoising

MRI denoising package with 5 denoising filters and an automatic noise estimation (here)

In this section, several MRI denoising filters and their corresponding codes are proposed. Each of these filters is dedicated to a specific use. The first one, the ONLM filter works on traditional Gaussian and Rician stationary noise models. The adaptive ONLM (ANLM) filter deals with non stationary Gaussian and Rician noise models and should be used for parallel imaging (e.g., SENSE). Finally, the PRINLM and the MR-ONLM filters are improved version of the ONLM filter especially efficient for high level of noise. The PRINLM filter is the fastest proposed implementation and thus it is a good choice for processing large database.

The denoising performance of these filters is studied in corresponding journal papers. In addition, the impact of the proposed MRI denoising filter on tissue segmentation and cortical surface extraction has been also investigated.

Optimized nonlocal means filter for 3D MRI (ONLM)

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, , 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation.

• • Matlab C-MEX

  • C++ implementation as MINC tool

  • C++ implementation as MedINRIA plug-in

  • C++ implementation as ITK tool

Reference: P. Coupé, P. Yger, S. Prima, P. Hellier, C. Kervrann, C. Barillot. An Optimized Blockwise NonLocal Means Denoising Filter for 3-D Magnetic Resonance Images. IEEE Transactions on Medical Imaging, 27(4):425–441, 2008.

Spatially adaptive nonlocal means filter for 3D MRI (ANLM)

Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method.The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases. The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images.

• • Matlab C-MEX as VBM8 plug-in (SPM extension):

  • C++ implementation as ITK tool

Reference: J. V. Manjon, P. Coupé, L. Martí-Bonmatí, D. L. Collins, M. Robles. Adaptive Non-Local Means Denoising of MR Images with Spatially Varying Noise Levels. Journal of Magnetic Resonance Imaging, 31(1):192–203, 2010.

Denoising based on Sparseness and Self-Similarity for 3D MRI (PRINLM)

This paper proposes two new methods for the three-dimensional denoising of magnetic resonance images that exploit the sparseness and self-similarity properties of the images. The proposed methods are based on a three-dimensional moving-window discrete cosine transform hard thresholding and a three-dimensional rotationally invariant version of the well-known nonlocal means filter. The proposed approaches were compared with related state-of-the-art methods and produced very competitive results. Both methods run in less than a minute, making them usable in most clinical and research settings.

• • Matlab C-MEX code

Reference: J. V. Manjon, P. Coupé, A. Buades, D. L. Collins, M. Robles. New Methods for MRI Denoising based on Sparseness and Self-Similarity. Medical Image Analysis, 16(1): 18-27, 2012.

Multiresolution Non-local Means Filter for 3D MRI (MR-ONLM)

In this paper, an adaptive multiresolution version of the Blockwise Non-Local (NL-) means filter is presented for 3D Magnetic Resonance (MR) images. Based on an adaptive soft wavelet coefficient mixing, the proposed filter implicitly adapts the amount of denoising according to the spatial and frequency information contained in the image. Two versions of the filter are described for Gaussian and Rician noise. Quantitative validation was carried out on Brainweb datasets by using several quality metrics. The results show that the proposed multiresolution filter obtained competitive performance compared to recently proposed Rician NL-means filters. Finally, qualitative experiments on anatomical and Diffusion-Weighted MR images show that the proposed filter efficiently removes noise while preserving fine structures in classical and very noisy cases. The impact of the proposed denoising method on fiber tracking is also presented on a HARDI dataset.

• • Matlab C-MEX code

Reference: P. Coupé, J. V Manjon, M. Robles, D. L. Collins. Adaptive multiresolution non-local means filter for three-dimensional magnetic resonance image denoising. IET Image Processing, 6(5): 558-568, 2012.