Faculty Publications
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Item Nonlocal linear minimum mean square error methods for denoising MRI(Elsevier Ltd, 2015) Sudeep, P.V.; Ponnusamy, P.; Kesavadas, C.; Rajan, J.The presence of noise results in quality deterioration of magnetic resonance (MR) images and thus limits the visual inspection and influence the quantitative measurements from the data. In this work, an efficient two stage linear minimum mean square error (LMMSE) method is proposed for the enhancement of magnitude MR images in which data in the presence of noise follows a Rician distribution. The conventional Rician LMMSE estimator determines a closed-form analytical solution to the aforementioned inverse problem. Even-though computationally efficient, this approach fails to take advantage of data redundancy in the 3D MR data and hence leads to a suboptimal filtering performance. Motivated by this observation, we put forward the concept of nonlocal implementation with LMMSE estimation method. To select appropriate samples for the nonlocal version of the LMMSE estimation, the similarity weights are computed using Euclidean distance between either the gray level values in the spatial domain or the coefficients in the transformed domain. Assuming that the signal dependent component of the noise is optimally suppressed by this filtering and the rest is a white and uncorrelated noise with the image, we adopt a second stage LMMSE filtering in the principal component analysis (PCA) domain to further enhance the image and the noise variance is adaptively adjusted. Experiments on both simulated and real data show that the proposed filters have excellent filtering performance over other state-of-the-art methods. © 2015 Elsevier Ltd. All rights reserved.Item A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps(Springer London, 2017) Sudeep, P.V.; Ponnusamy, P.; Kesavadas, C.; Sijbers, J.; den Dekker, A.J.; Rajan, J.A phase map can be obtained from the real and imaginary components of a complex valued magnetic resonance (MR) image. Many applications, such as MR phase velocity mapping and susceptibility mapping, make use of the information contained in the MR phase maps. Unfortunately, noise in the complex MR signal affects the measurement of parameters related to phase (e.g, the phase velocity). In this paper, we propose a nonlocal maximum likelihood (NLML) estimation method for enhancing phase maps. The proposed method estimates the true underlying phase map from a noisy MR phase map. Experiments on both simulated and real data sets indicate that the proposed NLML method has a better performance in terms of qualitative and quantitative evaluations when compared to state-of-the-art methods. © 2016, Springer-Verlag London.Item An improved nonlocal maximum likelihood estimation method for denoising magnetic resonance images with spatially varying noise levels(Elsevier B.V., 2020) Sudeep, P.V.; Ponnusamy, P.; Kesavadas, C.; Rajan, J.Magnetic resonance images (MRI) reconstructed with parallel MRI (pMRI) techniques generally have spatially varying (non-stationary) noise levels. However, most of the existing MRI denoising methods rely on a stationary noise model and end with suboptimal results when applied to pMRI images. To address this problem, this paper proposes an improved nonlocal maximum likelihood (NLML) estimation method. In the proposed method, a noise map is computed with a robust noise estimator before the ML estimation of the underlying signal. Also, a similarity measure based on local frequency descriptors (LFD) is introduced to find the nonlocal samples for ML estimation. The experiments on simulated and real magnetic resonance (MR) data demonstrate that the proposed technique has superior filtering capabilities in terms of subjective and quantitative assessments when compared with other state-of-the-art methods. © 2018 Elsevier B.V.