144 research outputs found
Denoising of 3D magnetic resonance images using non-local PCA and Transform-Domain Filter
The Magnetic Resonance Imaging (MRI) technologyused in clinical diagnosis demands high Peak Signal-to-Noise ratio(PSNR) and improved resolution for accurate analysis and treatmentmonitoring. However, MRI data is often corrupted by random noisewhich degrades the quality of Magnetic Resonance (MR) images.Denoising is a paramount challenge as removing noise causesreduction in the fine details of MRI images. We have developed anovel algorithm which employs Principal Component Analysis(PCA) decomposition and Wiener filtering. We have proposed a twostage approach. In first stage, non-local PCA thresholding is appliedon noisy image and second stage uses Wiener filter over this filteredimage. Our algorithm is implemented using MATLAB andperformance is measured via PSNR. The proposed approach hasalso been compared with related state-of-art methods. Moreover, wepresent both qualitative and quantitative results which prove thatproposed algorithm gives superior denoising performance
Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord
Background: To investigate the effect of using a Rician nonlocal means (NLM) filter on quantification of diffusion tensor (DT)- and diffusion kurtosis (DK)-derived metrics in various anatomical regions of the human brain and the spinal cord, when combined with a constrained linear least squares (CLLS) approach. /
Methods: Prospective brain data from 9 healthy subjects and retrospective spinal cord data from 5 healthy subjects from a 3 T MRI scanner were included in the study. Prior to tensor estimation, registered diffusion weighted images were denoised by an optimized blockwise NLM filter with CLLS. Mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA), were determined in anatomical structures of the brain and the spinal cord. DTI and DKI metrics, signal-to-noise ratio (SNR) and Chi-square values were quantified in distinct anatomical regions for all subjects, with and without Rician denoising. /
Results: The averaged SNR significantly increased with Rician denoising by a factor of 2 while the averaged Chi-square values significantly decreased up to 61% in the brain and up to 43% in the spinal cord after Rician NLM filtering. In the brain, the mean MK varied from 0.70 (putamen) to 1.27 (internal capsule) while AK and RK varied from 0.58 (corpus callosum) to 0.92 (cingulum) and from 0.70 (putamen) to 1.98 (corpus callosum), respectively. In the spinal cord, FA varied from 0.78 in lateral column to 0.81 in dorsal column while MD varied from 0.91βΓβ10β3 mm2/s (lateral) to 0.93βΓβ10β3 mm2/s (dorsal). RD varied from 0.34βΓβ10β3 mm2/s (dorsal) to 0.38βΓβ10β3 mm2/s (lateral) and AD varied from 1.96βΓβ10β3 mm2/s (lateral) to 2.11βΓβ10β3 mm2/s (dorsal). /
Conclusions: Our results show a Rician denoising NLM filter incorporated with CLLS significantly increases SNR and reduces estimation errors of DT- and KT-derived metrics, providing the reliable metrics estimation with adequate SNR levels
Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific
parameters -- such as the apparent transverse relaxation rate R2*, the
longitudinal relaxation rate R1 and the magnetisation transfer saturation --
that can be compared across sites and scanners and carry important information
about the underlying microstructure. The multi-parameter mapping (MPM) protocol
takes advantage of multi-echo acquisitions with variable flip angles to extract
these parameters in a clinically acceptable scan time. In this context,
ESTATICS performs a joint loglinear fit of multiple echo series to extract R2*
and multiple extrapolated intercepts, thereby improving robustness to motion
and decreasing the variance of the estimators. In this paper, we extend this
model in two ways: (1) by introducing a joint total variation (JTV) prior on
the intercepts and decay, and (2) by deriving a nonlinear maximum \emph{a
posteriori} estimate. We evaluated the proposed algorithm by predicting
left-out echoes in a rich single-subject dataset. In this validation, we
outperformed other state-of-the-art methods and additionally showed that the
proposed approach greatly reduces the variance of the estimated maps, without
introducing bias.Comment: 11 pages, 2 figures, 1 table, conference paper, accepted at MICCAI
202
Fast Separable Non-Local Means
We propose a simple and fast algorithm called PatchLift for computing
distances between patches (contiguous block of samples) extracted from a given
one-dimensional signal. PatchLift is based on the observation that the patch
distances can be efficiently computed from a matrix that is derived from the
one-dimensional signal using lifting; importantly, the number of operations
required to compute the patch distances using this approach does not scale with
the patch length. We next demonstrate how PatchLift can be used for patch-based
denoising of images corrupted with Gaussian noise. In particular, we propose a
separable formulation of the classical Non-Local Means (NLM) algorithm that can
be implemented using PatchLift. We demonstrate that the PatchLift-based
implementation of separable NLM is few orders faster than standard NLM, and is
competitive with existing fast implementations of NLM. Moreover, its denoising
performance is shown to be consistently superior to that of NLM and some of its
variants, both in terms of PSNR/SSIM and visual quality
A CURE for noisy magnetic resonance images: Chi-square unbiased risk estimation
In this article we derive an unbiased expression for the expected
mean-squared error associated with continuously differentiable estimators of
the noncentrality parameter of a chi-square random variable. We then consider
the task of denoising squared-magnitude magnetic resonance image data, which
are well modeled as independent noncentral chi-square random variables on two
degrees of freedom. We consider two broad classes of linearly parameterized
shrinkage estimators that can be optimized using our risk estimate, one in the
general context of undecimated filterbank transforms, and another in the
specific case of the unnormalized Haar wavelet transform. The resultant
algorithms are computationally tractable and improve upon state-of-the-art
methods for both simulated and actual magnetic resonance image data.Comment: 30 double-spaced pages, 11 figures; submitted for publicatio
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