Spatially variable Rician noise in magnetic resonance imaging

New Magnetic Resonance (MR) imaging applications include real time monitoring of temperature changes during cardiac radiofrequency ablations. MR-thermometry requires online robust motion compensation to cope with the complex motion of the heart resulting from respiratory activity and cardiac contraction (potentially in presence of arrhythmia), together with the presence of noise in MR images. We propose a method to adaptively and automatically tune parameters of motion compensation algorithms that use robustness function. The core of the method is the estimation of the probability density function (pdf) of the error for each pixel in a reference frame using the Rician noise pdf model in MRI. Then parameter map is derived from estimated pdf. The proposed method leads to better results than using a fixed control parameter of the robustness function, which should facilitate the use of such methods for clinical purpose.

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“…The Rician distribution is dependent on the value of the true signal . To estimate it, we apply the method proposed in [7]:…”

Section: Robust Motion Estimation With Explicit Signal Pdfmentioning

confidence: 99%

Adaptive rejection of outliers for robust motion compensation in cardiac MR-thermometry

Emilien

Benois-Pineau

Elbes

et al. 2013

2013 IEEE International Conference on Image Processing

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“…In fact, several approaches were presented in the literature in order to enhance the general DTI image quality. For instance, some of these DTI filtering methods are based on MRI noise regularization (Aja-Fernandez et al, 2008;Martin-Fernandez et al, 2009;Maximov et al, 2012;Tristán-Vega and Aja-Fernández, 2010), pre-filtering DWI images requested for DTI images reconstruction (Martin-Fernandez et al, 2009;Xu et al, 2010), noise attenuation on k-space (Basu et al, 2006), local neighborhood pattern analysis (Manjón et al, 2008), high-order partial differential equations (Bai and Feng, 2007;Moraschi et al, 2010), diffusion tensor…”

Section: Introductionmentioning

confidence: 99%

Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

et al. 2017

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Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods: A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results: The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions: In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.

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“…As a result, voxel-wise noise estimations from multiple, independent acquisition schemes are preferred over single acquisition methods (22,25). In addition, ROI-based noise estimates are not suitable for spatially varying noise distributions, as may result from the use of parallel imaging, which also requires the noise level to be estimated in a voxel-wise manner (28–31). Since parallel imaging is routinely used to decrease acquisition time, background noise estimators are almost never preferred to voxel-wise methods.…”

Section: Introductionmentioning

confidence: 99%

“…There are a wide variety of noise correction methods available, including those that utilize lookup tables (3), global correction via a single noise correction term (19–21), non-linear maximum likelihood (ML) or maximum a posteriori (MAP) estimation which utilize Bessel functions in the measured signal model (12,25,32), nonlocal maximum likelihood estimation which sample neighboring voxels to determine spatially varying noise statistics (33,34), nonlinear diffusion filtering for spatially varying noise (35), spatially varying noise corrections involving confluent hypergeometric functions (36,37), and multistep weighting strategies to remove errors that can be introduced through the estimated weight factors (38). Although the more complicated noise correction techniques may have desirable theoretical properties under certain circumstances, they can be difficult to implement, require nonlinear optimization, and/or involve iterative fitting algorithms, which can be computationally demanding and may not guarantee a unique solution.…”

Section: Introductionmentioning

confidence: 99%

A simple noise correction scheme for diffusional kurtosis imaging

Glenn¹,

Tabesh²,

Jensen³

2015

Magnetic Resonance Imaging

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Purpose Diffusional kurtosis imaging (DKI) is sensitive to the effects of signal noise due to strong diffusion weightings and higher order modeling of the diffusion weighted signal. A simple noise correction scheme is proposed to remove the majority of the noise bias in the estimated diffusional kurtosis. Methods Weighted linear least squares (WLLS) fitting together with a voxel-wise, subtraction-based noise correction from multiple, independent acquisitions are employed to reduce noise bias in DKI data. The method is validated in phantom experiments and demonstrated for in vivo human brain for DKI-derived parameter estimates. Results As long as the signal-to-noise ratio (SNR) for the most heavily diffusion weighted images is greater than 2.1, errors in phantom diffusional kurtosis estimates are found to be less than 5 percent with noise correction, but as high as 44 percent for uncorrected estimates. In human brain, noise correction is also shown to improve diffusional kurtosis estimates derived from measurements made with low SNR. Conclusion The proposed correction technique removes the majority of noise bias from diffusional kurtosis estimates in noisy phantom data and is applicable to DKI of human brain. Features of the method include computational simplicity and ease of integration into standard WLLS DKI post-processing algorithms.

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Spatially variable Rician noise in magnetic resonance imaging

Cited by 44 publications

References 42 publications

Adaptive rejection of outliers for robust motion compensation in cardiac MR-thermometry

Adaptive rejection of outliers for robust motion compensation in cardiac MR-thermometry

Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

A simple noise correction scheme for diffusional kurtosis imaging

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