Rician noise reduction in magnetic resonance images using adaptive non-local mean and guided image filtering

Mahmood, Muhammad Tariq; Chu, Yeon-Ho; Choi, Young-Kyu

doi:10.1007/s10043-016-0220-z

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Their worse performance compared with the GS function may be attributed to a lack of strong correlations between similar patches in the brain MRI scans. 3) The MRI data collected in the spatial frequency space are usually corrupted by Gaussian noise ( Mahmood et al, 2016 ), and GS can help reduce the effect of Gaussian noise by exploiting the spatial correlations. Therefore, the nonlocal method with weight setting by the GS function always obtained the best results.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD

Huang

Deng

et al. 2019

NeuroImage: Clinical

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Potential biomarker detection is a crucial area of study for the prediction, diagnosis, and monitoring of Alzheimer's disease (AD). The voxelwise genome-wide association study (vGWAS) is widely used in imaging genomics studies that is usually applied to the detection of AD biomarkers in both imaging and genetic data. However, performing vGWAS remains a challenge because of the computational complexity of the technique and our ignorance of the spatial correlations within the imaging data. In this paper, we propose a novel method based on the exploitation of spatial correlations that may help to detect potential AD biomarkers using a fast vGWAS. To incorporate spatial correlations, we applied a nonlocal method that supposed that a given voxel could be represented by weighting the sum of the other voxels. Three commonly used weighting methods were adopted to calculate the weights among different voxels in this study. Then, a fast vGWAS approach was used to assess the association between the image and the genetic data. The proposed method was estimated using both simulated and real data. In the simulation studies, we designed a set of experiments to evaluate the effectiveness of the nonlocal method for incorporating spatial correlations in vGWAS. The experiments showed that incorporating spatial correlations by the nonlocal method could improve the detecting accuracy of AD biomarkers. For real data, we successfully identified three genes, namely, ANK3, MEIS2, and TLR4, which have significant associations with mental retardation, learning disabilities and age according to previous research. These genes have profound impacts on AD or other neurodegenerative diseases. Our results indicated that our method might be an effective and valuable tool for detecting potential biomarkers of AD.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD

Huang

Deng

et al. 2019

NeuroImage: Clinical

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“…In addition to Fourier-domain and PCA based processing, it has been shown that DWI and DTI data can be denoised with other signal processing algorithms, ranging from wavelet-based smoothing [ 54 ], to the anisotropic diffusion filtering technique [ 55 , 56 ], to adaptive smoothing [ 57 – 59 ], to non-local means variants [ 60 ], to patch-based analysis [ 61 ], to rank constraints [ 62 ], to singular value decomposition [ 63 ], to sparseness and self-similarity based denoising [ 64 ]. These advanced methods were not evaluated in our current study.…”

Section: Discussionmentioning

confidence: 99%

A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI

et al. 2018

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Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses.

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“…The denoising method becomes adaptive, i.e., the adaptive NLM (ANLM), when an NLM method adapts its h parameter values based on the characteristics of the image. 22,26,29,36,37 Previous studies have suggested that the optimal values for h could be selected to be proportional to the noise variation. 22,25 For an image with an unknown true noise variation, the noise level, σðx i Þ, at voxel x i , could be estimated by computing the minimal Euclidean distance of the weighted average of all blocks neighboring the voxel x i E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 4 ; 3 2 6 ; 6 0 9…”

Section: Basics Of Nonlocal Means-based Denoisingmentioning

confidence: 99%

“…15,21,22,27,28 To reduce image noise while preserving structural information, both the image noise and the anatomical information in the acquired image should be considered together. [29][30][31] Prior algorithms adjusted the denoising filter strength according to the measured local structural information, the percentage of the maximum intensity level, the dominance of wavelets, and principle components, or visual assessments. 20,21,26,31 In the wavelet-NLM filter algorithm, 15 the denoising parameter is adapted over the different spatial frequency resolutions of the image.…”

Section: Introductionmentioning

confidence: 99%

Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI

et al. 2017

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, "Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI," J. Med. Imag. 4(3), 034004 (2017), doi: 10.1117/1.JMI.4.3.034004. Abstract. Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i.e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i.e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography.

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Rician noise reduction in magnetic resonance images using adaptive non-local mean and guided image filtering

Cited by 6 publications

References 20 publications

Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD

Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD

A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI

Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI

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