Model‐based denoising in diffusion‐weighted imaging using generalized spherical deconvolution

Sperl, Jonathan I.; Sprenger, Tim; Tan, Ek Tsoon; Menzel, Marion I.; Hardy, Christopher J.; Marinelli, Luca

doi:10.1002/mrm.26626

Cited by 15 publications

(20 citation statements)

References 47 publications

(73 reference statements)

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“…The "PCA‐only" denoising method employed a random matrix method and used "dwidenoise" in MRtrix3 (http://www.mrtrix.org). The "genSD" denoising method implemented a generalized spherical deconvolution technique in MatLab (MathWorks, Natick, MA) . In the novel combined "PCA + genSD" denoising method, images were processed with both the PCA and genSD denoising methods, applied sequentially.…”

Section: Methodsmentioning

confidence: 99%

“…The "genSD" denoising method implemented a generalized spherical deconvolution technique in MatLab (MathWorks, Natick, MA). 24 In the novel combined "PCA + genSD" denoising method, images were processed with both the PCA and genSD denoising methods, applied sequentially. Processing time of each individual dataset was 16 seconds and 27 seconds for the "PCA" denoising and the "genSD" denoising techniques, respectively, using an iMac 2017 equipped with a qual-core 4.2 GHz Intel Core i7 CPU and 32 gigabytes of memory.…”

Section: Imagingmentioning

confidence: 99%

“…On the other hand, diffusion encoding space (q‐space) model‐based denoising techniques, such as generalized spherical deconvolution (genSD), explicitly assume diffusivity but make no assumptions about the local, thermal noise characteristics. In the brain, genSD has been shown to effectively reduce scan times by 4‐fold using just 30 DWI scans in comparison to 140 conventional DWI scans, enabling reliable evaluation of white matter plaques in multiple sclerosis . Like PCA, genSD is also applied to magnitude images, which obviates the need for phase‐sensitive reconstruction.…”

mentioning

confidence: 99%

“…In the brain, genSD has been shown to effectively reduce scan times by 4-fold using just 30 DWI scans in comparison to 140 conventional DWI scans, enabling reliable evaluation of white matter plaques in multiple sclerosis. 24 Like PCA, genSD is also applied to magnitude images, which obviates the need for phase-sensitive reconstruction. Hence, model-based genSD may be complementary to model-independent PCA as a denoising method.…”

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confidence: 99%

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Denoising of diffusion MRI improves peripheral nerve conspicuity and reproducibility

Sneag

Zochowski

Tan

et al. 2019

Magnetic Resonance Imaging

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Background Quantitative diffusion MRI is a promising technique for evaluating peripheral nerve integrity but low signal‐to‐noise ratio (SNR) can impede measurement accuracy. Purpose To evaluate principal component analysis (PCA) and generalized spherical deconvolution (genSD) denoising techniques to improve within‐subject reproducibility and peripheral nerve conspicuity. Study Type Prospective. Subjects Seven healthy volunteers and three peripheral neuropathy patients. Field Strength/Sequence 3T/multiband single‐shot echo planar diffusion sequence using multishell 55‐direction scheme. Assessment Images were processed using four methods: "original" (no denoising), "average" (10 repetitions), "PCA‐only," and "PCA + genSD." Tibial and common peroneal nerve segmentations and masks were generated from volunteer diffusion data. Quantitative (SNR and contrast‐to‐noise ratio [CNR]) values were calculated. Three radiologists qualitatively evaluated nerve conspicuity for each method. The two denoising methods were also performed in three patients with peripheral neuropathies. Statistical Tests For healthy volunteers, calculations included SNR and CNRFA (computed using FA values). Coefficient of variation (CV%) of CNRFA quantified within‐subject reproducibility. Groups were compared with two‐sample t‐tests (significance P < 0.05; two‐tailed, Bonferroni‐corrected). Odds ratios (ORs) quantified the relative rates of each of three radiologists confidently identifying a nerve, per slice, for the four methods. Results "PCA + genSD" yielded the highest SNR (meanoverall = 14.83 ± 1.99) and tibial and common peroneal nerve CNRFA (meantibial = 3.45, meanperoneal = 2.34) compared to "original" (P SNR < 0.001; P CNR = 0.011) and "PCA‐only" (P SNR < 0.001, P CNR < 0.001). "PCA + genSD" had higher within‐subject reproducibility (low CV%) for tibial (6.04 ± 1.98) and common peroneal nerves (8.27 ± 2.75) compared to "original" and "PCA‐only." The mean FA was higher for "original" than "average" (P < 0.001), but did not differ significantly between "average" and "PCA + genSD" (P = 0.14). "PCA + genSD" had higher tibial and common peroneal nerve conspicuity than "PCA‐only" (ORtibial = 2.50, P < 0.001; ORperoneal = 1.86, P < 0.001) and "original" (ORtibial = 2.73, P < 0.001; ORperoneal = 2.43, P < 0.001). Data Conclusion PCA + genSD denoising method improved SNR, CNRFA, and within‐subject reproducibility (CV%) without biasing FA and nerve conspicuity. This technique holds promise for facilitating more reliable, unbiased diffusion measurements of peripheral nerves. Level of Evidence: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1128–1137.

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Section: Methodsmentioning

confidence: 99%

Section: Imagingmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Denoising of diffusion MRI improves peripheral nerve conspicuity and reproducibility

Sneag

Zochowski

Tan

et al. 2019

Magnetic Resonance Imaging

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“…Consequently, methods that better correlate with human ratings are preferred. Considering typical usage of NR methods, limited to 2D images, NORMIQA can be applied to select best performing denoising techniques, approaches for correcting artifacts, or image reconstruction solutions . Also, the method can be used to support subjective tests with human observers since such an assessment is often unrepeatable.…”

Section: Discussionmentioning

confidence: 99%

No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

Oszust

Piórkowski

Obuchowicz

2020

Magnetic Resonance in Med

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Purpose: Subjective quality assessment of displayed magnetic resonance (MR) images plays a key role in diagnosis and the resultant treatment. Therefore, this study aims to introduce a new no-reference (NR) image quality assessment (IQA) method for the objective, automatic evaluation of MR images and compare its judgments with those of similar techniques. Methods: A novel NR-IQA method was developed. The method uses a sequence of scaled images filtered to enhance high-frequency components and preserve lowfrequency parts. Since the human visual system (HVS) is sensitive to local image variations and local features often mimic the attraction of the HVS to high-frequency image regions, they were detected in the filtered images and described. Then, the statistics of obtained descriptors were used to build a quality model via the Support Vector Regression method. Results: The method was compared with 21 state-of-the-art techniques for NR-IQA on a new dataset of 70 distorted MR images assessed by 31 experienced radiologists, using typical evaluation criteria for the comparison of NR measures. The introduced method significantly outperforms the compared approaches, in terms of the correlation with human judgments. Conclusions: It is demonstrated that the presented NR-IQA method for the assessment of MR images is superior to the state-of-the-art NR techniques. The method would be beneficial for a wide range of image processing applications, assessing their outputs and affecting the directions of their development. K E Y W O R D Shigh-boost filtering, image quality assessment, local features, magnetic resonance images, no-reference, subjective tests

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Denoising and Multiple Tissue Compartment Visualization of Multi‐b‐Valued Breast Diffusion MRI

Tan

Wilmes

Joe

et al. 2020

Magnetic Resonance Imaging

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Background Multi‐b‐valued/multi‐shell diffusion provides potentially valuable metrics in breast MRI but suffers from low signal‐to‐noise ratio and has potentially long scan times. Purpose To investigate the effects of model‐based denoising with no loss of spatial resolution on multi‐shell breast diffusion MRI; to determine the effects of downsampling on multi‐shell diffusion; and to quantify these effects in multi‐b‐valued (three directions per b‐value) acquisitions. Study Type Prospective (“fully‐sampled” multi‐shell) and retrospective longitudinal (multi‐b). Subjects One normal subject (multi‐shell) and 10 breast cancer subjects imaging at four timepoints (multi‐b). Field Strength/Sequence 3T multi‐shell acquisition and 1.5T multi‐b acquisition. Assessment The “fully‐sampled” multi‐shell acquisition was retrospectively downsampled to determine the bias and error from downsampling. Mean, axial/parallel, radial diffusivity, and fractional anisotropy (FA) were analyzed. Denoising was applied retrospectively to the multi‐b‐valued breast cancer subject dataset and assessed subjectively for image noise level and tumor conspicuity. Statistical Tests Parametric paired t‐test (P < 0.05 considered statistically significant) on mean and coefficient of variation of each metric—the apparent diffusion coefficient (ADC) from all b‐values, fast ADC, slow ADC, and perfusion fraction. Paired and two‐sample t‐tests for each metric comparing normal and tumor tissue. Results In the multi‐shell data, denoising effectively suppressed FA (–45% to –78%), with small biases in mean diffusivity (–5% in normal, +23% in tumor, and –4% in vascular compartments). In the multi‐b data, denoising resulted in small biases to the ADC metrics in tumor and normal contralateral tissue (by –3% to +11%), but greatly reduced the coefficient of variation for every metric (by –1% to –24%). Denoising improved differentiation of tumor and normal tissue regions in most metrics and timepoints; subjectively, image noise level and tumor conspicuity were improved in the fast ADC maps. Data Conclusion Model‐based denoising effectively suppressed erroneously high FA and improved the accuracy of diffusivity metrics. Evidence Level 3 Technical Efficacy Stage 1

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Model‐based denoising in diffusion‐weighted imaging using generalized spherical deconvolution

Abstract: The proposed denoising technique termed generalized spherical deconvolution has the potential of significantly improving image quality in diffusion MRI. Magn Reson Med 78:2428-2438, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Cited by 15 publications

References 47 publications

Denoising of diffusion MRI improves peripheral nerve conspicuity and reproducibility

Denoising of diffusion MRI improves peripheral nerve conspicuity and reproducibility

No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

Denoising and Multiple Tissue Compartment Visualization of Multi‐b‐Valued Breast Diffusion MRI

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