Robust Spectral Denoising for Water-Fat Separation in Magnetic Resonance Imaging

Lugauer, Felix; Nickel, Dominik; Wetzl, Jens; Kannengießer, Stephan; Maier, Andreas; Hornegger, Joachim

doi:10.1007/978-3-319-24571-3_80

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…With multi‐echo acquisitions targeted at chemical decomposition being inherently overdetermined in the contrast dimension (i.e. the TE dimension), we propose to make use of an adaptive spectral denoising approach on the complex input data and before estimation of confounding factors and FAC estimation. This denoising approach enforces a spectral low rank constraint locally along the echo dimension in small image patches.…”

Section: Methodsmentioning

confidence: 99%

“…It was shown to reduce noise in the UFA map in the liver of an exemplary clinical trial subject by 30%–40%, without introducing systemic errors in the FAC maps and while preserving edges . The only parameter that has to be chosen is the patch length of the 2‐D blocks, which was set to 5 (as in the work of Lugauer et al). Then, accurate field map ψ and transverse relaxation

R_{2}^{*}

estimates are calculated by means of voxel‐by‐voxel minimization of Equation (10) using the L‐BFGS (Limited‐memory Broyden‐Fletcher‐Goldfarb‐Shanno) algorithm.…”

Section: Methodsmentioning

confidence: 99%

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Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi‐echo MRI

Schneider

Janas

Lugauer

et al. 2018

Magnetic Resonance in Med

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Purpose To develop a bipolar multi‐echo MRI method for the accurate estimation of the adipose tissue fatty acid composition (FAC) using clinically relevant protocols at clinical field strength. Methods The proposed technique jointly estimates confounding factors (field map, R2*, eddy‐current phase) and triglyceride saturation state parameters by fitting multi‐echo gradient echo acquisitions to a complex signal model. The noise propagation behavior was improved by applying a low‐rank enforcing denoising technique and by addressing eddy‐current‐induced phase discrepancies analytically. The impact of the total echo train duration on the FAC parameter map accuracy was analyzed in an oil phantom at 3T. Accuracy and reproducibility assessment was based on in vitro oil phantom measurements at two field strengths (3T and 1.5T) and with two different protocols. Repeatability was assessed in vivo in patients (n = 8) with suspected fatty liver disease using test–retest acquisitions in the abdominal subcutaneous, perirenal and mesenteric fat depots. Results Echo train readout durations of at least five times the conventional in‐phase time were required for accurate FAC estimation in areas of high fat content. In vitro, linear regression and Bland–Altman analyses demonstrated strong (r > 0.94) and significant (P ≪ 0.01) correlations between measured and reference FACs for all acquisitions, with smaller overall intercepts and biases at 3T compared to 1.5T. In vivo, reported mean absolute differences between test and retest were 1.54%, 3.31%, and 2.63% for the saturated, mono‐unsaturated, and poly‐unsaturated fat component, respectively. Conclusions Accurate and reproducible MRI‐based FAC quantification within a breath‐hold is possible at clinical field strengths.

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Section: Methodsmentioning

confidence: 99%

R_{2}^{*}

estimates are calculated by means of voxel‐by‐voxel minimization of Equation (10) using the L‐BFGS (Limited‐memory Broyden‐Fletcher‐Goldfarb‐Shanno) algorithm.…”

Section: Methodsmentioning

confidence: 99%

Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi‐echo MRI

Schneider

Janas

Lugauer

et al. 2018

Magnetic Resonance in Med

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“…For example, the redundancy of information in multiple spin echo images has been used to produce higher quality images and faster acquisition for mono‐exponential

T_{2}

mapping . Further, low rank denoising has been demonstrated effective for improving precision of chemical shift imaging data, 27 and fat fraction maps derived from multiple gradient echo acquisitions. Similarly, principal component analysis (PCA) has been used for denoising diffusion weighted imaging data sets for improved precision of diffusion tensor or other parametric characterizations .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of principal component analysis image denoising on multi‐exponential MRI relaxometry

Does

Olesen

Harkins

et al. 2019

Magnetic Resonance in Med

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Purpose Multi‐exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal‐to‐noise ratio (SNR). This work evaluates the use of principal‐component‐analysis (PCA) denoising to mitigate these SNR demands and improve the precision of relaxometry measures. MethodsPCA denoising was evaluated using both simulated and experimental MRI data. Bi‐exponential transverse relaxation signals were simulated for a wide range of acquisition and sample parameters, and experimental data were acquired from three excised and fixed mouse brains. In both cases, standard relaxometry analysis was performed on both original and denoised image data, and resulting estimated signal parameters were compared. ResultsDenoising reduced the root‐mean‐square‐error of parameters estimated from multi‐exponential relaxometry by factors of ≈3×, for typical acquisition and sample parameters. Denoised images and subsequent parameter maps showed little or no signs of spatial artifact or loss of resolution. Conclusion Experimental studies and simulations demonstrate that PCA denoising of MRI relaxometry data is an effective method of improving parameter precision without sacrificing image resolution. This simple yet important processing step thus paves the way for broader applicability of multi‐exponential MRI relaxometry.

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“…MR images are affected by various types of artifacts including inhomogeneity (field bias), intensity non-standardness, and inherent noises from the acquisition process [19]. The removal of bias generates additional noise, and hence a de-noising filter should be applied after bias correction.…”

Section: Pre-processing Mrismentioning

confidence: 99%

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Anwar,

Irmakci,

Torigian

et al. 2020

Preprint

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Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and research investigations in various conditions such as aging, diabetes mellitus, obesity, metabolic syndrome, and their associated comorbidities. Towards a fully automated, robust, and precise quantification of thigh tissues, herein we designed a novel semi-supervised segmentation algorithm based on deep network architectures. Built upon Tiramisu segmentation engine, our proposed deep networks use variational and specially designed targeted dropouts for faster and robust convergence, and utilize multi-contrast MRI scans as input data. In our experiments, we have used 150 scans from 50 distinct subjects from the Baltimore Longitudinal Study of Aging (BLSA). The proposed system made use of both labeled and unlabeled data with high efficacy for training, and outperformed the current state-of-theart methods with dice scores of 97.52%, 94.61%, 80.14%, 95.93%, and 96.83% for muscle, fat, IMAT, bone, and bone marrow tissues, respectively. Our results indicate that the proposed system can be useful for clinical research studies where volumetric and distributional tissue quantification is pivotal and labeling is a significant issue. To the best of our knowledge, the proposed system is the first attempt at multi-tissue segmentation using a single end-to-end semisupervised deep learning framework for multi-contrast thigh MRI scans.

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Robust Spectral Denoising for Water-Fat Separation in Magnetic Resonance Imaging

Cited by 8 publications

References 9 publications

Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi‐echo MRI

Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi‐echo MRI

Evaluation of principal component analysis image denoising on multi‐exponential MRI relaxometry

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

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