Accelerated three‐dimensional multispectral MRI with robust principal component analysis for separation of on‐ and off‐resonance signals

Levine, Evan; Stevens, Kathryn J.; Beaulieu, Christopher; Hargreaves, Brian A.

doi:10.1002/mrm.26819

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Among various BSS algorithms, robust principal component analysis (rPCA) has been introduced for separation of background signal This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ in dynamic MRI [14], on/off-resonance signal representation in multispectral imaging [15] and elimination of MR artifacts [16], [17]. Based on singular value decomposition (SVD) analysis, rPCA extracts redundant signal sources using low-rank property (or fixed-rank property) and encourages the sparsity of the residual signal.…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging

Song

Shin

Lee

et al. 2020

IEEE Trans. Med. Imaging

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In conventional gradient-echo myelin water imaging (GRE-MWI), myelin water fraction (MWF) is estimated by fitting the multi-echo gradient recalled echo (mGRE) signal to a pre-assumed numerical model (e.g., multi-component exponential curves or three component exponential curves). However, in mGRE, imaging artifacts (e.g., voxel spread function and physiological noise) and noise render the signal to deviate from the numerical model, leading to misfit of the model parameters. Here, as an alternative to the model-based GRE-MWI, a blind source separation (BSS) technique for the separation of multi-exponential mGRE signal is proposed. Among the various BSS techniques, a modified robust principal component analysis (rPCA) is presented to separate signal sources by enforcing the data-driven properties such as "low rankness" and "sparsity." Considering the signal evolution of T * 2 relaxation (i.e., non-negative exponential decay), low rankness of exponential decay was enforced by nonnegative matrix factorization (NMF) and hankelization. This method provides the separation of slow-decaying, fast-decaying exponential components and artifact components from mGRE images. After the separation, MWF map is reconstructed as the ratio of the fast-decayingcomponent to the total decaying components. The proposed method was demonstrated in numerical simulations and in vivo scans. The method provided a robust estimation of MWF in the presence of statistical noise and imaging artifacts.

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Section: Introductionmentioning

confidence: 99%

Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging

Song

Shin

Lee

et al. 2020

IEEE Trans. Med. Imaging

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“…Partial Fourier reconstruction methods can be used to fill in the missing phase‐encoding lines after the shot‐LLR reconstruction by using the conjugate symmetric property of the k‐space. Similar to the idea of virtual conjugate coils, which is an alternative way of exploiting this property, we generate virtual conjugate shots (VCSs) by flipping and conjugating the acquired data, and treating them as additional shots to avoid the estimation of the low‐resolution phase from the central k‐space data. The decomposition of the LLR matrices with VCS is provided in the Appendix.…”

Section: Theorymentioning

confidence: 99%

Motion‐robust reconstruction of multishot diffusion‐weighted images without phase estimation through locally low‐rank regularization

Levine

Tian

et al. 2018

Magnetic Resonance in Med

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Purpose The goal of this work is to propose a motion robust reconstruction method for diffusion‐weighted MRI that resolves shot‐to‐shot phase mismatches without using phase estimation. Methods Assuming that shot‐to‐shot phase variations are slowly varying, spatial‐shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low‐rank constraint on the spatial‐shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method. Results The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase‐estimation‐based methods to achieve even higher image quality. Conclusion We introduced the shot–locally low‐rank method, a reconstruction technique for multishot diffusion‐weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.

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“…The strength of the differentiation varies according to TSL, and it may be better to use different thresholds for the images at different TSLs. In previous studies (20,32), L+S decomposition was applied to dynamic imaging or multispectral imaging, in which the data did not fit a low-rank only component and therefore the sparse component contains significant energy. L+S decomposition was applied in static imaging in this study, but we found that some image details can still be observed in the sparse component, especially in the cartilage region.…”

mentioning

confidence: 99%

Accelerating the 3D T1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model

Liu¹,

Ying²,

Chen³

et al. 2021

Quant Imaging Med Surg

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Background: Magnetic resonance (MR) quantitative T 1ρ imaging has been increasingly used to detect the early stages of osteoarthritis. The small volume and curved surface of articular cartilage necessitate imaging with high in-plane resolution and thin slices for accurate T 1ρ measurement. Compared with 2D T 1ρ mapping, 3D T 1ρ mapping is free from artifacts caused by slice cross-talk and has a thinner slice thickness and full volume coverage. However, this technique needs to acquire multiple T 1ρ -weighted images with different spin-lock times, which results in a very long scan duration. It is highly expected that the scan time can be reduced in 3D T 1ρ mapping without compromising the T 1ρ quantification accuracy and precision.Methods: To accelerate the acquisition of 3D T 1ρ mapping without compromising the T 1ρ quantification accuracy and precision, a signal-compensated robust tensor principal component analysis method was proposed in this paper. The 3D T 1ρ -weighted images compensated at different spin-lock times were decomposed as a low-rank high-order tensor plus a sparse component. Poisson-disk random undersampling patterns were applied to k-space data in the phase-and partition-encoding directions in both retrospective and prospective experiments. Five volunteers were involved in this study. The fully sampled k-space data acquired from 3 volunteers were retrospectively undersampled at R=5.2, 7.7, and 9.7, respectively.Reference values were obtained from the fully sampled data. Prospectively undersampled data for R=5 and R=7 were acquired from 2 volunteers. Bland-Altman analyses were used to assess the agreement between the accelerated and reference T 1ρ measurements. The reconstruction performance was evaluated using the normalized root mean square error and the median of the normalized absolute deviation (MNAD) of the reconstructed T 1ρ -weighted images and the corresponding T 1ρ maps.Results: T 1ρ parameter maps were successfully estimated from T 1ρ -weighted images reconstructed using the proposed method for all accelerations. The accelerated T 1ρ measurements and reference values were in good agreement for R=5.2 (T 1ρ : 40.4±1.4 ms), R=7.7 (T 1ρ : 40.4±2.1 ms), and R=9.7 (T 1ρ : 40.9±2.2 ms) in the Bland-Altman analyses. The T 1ρ parameter maps reconstructed from the prospectively undersampled data also showed promising image quality using the proposed method. Conclusions:The proposed method achieves the 3D T 1ρ mapping of in vivo knee cartilage in eight minutes using a signal-compensated robust tensor principal component analysis method in image reconstruction.

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Accelerated three‐dimensional multispectral MRI with robust principal component analysis for separation of on‐ and off‐resonance signals

Abstract: Three-dimensional multispectral imaging can be highly accelerated by varying undersampling between bins and separating on- and off-resonance. Magn Reson Med 79:1495-1505, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Cited by 5 publications

References 23 publications

Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging

Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging

Motion‐robust reconstruction of multishot diffusion‐weighted images without phase estimation through locally low‐rank regularization

Accelerating the 3D T1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model

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