A diffusion model‐free framework with echo time dependence for free‐water elimination and brain tissue microstructure characterization

Molina-Romero, Miguel; Gómez, Pedro A.; Sperl, Jonathan I.; Czisch, Michael; Sämann, Philipp G.; Jones, Derek K.; Menzel, Marion I.; Menze, Bjoern H.

doi:10.1002/mrm.27181

Cited by 12 publications

(8 citation statements)

References 58 publications

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“…This allows separating specific signals (e.g. pulsation artifacts in fMRI [11], free water in DWI [13]) that are difficult to specify a numerical model. 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.…”

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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“…Manual binning becomes increasingly challenging in even higher-dimensional spaces and should preferably be implemented as some automatic data-driven approach incorporating information from multiple voxels, for instance building on the recent works in Refs. [90][91][92].…”

Section: Discussionmentioning

confidence: 99%

Massively Multidimensional Diffusion-Relaxation Correlation MRI

et al. 2022

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Diverse approaches such as oscillating gradients, tensor-valued encoding, and diffusion-relaxation correlation have been used to study microstructure and heterogeneity in healthy and pathological biological tissues. Recently, acquisition schemes with free gradient waveforms exploring both the frequency-dependent and tensorial aspects of the encoding spectrum b(ω) have enabled estimation of nonparametric distributions of frequency-dependent diffusion tensors. These “D(ω)-distributions” allow investigation of restricted diffusion for each distinct component resolved in the diffusion tensor trace, anisotropy, and orientation dimensions. Likewise, multidimensional methods combining longitudinal and transverse relaxation rates, R1 and R2, with (ω-independent) D-distributions capitalize on the component resolution offered by the diffusion dimensions to investigate subtle differences in relaxation properties of sub-voxel water populations in the living human brain, for instance nerve fiber bundles with different orientations. By measurements on an ex vivo rat brain, we here demonstrate a “massively multidimensional” diffusion-relaxation correlation protocol joining all the approaches mentioned above. Images acquired as a function of the magnitude, normalized anisotropy, orientation, and frequency content of b(ω), as well as the repetition time and echo time, yield nonparametric D(ω)-R1-R2-distributions via a Monte Carlo data inversion algorithm. The obtained per-voxel distributions are converted to parameter maps commonly associated with conventional lower-dimensional methods as well as unique statistical descriptors reporting on the correlations between restriction, anisotropy, and relaxation.

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“… Data‐driven regularization, where a fixed number of correlation spectra

are assumed within the image. 117 , 118 This approach is related to the previously mentioned blind source‐separation methods 105 , 106 , 107 and seeks a lower‐dimensional spectral representation of the image that is supported by the data, effectively regularizing the inversion by sharing information across voxels. This approach is appropriate when seeking to discover prominent microstructural features, at the expense of estimating spectra in every voxel.…”

Section: Discussionmentioning

confidence: 99%

Combined diffusion‐relaxometry microstructure imaging: Current status and future prospects

Slator

Palombo

Miller

et al. 2021

Magnetic Resonance in Med

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A diffusion model‐free framework with echo time dependence for free‐water elimination and brain tissue microstructure characterization

Abstract: Formulation of the diffusion-relaxation dependence as a BSS problem introduces a new framework for studying microstructure compartmentalization, and a novel tool for free-water elimination.

Cited by 12 publications

References 58 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

Massively Multidimensional Diffusion-Relaxation Correlation MRI

Combined diffusion‐relaxometry microstructure imaging: Current status and future prospects

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