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  • 1
    Online Resource
    Online Resource
    Accelerated 3D whole-brain T1, T2, and proton density mapping: feasibility for clinical glioma MR imaging
    Springer Science and Business Media LLC ; 2021
    In:  Neuroradiology Vol. 63, No. 11 ( 2021-11), p. 1831-1851
    Details
    In: Neuroradiology, Springer Science and Business Media LLC, Vol. 63, No. 11 ( 2021-11), p. 1831-1851
    Abstract: Advanced MRI-based biomarkers offer comprehensive and quantitative information for the evaluation and characterization of brain tumors. In this study, we report initial clinical experience in routine glioma imaging with a novel, fully 3D multiparametric quantitative transient-state imaging (QTI) method for tissue characterization based on T1 and T2 values. Methods To demonstrate the viability of the proposed 3D QTI technique, nine glioma patients (grade II–IV), with a variety of disease states and treatment histories, were included in this study. First, we investigated the feasibility of 3D QTI (6:25 min scan time) for its use in clinical routine imaging, focusing on image reconstruction, parameter estimation, and contrast-weighted image synthesis. Second, for an initial assessment of 3D QTI-based quantitative MR biomarkers, we performed a ROI-based analysis to characterize T1 and T2 components in tumor and peritumoral tissue. Results The 3D acquisition combined with a compressed sensing reconstruction and neural network-based parameter inference produced parametric maps with high isotropic resolution (1.125 × 1.125 × 1.125 mm 3 voxel size) and whole-brain coverage (22.5 × 22.5 × 22.5 cm 3 FOV), enabling the synthesis of clinically relevant T1-weighted, T2-weighted, and FLAIR contrasts without any extra scan time. Our study revealed increased T1 and T2 values in tumor and peritumoral regions compared to contralateral white matter, good agreement with healthy volunteer data, and high inter-subject consistency. Conclusion 3D QTI demonstrated comprehensive tissue assessment of tumor substructures captured in T1 and T2 parameters. Aiming for fast acquisition of quantitative MR biomarkers, 3D QTI has potential to improve disease characterization in brain tumor patients under tight clinical time-constraints.
    Type of Medium: Online Resource
    ISSN: 0028-3940 , 1432-1920
    URL: Article
    DOI: 10.1007/s00234-021-02703-0
    RVK:
    XA 10000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 1462953-7
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  • 2
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    A diffusion model‐free framework with echo time dependence for free‐water elimination and brain tissue microstructure characterization
    Wiley ; 2018
    In:  Magnetic Resonance in Medicine Vol. 80, No. 5 ( 2018-11), p. 2155-2172
    Details
    In: Magnetic Resonance in Medicine, Wiley, Vol. 80, No. 5 ( 2018-11), p. 2155-2172
    Abstract: The compartmental nature of brain tissue microstructure is typically studied by diffusion MRI, MR relaxometry or their correlation. Diffusion MRI relies on signal representations or biophysical models, while MR relaxometry and correlation studies are based on regularized inverse Laplace transforms (ILTs). Here we introduce a general framework for characterizing microstructure that does not depend on diffusion modeling and replaces ill‐posed ILTs with blind source separation (BSS). This framework yields proton density, relaxation times, volume fractions, and signal disentanglement, allowing for separation of the free‐water component. Theory and Methods Diffusion experiments repeated for several different echo times, contain entangled diffusion and relaxation compartmental information. These can be disentangled by BSS using a physically constrained nonnegative matrix factorization. Results Computer simulations, phantom studies, together with repeatability and reproducibility experiments demonstrated that BSS is capable of estimating proton density, compartmental volume fractions and transversal relaxations. In vivo results proved its potential to correct for free‐water contamination and to estimate tissue parameters. Conclusion 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.
    Type of Medium: Online Resource
    ISSN: 0740-3194 , 1522-2594
    URL: Issue
    URL: Article
    DOI: 10.1002/mrm.v80.5
    DOI: 10.1002/mrm.27181
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 1493786-4
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  • 3
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    Saturation‐recovery metabolic‐exchange rate imaging with hyperpolarized [1‐ 13 C] pyruvate using spectral‐spatial excitation
    Wiley ; 2013
    In:  Magnetic Resonance in Medicine Vol. 69, No. 5 ( 2013-05), p. 1209-1216
    Details
    In: Magnetic Resonance in Medicine, Wiley, Vol. 69, No. 5 ( 2013-05), p. 1209-1216
    Abstract: Within the last decade hyperpolarized [1‐ 13 C] pyruvate chemical‐shift imaging has demonstrated impressive potential for metabolic MR imaging for a wide range of applications in oncology, cardiology, and neurology. In this work, a highly efficient pulse sequence is described for time‐resolved, multislice chemical shift imaging of the injected substrate and obtained downstream metabolites. Using spectral‐spatial excitation in combination with single‐shot spiral data acquisition, the overall encoding is evenly distributed between excitation and signal reception, allowing the encoding of one full two‐dimensional metabolite image per excitation. The signal‐to‐noise ratio can be flexibly adjusted and optimized using lower flip angles for the pyruvate substrate and larger ones for the downstream metabolites. Selectively adjusting the excitation of the down‐stream metabolites to 90° leads to a so‐called “saturation‐recovery” scheme with the detected signal content being determined by forward conversion of the available pyruvate. In case of repetitive excitations, the polarization is preserved using smaller flip angles for pyruvate. Metabolic exchange rates are determined spatially resolved from the metabolite images using a simplified two‐site exchange model. This novel contrast is an important step toward more quantitative metabolic imaging. Goal of this work was to derive, analyze, and implement this “saturation‐recovery metabolic exchange rate imaging” and demonstrate its capabilities in four rats bearing subcutaneous tumors. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 0740-3194 , 1522-2594
    URL: Issue
    URL: Article
    DOI: 10.1002/mrm.v69.5
    DOI: 10.1002/mrm.24353
    Language: English
    Publisher: Wiley
    Publication Date: 2013
    detail.hit.zdb_id: 1493786-4
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  • 4
    Online Resource
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    Accelerated diffusion spectrum imaging in the human brain using compressed sensing
    Wiley ; 2011
    In:  Magnetic Resonance in Medicine Vol. 66, No. 5 ( 2011-11), p. 1226-1233
    Details
    In: Magnetic Resonance in Medicine, Wiley, Vol. 66, No. 5 ( 2011-11), p. 1226-1233
    Abstract: We developed a novel method to accelerate diffusion spectrum imaging using compressed sensing. The method can be applied to either reduce acquisition time of diffusion spectrum imaging acquisition without losing critical information or to improve the resolution in diffusion space without increasing scan time. Unlike parallel imaging, compressed sensing can be applied to reconstruct a sub‐Nyquist sampled dataset in domains other than the spatial one. Simulations of fiber crossings in 2D and 3D were performed to systematically evaluate the effect of compressed sensing reconstruction with different types of undersampling patterns (random, gaussian, Poisson disk) and different acceleration factors on radial and axial diffusion information. Experiments in brains of healthy volunteers were performed, where diffusion space was undersampled with different sampling patterns and reconstructed using compressed sensing. Essential information on diffusion properties, such as orientation distribution function, diffusion coefficient, and kurtosis is preserved up to an acceleration factor of R = 4. Magn Reson Med, 2011. © 2011 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 0740-3194 , 1522-2594
    URL: Issue
    URL: Article
    DOI: 10.1002/mrm.v66.5
    DOI: 10.1002/mrm.23064
    Language: English
    Publisher: Wiley
    Publication Date: 2011
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  • 5
    Online Resource
    Online Resource
    NMR on contaminated salt water ice
    Elsevier BV ; 2001
    In:  Magnetic Resonance Imaging Vol. 19, No. 3-4 ( 2001-4), p. 578-
    Details
    In: Magnetic Resonance Imaging, Elsevier BV, Vol. 19, No. 3-4 ( 2001-4), p. 578-
    Type of Medium: Online Resource
    ISSN: 0730-725X
    URL: Article
    DOI: 10.1016/S0730-725X(01)00334-4
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2001
    detail.hit.zdb_id: 1500646-3
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  • 6
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    Multisite Kinetic Modeling of 13 C Metabolic MR Using [1- 13 C]Pyruvate
    Hindawi Limited ; 2014
    In:  Radiology Research and Practice Vol. 2014 ( 2014), p. 1-10
    Details
    In: Radiology Research and Practice, Hindawi Limited, Vol. 2014 ( 2014), p. 1-10
    Abstract: Hyperpolarized 13 C imaging allows real-time in vivo measurements of metabolite levels. Quantification of metabolite conversion between [1- 13 C]pyruvate and downstream metabolites [1- 13 C]alanine, [1- 13 C]lactate, and [ 13 C]bicarbonate can be achieved through kinetic modeling. Since pyruvate interacts dynamically and simultaneously with its downstream metabolites, the purpose of this work is the determination of parameter values through a multisite, dynamic model involving possible biochemical pathways present in MR spectroscopy. Kinetic modeling parameters were determined by fitting the multisite model to time-domain dynamic metabolite data. The results for different pyruvate doses were compared with those of different two-site models to evaluate the hypothesis that for identical data the uncertainty of a model and the signal-to-noise ratio determine the sensitivity in detecting small physiological differences in the target metabolism. In comparison to the two-site exchange models, the multisite model yielded metabolic conversion rates with smaller bias and smaller standard deviation, as demonstrated in simulations with different signal-to-noise ratio. Pyruvate dose effects observed previously were confirmed and quantified through metabolic conversion rate values. Parameter interdependency allowed an accurate quantification and can therefore be useful for monitoring metabolic activity in different tissues.
    Type of Medium: Online Resource
    ISSN: 2090-1941 , 2090-195X
    URL: Article
    DOI: 10.1155/2014/871619
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2014
    detail.hit.zdb_id: 2594649-3
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  • 7
    Online Resource
    Online Resource
    q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans
    Institute of Electrical and Electronics Engineers (IEEE) ; 2016
    In:  IEEE Transactions on Medical Imaging Vol. 35, No. 5 ( 2016-5), p. 1344-1351
    Details
    In: IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers (IEEE), Vol. 35, No. 5 ( 2016-5), p. 1344-1351
    Type of Medium: Online Resource
    ISSN: 0278-0062 , 1558-254X
    URL: Article
    DOI: 10.1109/TMI.2016.2551324
    RVK:
    XA 48667
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016
    detail.hit.zdb_id: 2068206-2
    detail.hit.zdb_id: 622531-7
    SSG: 12
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  • 8
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    Multi‐directional anisotropy from diffusion orientation distribution functions
    Wiley ; 2015
    In:  Journal of Magnetic Resonance Imaging Vol. 41, No. 3 ( 2015-03), p. 841-850
    Details
    In: Journal of Magnetic Resonance Imaging, Wiley, Vol. 41, No. 3 ( 2015-03), p. 841-850
    Abstract: To evaluate a model‐independent, multi‐directional anisotropy (MDA) metric that is analytically and experimentally equivalent to fractional anisotropy (FA) in single‐direction diffusivity, but potentially superior to FA in its sensitivity to the underlying anisotropy of multi‐directional diffusivity. Materials and Methods An expression for MDA was defined from the orientation distribution function (ODF) and its analytical relation to FA was derived. Simulations of single and crossed double‐fibers were performed using a compressed‐sensing‐accelerated diffusion‐spectrum‐imaging (CS‐DSI) scheme. In vivo brain imaging using CS‐DSI was performed on eight healthy subjects. MDA was compared with FA and with another ODF‐based metric known as generalized FA (GFA). Results In simulated single‐direction fibers, MDA was shown to be equivalent to FA (from FA = 0.2 to 0.8). In crossed fibers, MDA provided superior differentiation of the underlying anisotropy as compared to FA and GFA. In vivo analysis shows that the MDA was superior to both FA ( P  = 0.015) and GFA ( P  = 0.021) in terms of its relative accuracy in crossed fiber regions. Conclusion MDA provides a potentially superior measure of fiber anisotropy relative to conventional FA or GFA, and may be used to improve the assessment of disease in regions with multi‐directional brain fibers. J. Magn. Reson. Imaging 2015;41:841–850. © 2014 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 1053-1807 , 1522-2586
    URL: Issue
    URL: Article
    DOI: 10.1002/jmri.v41.3
    DOI: 10.1002/jmri.24589
    Language: English
    Publisher: Wiley
    Publication Date: 2015
    detail.hit.zdb_id: 1497154-9
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  • 9
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    Bias and precision analysis of diffusional kurtosis imaging for different acquisition schemes
    Wiley ; 2016
    In:  Magnetic Resonance in Medicine Vol. 76, No. 6 ( 2016-12), p. 1684-1696
    Details
    In: Magnetic Resonance in Medicine, Wiley, Vol. 76, No. 6 ( 2016-12), p. 1684-1696
    Abstract: Diffusional kurtosis imaging (DKI) is an approach to characterizing the non‐Gaussian fraction of water diffusion in biological tissue. However, DKI is highly susceptible to the low signal‐to‐noise ratio of diffusion‐weighted images, causing low precision and a significant bias due to Rician noise distribution. Here, we evaluate precision and bias using weighted linear least squares fitting of different acquisition schemes including several multishell schemes, a diffusion spectrum imaging (DSI) scheme, as well as a compressed sensing reconstruction of undersampled DSI scheme. Methods Monte Carlo simulations were performed to study the three‐dimensional distribution of the apparent kurtosis coefficient (AKC). Experimental data were acquired from one healthy volunteer with multiple repetitions, using the same acquisition schemes as for the simulations. Results The angular distribution of the bias and precision were very inhomogeneous. While axial kurtosis was significantly overestimated, radial kurtosis was underestimated. The precision of radial kurtosis was up to 10‐fold lower than axial kurtosis. Conclusion The noise bias behavior of DKI is highly complex and can cause overestimation as well as underestimation of the AKC even within one voxel. The acquisition scheme with three shells, suggested by Poot et al, provided overall the best performance. Magn Reson Med 76:1684–1696, 2016. © 2016 International Society for Magnetic Resonance in Medicine
    Type of Medium: Online Resource
    ISSN: 0740-3194 , 1522-2594
    URL: Issue
    URL: Article
    DOI: 10.1002/mrm.v76.6
    DOI: 10.1002/mrm.26008
    Language: English
    Publisher: Wiley
    Publication Date: 2016
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  • 10
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    Online Resource
    Diffusion in realistic biophysical systems can lead to aliasing effects in diffusion spectrum imaging
    Wiley ; 2016
    In:  Magnetic Resonance in Medicine Vol. 76, No. 6 ( 2016-12), p. 1837-1847
    Details
    In: Magnetic Resonance in Medicine, Wiley, Vol. 76, No. 6 ( 2016-12), p. 1837-1847
    Abstract: Diffusion spectrum imaging (DSI) is an imaging technique that has been successfully applied to resolve white matter crossings in the human brain. However, its accuracy in complex microstructure environments has not been well characterized. Theory and Methods Here we have simulated different tissue configurations, sampling schemes, and processing steps to evaluate DSI performances' under realistic biophysical conditions. A novel approach to compute the orientation distribution function (ODF) has also been developed to include biophysical constraints, namely integration ranges compatible with axial fiber diffusivities. Results Performed simulations identified several DSI configurations that consistently show aliasing artifacts caused by fast diffusion components for both isotropic diffusion and fiber configurations. The proposed method for ODF computation showed some improvement in reducing such artifacts and improving the ability to resolve crossings, while keeping the quantitative nature of the ODF. Conclusion In this study, we identified an important limitation of current DSI implementations, specifically the presence of aliasing due to fast diffusion components like those from pathological tissues, which are not well characterized, and can lead to artifactual fiber reconstructions. To minimize this issue, a new way of computing the ODF was introduced, which removes most of these artifacts and offers improved angular resolution. Magn Reson Med 76:1837–1847, 2016. © 2015 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
    Type of Medium: Online Resource
    ISSN: 0740-3194 , 1522-2594
    URL: Issue
    URL: Article
    DOI: 10.1002/mrm.v76.6
    DOI: 10.1002/mrm.26080
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 1493786-4
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