Linking spherical mean diffusion weighted signal with intra-axonal volume fraction

Abstract Background To compare the diagnostic performance of neurite orientation dispersion and density imaging (NODDI), mean apparent propagator magnetic resonance imaging (MAP-MRI), diffusion kurtosis imaging (DKI), diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs). Methods Patients with previously untreated, histopathologically confirmed HGGs (n = 20) or SBMs (n = 21) appearing as a solitary and contrast-enhancing lesion on structural MRI were prospectively recruited to undergo diffusion-weighted MRI. DWI data were obtained using a q-space Cartesian grid sampling procedure and were processed to generate parametric maps by fitting the NODDI, MAP-MRI, DKI, DTI and DWI models. The diffusion metrics of the contrast-enhancing tumor and peritumoral edema were measured. Differences in the diffusion metrics were compared between HGGs and SBMs, followed by receiver operating characteristic (ROC) analysis and the Hanley and McNeill test to determine their diagnostic performances. Results NODDI-based isotropic volume fraction (Viso) and orientation dispersion index (ODI); MAP-MRI-based mean-squared displacement (MSD) and q-space inverse variance (QIV); DKI-generated radial, mean diffusivity and fractional anisotropy (RDk, MDk and FAk); and DTI-generated radial, mean diffusivity and fractional anisotropy (RD, MD and FA) of the contrast-enhancing tumor were significantly different between HGGs and SBMs (p < 0.05). The best single discriminative parameters of each model were Viso, MSD, RDk and RD for NODDI, MAP-MRI, DKI and DTI, respectively. The AUC of Viso (0.871) was significantly higher than that of MSD (0.736), RDk (0.760) and RD (0.733) (p < 0.05). Conclusion NODDI outperforms MAP-MRI, DKI, DTI and DWI in differentiating between HGGs and SBMs. NODDI-based Viso has the highest performance.

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A simulation study of cell size and volume fraction mapping for tissue with two underlying cell populations using diffusion‐weighted MRI

Magnetic Resonance in Medicine ◽

10.1002/mrm.28694 ◽

2021 ◽

Author(s):

Shu Xing ◽

Ives R. Levesque

Keyword(s):

Cell Size ◽

Simulation Study ◽

Volume Fraction ◽

Cell Populations ◽

Diffusion Weighted Mri ◽

Diffusion Weighted

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Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis

Frontiers in Neurology ◽

10.3389/fneur.2021.662855 ◽

2021 ◽

Vol 12 ◽

Author(s):

Daniel Johnson ◽

Antonio Ricciardi ◽

Wallace Brownlee ◽

Baris Kanber ◽

Ferran Prados ◽

...

Keyword(s):

Multiple Sclerosis ◽

White Matter ◽

Diffusion Mri ◽

Volume Fraction ◽

Diffusion Tensor ◽

Healthy Controls ◽

Age And Gender ◽

Spherical Mean ◽

White Matter Microstructure ◽

And Gender

Background: Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings.Purpose: To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics.Methods: Sixty three relapsing-remitting MS patients were compared to 28 healthy controls. NODDI and SMT metrics corresponding to intracellular volume fraction (vin), orientation dispersion (ODI and ODE), diffusivity (D) (SMT only) and isotropic volume fraction (viso) (NODDI only) were calculated from diffusion MRI data, alongside DTI metrics (fractional anisotropy, FA; axial/mean/radial diffusivity, AD/MD/RD). Correlations between all pairs of MRI metrics were calculated in normal-appearing white matter (NAWM). Associations with expanded disability status scale (EDSS), controlling for age and gender, were evaluated. Patient-control differences were assessed voxel-by-voxel in MNI space controlling for age and gender at the 5% significance level, correcting for multiple comparisons. Spatial overlap of areas showing significant differences were compared using Dice coefficients.Results: NODDI and SMT show significant associations with EDSS (standardised beta coefficient −0.34 in NAWM and −0.37 in lesions for NODDI vin; 0.38 and −0.31 for SMT ODE and vin in lesions; p < 0.05). Significant correlations in NAWM are observed between DTI and NODDI/SMT metrics. NODDI vin and SMT vin strongly correlated (r = 0.72, p < 0.05), likewise NODDI ODI and SMT ODE (r = −0.80, p < 0.05). All DTI, NODDI and SMT metrics detect widespread differences between patients and controls in NAWM (12.57% and 11.90% of MNI brain mask for SMT and NODDI vin, Dice overlap of 0.42).Data Conclusion: SMT and NODDI detect significant differences in white matter microstructure between MS patients and controls, concurring on the direction of these changes, providing consistent descriptors of tissue microstructure that correlate with disability and show alterations beyond focal damage. Our study suggests that NODDI and SMT may play a role in monitoring MS in clinical trials and practice.

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Comparison of NODDI and spherical mean signal for measuring intra-neurite volume fraction

Magnetic Resonance Imaging ◽

10.1016/j.mri.2018.11.021 ◽

2019 ◽

Vol 57 ◽

pp. 151-155 ◽

Cited By ~ 2

Author(s):

Hua Li ◽

Rahul Nikam ◽

Vinay Kandula ◽

Ho Ming Chow ◽

Arabinda K. Choudhary

Keyword(s):

Volume Fraction ◽

Spherical Mean

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Combined Arterial Spin Labeling and Diffusion-Weighted Imaging for Noninvasive Estimation of Capillary Volume Fraction and Permeability-Surface Product in the Human Brain

Journal of Cerebral Blood Flow & Metabolism ◽

10.1038/jcbfm.2012.125 ◽

2012 ◽

Vol 33 (1) ◽

pp. 67-75 ◽

Cited By ~ 21

Author(s):

Patrick W Hales ◽

Chris A Clark

Keyword(s):

Human Brain ◽

Diffusion Weighted Imaging ◽

Arterial Spin Labeling ◽

Volume Fraction ◽

A Priori ◽

Spin Labeling ◽

Gaussian Kernel ◽

Diffusion Weighted ◽

Permeability Surface ◽

Surface Product

A number of two-compartment models have been developed for the analysis of arterial spin labeling (ASL) data, from which both cerebral blood flow ( CBF) and capillary permeability-surface product ( PS) can be estimated. To derive values of PS, the volume fraction of the ASL signal arising from the intravascular space ( vbw) must be known a priori. We examined the use of diffusion-weighted imaging (DWI) and subsequent analysis using the intravoxel incoherent motion model to determine vbw in the human brain. These data were then used in a two-compartment ASL model to estimate PS. Imaging was performed in 10 healthy adult subjects, and repeated in five subjects to test reproducibility. In gray matter (excluding large arteries), mean voxel-wise vbw was 2.3 ± 0.2 mL blood/100 g tissue (all subjects mean ± s.d.), and CBF and PS were 44 ± 5 and 108 ± 2 mL per 100 g per minute, respectively. After spatial smoothing using a 6-mm full width at half maximum Gaussian kernel, the coefficient of repeatability of CBF, vbw and PS were 8 mL per 100 g per minute, 0.4 mL blood/100 g tissue, and 13 mL per 100 g per minute, respectively. Our results show that the combined use of ASL and DWI can provide a new, noninvasive methodology for estimating vbw and PS directly, with reproducibility that is sufficient for clinical use.

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Minimal number of gradient directions for robust measurement of spherical mean diffusion weighted signal

Magnetic Resonance Imaging ◽

10.1016/j.mri.2018.08.020 ◽

2018 ◽

Vol 54 ◽

pp. 148-152 ◽

Cited By ~ 5

Author(s):

Hua Li ◽

Ho Ming Chow ◽

Diane C. Chugani ◽

Harry T. Chugani

Keyword(s):

Diffusion Weighted ◽

Spherical Mean

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Extended T2-IVIM model for correction of TE dependence of pseudo-diffusion volume fraction in clinical diffusion-weighted magnetic resonance imaging

Physics in Medicine and Biology ◽

10.1088/1361-6560/61/24/n667 ◽

2016 ◽

Vol 61 (24) ◽

pp. N667-N680 ◽

Cited By ~ 28

Author(s):

N P Jerome ◽

J A d’Arcy ◽

T Feiweier ◽

D-M Koh ◽

M O Leach ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Volume Fraction ◽

Resonance Imaging ◽

Diffusion Weighted ◽

Weighted Magnetic Resonance Imaging

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Axonal T2 estimation using the spherical variance of the strongly diffusion-weighted MRI signal

10.1101/2021.08.19.456817 ◽

2021 ◽

Author(s):

Marco Pizzolato ◽

Mariam Andersson ◽

Erick Jorge Canales-Rodriguez ◽

Jean-Philippe Thiran ◽

Tim B Dyrby

Keyword(s):

White Matter ◽

Ex Vivo ◽

Transverse Relaxation Time ◽

Cell Nuclei ◽

Fixed Tissue ◽

Diffusion Weighted ◽

Spherical Mean ◽

Diffusion Weighting ◽

Strong Diffusion

In magnetic resonance imaging, the application of a strong diffusion weighting suppresses the signal contributions from the less diffusion-restricted constituents of the brain's white matter, thus enabling the estimation of the transverse relaxation time T2 that arises from the more diffusion-restricted constituents such as the axons. However, the presence of cell nuclei and vacuoles can confound the estimation of the axonal T2, as diffusion within those structures is also restricted, causing the corresponding signal to survive the strong diffusion weighting. We devise an estimator of the axonal T2 based on the directional spherical variance of the strongly diffusion-weighted signal. The spherical variance T2 estimates are insensitive to the presence of isotropic contributions to the signal like those provided by cell nuclei and vacuoles. We show that with a strong diffusion weighting these estimates differ from those obtained using the directional spherical mean of the signal which contains both axonal and isotropically-restricted contributions. Our findings hint at the presence of an MRI-visible isotropically-restricted contribution to the signal in the white matter ex vivo fixed tissue (monkey) at 7T, and do not allow us to discard such a possibility also for in vivo human data collected with a clinical 3T system.

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