New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter

Assaf, Yaniv; Freidlin, Raisa Z.; Rohde, Gustavo K.; Basser, Peter J.

doi:10.1002/mrm.20274

Cited by 470 publications

(482 citation statements)

References 38 publications

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“…To our knowledge, this was the first demonstration of simultaneous DTI, DSI, QBI and bi-exponential diffusion analysis from a single diffusion-weighted dataset. Possibly, the most similar experimental design is the CHARMED method (Assaf et al 2004, which has a similar q-space sampling scheme of concentric q-space shells. From the CHARMED analysis, one may estimate a single hindered (fast) diffusion tensor component and multiple restricted (slow) diffusion components.…”

Section: Discussionmentioning

confidence: 99%

“…While Cartesian sampling facilitates the straightforward FFT for estimation of the displacement densities of water molecules, it is not required. Recently, investigators have proposed non-Cartesian sampling strategies of q-space (Assaf et al, 2004;Assaf and Basser, 2005;Wu and Alexander, 2005;Khachaturian et al, 2007). Assaf et al applied a model (CHARMED) of slow and fast diffusion compartments to estimate what they deemed as hindered and restricted diffusion (Assaf et al, 2004;Assaf and Basser, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, investigators have proposed non-Cartesian sampling strategies of q-space (Assaf et al, 2004;Assaf and Basser, 2005;Wu and Alexander, 2005;Khachaturian et al, 2007). Assaf et al applied a model (CHARMED) of slow and fast diffusion compartments to estimate what they deemed as hindered and restricted diffusion (Assaf et al, 2004;Assaf and Basser, 2005). In a separate study, we demonstrated that the non-Cartesian q-space sampling strategy that uses concentric spherical shells of constant |q| may be used for estimating DTI, DSI and QBI properties in a single experiment (Wu and Alexander, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Hybrid diffusion imaging

2007

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Diffusion measurements in the human central nervous system are complex to characterize and a broad spectrum of methods have been proposed. In this study, a comprehensive diffusion encoding and analysis approach, Hybrid Diffusion Imaging (HYDI), is described. The HYDI encoding scheme is composed of multiple concentric "shells" of constant diffusion-weighting, which may be used to characterize the signal behavior with low, moderate and high diffusion-weighting. HYDI facilitates the application of multiple data-analyses strategies including diffusion tensor imaging (DTI), multiexponential diffusion measurements, diffusion spectrum imaging (DSI) and q-ball imaging (QBI). These different analysis strategies may provide complementary information. DTI measures (mean diffusivity and fractional anisotropy) may be estimated from either data in the inner shells or the entire HYDI data. Fast and slow diffusivities were estimated using a nonlinear least-squares biexponential fit on geometric means of the HYDI shells. DSI measurements from the entire HYDI data yield empirical model-independent diffusion information and are well-suited for characterizing tissue regions with complex diffusion behavior. DSI measurements were characterized using the zero displacement probability and the mean squared displacement. The outermost HYDI shell was analyzed using QBI analysis to estimate the orientation distribution function (ODF), which is useful for characterizing the directions of multiple fiber groups within a voxel. In this study, a HYDI encoding scheme with 102 diffusion-weighted measurements was obtained over most of the human cerebrum in under 30 minutes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid diffusion imaging

2007

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“…However, such metrics do not give a direct measurement of ECS and ICS volume fraction. ECS and ICS volume fraction estimates can be computed by fitting the echo attenuation curves to mathematical models (Assaf et al, 2006;Assaf et al, 2004). In another approach, Malmborg et al recently proposed a model-free method of ECS and ICS volume fraction estimation based on varying the duration of the diffusion gradients (Malmborg et al, 2006).…”

Section: Discussion Estimating Mean Axon Diameter With Qsimentioning

confidence: 99%

Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies

et al. 2008

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Q-space imaging (QSI), a diffusion MRI technique, can provide quantitative tissue architecture information at cellular dimensions not amenable by conventional diffusion MRI. By exploiting regularities in molecular diffusion barriers, QSI can estimate the average barrier spacing such as the mean axon diameter in white matter (WM). In this work, we performed ex vivo QSI on cervical spinal cord sections from healthy C57BL/6 mice at 400MHz using a custom-designed uniaxial 50T/m gradient probe delivering a 0.6 µm displacement resolution capable of measuring axon diameters on the scale of 1 µm. After generating QSI-derived axon diameter maps, diameters were calculated using histology from seven WM tracts (dorsal corticospinal, gracilis, cuneatus, rubrospinal, spinothalamic, reticulospinal, and vestibulospinal tracts) each with different axon diameters. We found QSI-derived diameters from regions drawn in the seven WM tracts (1.1 to 2.1 µm) to be highly correlated (r 2 = 0.95) with those calculated from histology (0.8 to 1.8 µm). The QSI-derived values overestimated those obtained by histology by approximately 20%, which is likely due to the presence of extracellular signal. Finally, simulations on images of synthetic circular axons and axons from histology suggest that QSI-derived diameters are informative despite diameter and axon shape variation and the presence of intra-cellular and extra-cellular signal. QSI may be able to quantify nondestructively changes in WM axon architecture due to pathology or injury at the cellular level.

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“…One of these limitations is due to the assumption that diffusion in biological tissues is Gaussian, namely that exists a linear relation between the mean square displacement (MSD) of diffusing molecules and time (t) during which diffusion occurs. Indeed, dynamics of water in living systems can be hindered and/or restricted by confining boundaries in multiple compartments leading to a deviation from the linear relation between MSD of diffusing particles and t [5,6]. Moreover, some biological tissues, such as the cerebral ones, can be described in terms of a multi-scale and multi-level complex system, in which a complex hierarchical topology resembling fractal folding can be found [7,8].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal anomalous diffusion imaging: results in controlled phantoms and in excised human meningiomas

Capuani

Palombo

Gabrielli³

et al. 2013

Magnetic Resonance Imaging

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Recently, we measured two anomalous diffusion (AD) parameters: the spatial and the temporal AD indices, called γ and α, respectively, by using spectroscopic pulse gradient field methods. We showed that γ quantifies pseudo-superdiffusion processes, while α quantifies subdiffusion processes. Here, we propose γ and α maps obtained in a controlled heterogeneous phantom, comprised of packed micro-beads in water and in excised human meningiomas. In few words, α maps represent the multi-scale spatial distribution of the disorder degree in the system, while γ maps are influenced by local internal gradients, thus highlighting the interface between compartments characterized by different magnetic susceptibility. γ maps were already obtained by means of AD stretched exponential imaging and α-type maps have been recently achieved for fixed rat brain with the aim of highlighting the fractal dimension of specific brain regions. However, to our knowledge, the maps representative of the spatial distribution of α and γ obtained on the same controlled sample and in the same excised tissue have never been compared. Moreover, we show here, for the first time, that α maps are representative of the spatial distribution of the disorder degree of the system. In a first phase, γ and α maps of controlled phantom characterized by an ordered and a disordered rearrangement of packed micro-beads of different sizes in water and by different magnetic susceptibility (Δχ) between beads and water were obtained. In a second phase, we investigated excised human meningiomas of different consistency. Results reported here, obtained at 9.4 T, show that α and γ maps are characterized by a different image contrast. Indeed, unlike γ maps, α maps are insensible to (Δχ) and they are sensible to the disorder degree of the microstructural rearrangement. These observations strongly suggest that AD indices α and γ reflect some additional microstructural information which cannot be obtained using conventional diffusion methods based on Gaussian diffusion. Moreover, α and γ maps obtained in excised meningiomas seem to provide more microstructural details above those obtained with conventional DTI analysis, which could be used to improve the classification of meningiomas based on their consistency.

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New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter

Cited by 470 publications

References 38 publications

Hybrid diffusion imaging

Hybrid diffusion imaging

Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies

Spatio-temporal anomalous diffusion imaging: results in controlled phantoms and in excised human meningiomas

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