Separating blood and water: Perfusion and free water elimination from diffusion MRI in the human brain

Rydhög, Anna; Szczepankiewicz, Filip; Wirestam, Ronnie; Ahlgren, André; Westin, Carl‐Fredrik; Knutsson, Linda; Pasternak, Ofer

doi:10.1016/j.neuroimage.2017.04.023

Cited by 55 publications

(61 citation statements)

References 65 publications

(89 reference statements)

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“…Previously, other models have been proposed to extend the two‐compartment approach, including three‐compartment models to separate the parenchymal, microvascular perfusion, and an additional component which is suggested to indicate freely diffusing water, a model which decomposes the microvascular flow into a fast and slow component and a model that disentangles diffusive and ballistic parts . These models differ from our approach.…”

Section: Discussionmentioning

confidence: 96%

Spectral Diffusion Analysis of Intravoxel Incoherent Motion MRI in Cerebral Small Vessel Disease

Wong

Backes

Drenthen

et al. 2019

Magnetic Resonance Imaging

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Background Cerebral intravoxel incoherent motion (IVIM) imaging assumes two components. However, more compartments are likely present in pathologic tissue. We hypothesized that spectral analysis using a nonnegative least‐squares (NNLS) approach can detect an additional, intermediate diffusion component, distinct from the parenchymal and microvascular components, in lesion‐prone regions. Purpose To investigate the presence of this intermediate diffusion component and its relation with cerebral small vessel disease (cSVD)‐related lesions. Study Type Prospective cross‐sectional study. Population Patients with cSVD (n = 69, median age 69.8) and controls (n = 39, median age 68.9). Field Strength/Sequence Whole‐brain inversion recovery IVIM acquisition at 3.0T. Assessment Enlarged perivascular spaces (PVS) were rated by three raters. White matter hyperintensities (WMH) were identified on a fluid attenuated inversion recovery (FLAIR) image using a semiautomated algorithm. Statistical Tests Relations between IVIM measures and cSVD‐related lesions were studied using the Spearman's rank order correlation. Results NNLS yielded diffusion spectra from which the intermediate volume fraction fint was apparent between parenchymal diffusion and microvasular pseudodiffusion. WMH volume and the extent of MRI‐visible enlarged PVS in the basal ganglia (BG) and centrum semiovale (CSO) were correlated with fint in the WMHs, BG, and CSO, respectively. fint was 4.2 ± 1.7%, 7.0 ± 4.1% and 13.6 ± 7.7% in BG and 3.9 ± 1.3%, 4.4 ± 1.4% and 4.5 ± 1.2% in CSO for the groups with low, moderate, and high number of enlarged PVS, respectively, and increased with the extent of enlarged PVS (BG: r = 0.49, P < 0.01; CSO: r = 0.23, P = 0.02). fint in the WMHs was 27.1 ± 13.1%, and increased with the WMH volume (r = 0.57, P < 0.01). Data Conclusion We revealed the presence of an intermediate diffusion component in lesion‐prone regions of cSVD and demonstrated its relation with enlarged PVS and WMHs. In tissue with these lesions, tissue degeneration or perivascular edema can lead to more freely diffusing interstitial fluid contributing to fint. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1170–1180.

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Section: Discussionmentioning

confidence: 96%

Spectral Diffusion Analysis of Intravoxel Incoherent Motion MRI in Cerebral Small Vessel Disease

Wong

Backes

Drenthen

et al. 2019

Magnetic Resonance Imaging

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“…Robust identification of the free-water compartment could thus contribute as a preprocessing step for the fitting procedures of these abovementioned methods. The two compartment model may further be improved by including additional fast diffusing compartments to account for pseudo-diffusion in micro-capillaries, 38 and by accounting for relaxation times that are distinctly different between free-water and tissue. 39,40 Such approaches require additional low b-value shells, or multiple echo times, which makes the acquisition considerably longer, and less feasible for clinical studies.…”

Section: Discussionmentioning

confidence: 99%

Fast and accurate initialization of the free‐water imaging model parameters from multi‐shell diffusion MRI

et al. 2019

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Cerebrospinal fluid partial volume effect is a known bias in the estimation of Diffusion Tensor Imaging (DTI) parameters from diffusion MRI data. The Free-Water Imaging model for diffusion MRI data adds a second compartment to the DTI model, which explicitly accounts for the signal contribution of extracellular free-water, such as cerebrospinal fluid. As a result the DTI parameters obtained through the free-water model are corrected for partial volume effects, and thus better represent tissue microstructure. In addition, the model estimates the fractional volume of free-water, and can be used to monitor changes in the extracellular space. Under certain assumptions, the model can be estimated from single-shell diffusion MRI data. However, by using data from multi-shell diffusion acquisitions, these assumptions can be relaxed, and the fit becomes more robust. Nevertheless, fitting the model to multi-shell data requires high computational cost, with a non-linear iterative minimization, which has to be initialized close enough to the global minimum to avoid local minima and to robustly estimate the model parameters. Here we investigate the properties of the main initialization approaches that are currently being used, and suggest new fast approaches to improve the initial estimates of the model parameters. We show that our proposed approaches provide a fast and accurate initial approximation of the model parameters, which is very close to the final solution. We demonstrate that the proposed initializations improve the final outcome of non-linear model fitting. KEYWORDSdiffusion tensor imaging (DTI), Free-Water Imaging, multi-shell diffusion imaging INTRODUCTIONDiffusion MRI is a non-invasive imaging methodology sensitive to the micron scale displacement of water molecules. The size of the displacement and its directional dependency reflect hindrance or restriction to the water molecules motion, yielding unique information, especially for white-matter brain structures, 1 about tissue microstructural properties and the organization of white-matter bundles. 2 Currently, Diffusion Tensor Imaging (DTI) 3,4 is the most popular way to infer microstructural properties from diffusion MRI. In DTI, the three-dimensional displacement is described by a single tensor compartment. However, the voxel resolution in diffusion MRI (typically in the mm scale) is coarse relative to tissue microstructure of interest in the brain (typically in micron scale). This leads to partial volume effects, i.e., misinterpretation of microstructural properties inferred by DTI, when multiple tissue types are present in the same voxel. 5The Free-Water Imaging model 6 is an extension of the DTI model that describes the diffusion MRI signal as a weighted mixture of a tensor compartment representing brain tissue, and a second compartment modeled by an isotropic tensor with diffusivity fixed to that of free-water at body temperature, i.e., 3 · 10 −3 mm 2 /s. 5 Free-water is defined as water molecules that do not experience hindrance or restriction during th...

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“…Disentangling these components is possible with the proposed method if data with sufficient SNR are provided. However, this may have minimal impact when only the diffusion properties of the tissue are of interest …”

Section: Discussionmentioning

confidence: 99%

“…For each voxel, the diffusion tensor model was fitted to the data at b = 5 and 1000 s/mm 2 , from which the main eigenvector V 1 was derived. The dMRI signal of each tissue class ( S i ) was generated as:

({, \begin{matrix} S_{i} = f_{tissue} e^{- b boldg R_{z} R_{y} D R_{y}^{T} R_{z}^{T} g^{T} + \frac{1}{6} b^{2} {\overset{true¯}{D}}^{2} K} + f_{FW} e^{- b D_{FW}} + f_{IVIM} e^{- b D^{*}}, \\ f_{tissue} + f_{FW} + f_{IVIM} = 1 \end{matrix})

where b is the b value, g is the corresponding gradient orientation, and R z and R y refer to rotation matrices around the y and z axes to align the tensor with V 1 . In this model, only the hindered diffusion component was assumed to be anisotropic.…”

Section: Methodsmentioning

confidence: 99%

“…For this reason, the number of components needed to appropriately describe the dMRI signal remains a topic of active research. 14,23,24 Recent work 22 has shown that both the pseudo-diffusion and FW compartments should be considered when including the non-weighted image in the model fitting. This suggests the need for at least three compartments for an accurate disentanglement of partial volume effects in the brain, which is in line with other studies on brain 25 and non-brain [26][27][28] tissue.…”

Section: Introductionmentioning

confidence: 99%

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A robust deconvolution method to disentangle multiple water pools in diffusion MRI

et al. 2018

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The diffusion‐weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo‐diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal‐to‐noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data‐driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro‐compartments, which were consistent with hindered diffusion, free water and pseudo‐diffusion. Taking free water and pseudo‐diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co‐existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.

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Separating blood and water: Perfusion and free water elimination from diffusion MRI in the human brain

Cited by 55 publications

References 65 publications

Spectral Diffusion Analysis of Intravoxel Incoherent Motion MRI in Cerebral Small Vessel Disease

Spectral Diffusion Analysis of Intravoxel Incoherent Motion MRI in Cerebral Small Vessel Disease

Fast and accurate initialization of the free‐water imaging model parameters from multi‐shell diffusion MRI

A robust deconvolution method to disentangle multiple water pools in diffusion MRI

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