Intravoxel incoherent motion MRI as a means to measure <i>in vivo</i> perfusion: A review of the evidence

Federau, Christian

doi:10.1002/nbm.3780

Cited by 83 publications

(88 citation statements)

References 147 publications

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“…Regarding the real cases, the tissues within the regions of interest were quite similar and homogeneous across the different methods, whilst the outside regions were differently estimated. In fact, the estimation of diffusion parameters in certain tissues could be affected by the high level of noise, the very low signal or the presence of artifacts in these regions; moreover, D ∗ may be not properly defined where the perfusion compartment is vanishing, such as in teeth or fat . The D maps were consistent through the different methods within the ROIs, where LSQ‐FULL was confirmed to estimate the noisiest maps, as shown in the simulations.…”

Section: Discussionmentioning

confidence: 99%

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion‐weighted MRI

et al. 2019

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The Intra-Voxel Incoherent Motion (IVIM) model is largely adopted to estimate slow and fast diffusion coefficients of water molecules in biological tissues, which are used in cancer applications. The most reported fitting approach is a voxel-wise segmented non-linear least square, whereas Bayesian approaches with a direct fit, also considering spatial regularization, were proposed too. In this work a novel segmented Bayesian method was proposed, also in combination with a spatial regularization through a Conditional Autoregressive (CAR) prior specification. The two segmented Bayesian approaches, with and without CAR specification, were compared with two standard least-square and a direct Bayesian fitting methods. All approaches were tested on simulated images and real data of patients with head-and-neck and rectal cancer. Estimation accuracy and maps noisiness were quantified on simulated images, whereas the coefficient of variation and the goodness of fit were evaluated for real data.Both versions of the segmented Bayesian approach outperformed the standard methods on simulated images for pseudo-diffusion (D * ) and perfusion fraction (f), whilst the segmented least-square fitting remained the less biased for the diffusion coefficient (D). On real data, Bayesian approaches provided the less noisy maps, and the two Bayesian methods without CAR generally estimated lower values for f and D * coefficients with respect to the other approaches.The proposed segmented Bayesian approaches were superior, in terms of estimation accuracy and maps quality, to the direct Bayesian model and the least-square fittings. The CAR method improved the estimation accuracy, especially for D * . KEYWORDS Bayesian Segmented Approach, Conditional Autoregressive Model, Diffusion-Weighted MRI, IVIM 1 INTRODUCTION Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is a non-invasive technique that quantitatively characterizes the diffusion properties of water molecules. 1 Typically, the standard analysis of DW-MRI signals provides the estimation of the Apparent Diffusion Coefficient (ADC), which is estimated using a mono-exponential decay model of the signal intensity over b values. The Intra-Voxel Incoherent Motion (IVIM) model describes the incoherent motion of the water molecules in function of diffusion and perfusionproperties of a tissue at the same time. 2 In the IVIM model, the signal intensity in each voxel is modeled via a bi-exponential decay over b, characterized by a first fast component, which is related to tissue perfusion (the blood flow in the capillaries), followed by a second slow component, which is related to tissue molecular diffusion. Specifically, the IVIM model allows to estimate the diffusion coefficient (D), the pseudo-diffusion coefficient (D * ) and the perfusion volume fraction (f) within the tissue. The estimation of these coefficients can be made within a region of interest, by averaging the signal intensities over the voxels, or voxel-by-voxel to obtain parametric maps.NMR in Biomedicine. 2020;33:e4201. wil...

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Section: Discussionmentioning

confidence: 99%

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion‐weighted MRI

et al. 2019

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“…Multiple b ‐values around 10–30 are necessary to measure IVIM parameters due to the biexponential model. However, there has been no consensus on the either the optimal number of b ‐values or the optimal choice of b ‐values …”

Section: Intravoxel Incoherent Motion (Ivim)mentioning

confidence: 99%

“…Therefore, f × D * can be regarded to be proportional to blood flow. Correlations between IVIM parameters and histological microvasculature density were analyzed and shown to have fair to good results . Therefore, measuring this fast diffusion component could indirectly provide a marker of tissue vascularity (Figs.…”

Section: Intravoxel Incoherent Motion (Ivim)mentioning

confidence: 99%

MRI biomarkers in osseous tumors

Fukuda

Wengler

Carvalho

et al. 2019

Magnetic Resonance Imaging

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Although radiography continues to play a critical role in osseous tumor assessment, there have been remarkable advances in cross‐sectional imaging. MRI has taken a lead in this assessment due to high tissue contrast and spatial resolution, which are well suited for bone lesion assessment. More recently, although somewhat lagging other organ systems, quantitative parameters have shown promising potential as biomarkers for osseous tumors. Among these sequences are chemical shift imaging (CSI), apparent diffusion coefficient (ADC), and intravoxel incoherent motion (IVIM) from diffusion‐weighted imaging (DWI), quantitative dynamic contrast enhanced (DCE)‐MRI, and magnetic resonance spectroscopy (MRS). In this article, we review the background and recent roles of these quantitative MRI biomarkers for osseous tumors. Level of Evidence: 3 Technical Efficacy Stage: 3 J. MAGN. RESON. IMAGING 2019. J. Magn. Reson. Imaging 2019;50:702–718.

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“…The pseudo‐diffusion component has been investigated previously in conjunction with ADC, DTI and diffusion kurtosis imaging (DKI) . The extension of classic models with pseudo‐diffusion and FW compartments has provided valuable biomarkers in several applications . Although such partial volume effects are an important bias in DTI and DKI analysis, taking them into account results in improved signal characterization, reduced estimation biases in DTI and DKI metrics, and additional specificity, as it potentially allows intra‐ and extracellular‐related changes to be disentangled.…”

Section: Introductionmentioning

confidence: 99%

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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Intravoxel incoherent motion MRI as a means to measure in vivo perfusion: A review of the evidence

Cited by 83 publications

References 147 publications

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion‐weighted MRI

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion‐weighted MRI

MRI biomarkers in osseous tumors

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

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