Effects of perfusion on DTI and DKI estimates in the skeletal muscle

Luca, Alberto De; Bertoldo, Alessandra; Froeling, Martijn

doi:10.1002/mrm.26373

Cited by 41 publications

(42 citation statements)

References 68 publications

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“…The correction for partial volume effects was also investigated with in vivo data, where it resulted in lower values and more homogeneous MD maps. The changes as a result of bias correction were in line with our simulations and previous findings of lower MD and K values after pseudo‐diffusion and FW correction …”

Section: Discussionsupporting

confidence: 92%

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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Section: Discussionsupporting

confidence: 92%

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

et al. 2018

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“…The T2 fat relaxation time was obtained from the subcutaneous fat of each subject and fixed during individual fit. Diffusion data were processed taking intravoxel incoherent motion (IVIM) into account . By using IVIM correction, an isotropic pseudo‐diffusion component was modeled in addition to the diffusion tensor, to effectively remove biases in mean diffusivity (MD) estimation.…”

Section: Methodsmentioning

confidence: 99%

Multi‐center evaluation of stability and reproducibility of quantitative MRI measures in healthy calf muscles

et al. 2019

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The purpose of this study was to evaluate temporal stability, multi‐center reproducibility and the influence of covariates on a multimodal MR protocol for quantitative muscle imaging and to facilitate its use as a standardized protocol for evaluation of pathology in skeletal muscle. Quantitative T2, quantitative diffusion and four‐point Dixon acquisitions of the calf muscles of both legs were repeated within one hour. Sixty‐five healthy volunteers (31 females) were included in one of eight 3‐T MR systems. Five traveling subjects were examined in six MR scanners. Average values over all slices of water‐T2 relaxation time, proton density fat fraction (PDFF) and diffusion metrics were determined for seven muscles. Temporal stability was tested with repeated measured ANOVA and two‐way random intraclass correlation coefficient (ICC). Multi‐center reproducibility of traveling volunteers was assessed by a two‐way mixed ICC. The factors age, body mass index, gender and muscle were tested for covariance. ICCs of temporal stability were between 0.963 and 0.999 for all parameters. Water‐T2 relaxation decreased significantly (P < 10−3) within one hour by ~ 1 ms. Multi‐center reproducibility showed ICCs within 0.879–0.917 with the lowest ICC for mean diffusivity. Different muscles showed the highest covariance, explaining 20–40% of variance for observed parameters. Standardized acquisition and processing of quantitative muscle MRI data resulted in high comparability among centers. The imaging protocol exhibited high temporal stability over one hour except for water T2 relaxation times. These results show that data pooling is feasible and enables assembling data from patients with neuromuscular diseases, paving the way towards larger studies of rare muscle disorders.

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“…The dMRI data of each subject was preprocessed for subject motion and eddy currents with Elastix, realigning each volume to the first nondiffusion weighted with an affine transformation, then the b‐matrix was rotated accordingly . Fractional anisotropy (FA) and mean diffusivity (MD) maps were computed with an in‐house linear estimator implemented in MATLAB R2015b (MathWorks Inc.). The acquisition of 2 diffusion‐weighted shells was performed to improve the robustness of the DTI fit to perfusion biases …”

Section: Methodsmentioning

confidence: 99%

“…Fractional anisotropy (FA) and mean diffusivity (MD) maps were computed with an in‐house linear estimator implemented in MATLAB R2015b (MathWorks Inc.). The acquisition of 2 diffusion‐weighted shells was performed to improve the robustness of the DTI fit to perfusion biases …”

Section: Methodsmentioning

confidence: 99%

“…The acquisition of 2 diffusion-weighted shells was performed to improve the robustness of the DTI fit to perfusion biases. 36 Regions of Interest and Statistical Analysis. Given the high contrast provided by the Dixon WS images, these were chosen as references to manually draw 4 regions of interest (ROIs) for statistical analysis purposes: (1) muscles from the anterior compartment of the thigh (rectus femoris, vastus medialis, vastus intermedius, and vastus lateralis); (2) muscles from the postero-medial compartment of the thigh (semitendinosus, semimembranosus, biceps femoris, adductor magnus, and adductor longus); (3) gracilis muscle; (4) sartorius muscle.…”

Section: Methodsmentioning

confidence: 99%

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Multiparametric quantitative MRI assessment of thigh muscles in limb‐girdle muscular dystrophy 2A and 2B

et al. 2018

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Effects of perfusion on DTI and DKI estimates in the skeletal muscle

Abstract: Blood perfusion has an effect on DTI and DKI estimations, but it can be minimized with IVIM correction or multishell acquisition strategies. Magn Reson Med 78:233-246, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Cited by 41 publications

References 68 publications

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

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

Multi‐center evaluation of stability and reproducibility of quantitative MRI measures in healthy calf muscles

Multiparametric quantitative MRI assessment of thigh muscles in limb‐girdle muscular dystrophy 2A and 2B

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