Model selection and estimation of multi-compartment models in diffusion MRI with a Rician noise model

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

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.

Impact of fixation, coil, and number of excitations on diffusion tensor imaging of rat brains at 7.0 T

BACKGROUND

We sought to compare diffusion tensor imaging (DTI) parameters in vivo and ex vivo in the brain and to explore the effects of radiofrequency coil and number of excitations on ex vivo DTI parameters.

METHODS

Six Sprague-Dawley rat brains were used to obtain in vivo and ex vivo DTI maps with different coils and number of excitations. DTI parameters of white matter and grey matter including diffusivities, fractional anisotropy, and other dimensionless ratios (λ2/λ1, λ3/λ1, and λ2/λ3) were obtained from reconstruction maps. Comparisons of ex vivo signal-to-noise ratio with different coils and number of excitations were conducted.

RESULTS

Diffusivities decreased significantly after fixation in all the selected white matter and grey matter regions of interest (all at p < 0.001). The diffusivities in white matter integrity decreased more than in grey matter integrity after fixation (all at p < 0.001). The ratio of λ2/λ3 in the major brain structures changed after fixation (most at p < 0.05). There were differences in major ex vivo brain structures in DTI parameters and signal-to-noise ratio between surface coil and volume coil, and between one and four excitations (most at p < 0.05).

CONCLUSION

The impact of fixation, coil, and number of excitations on DTI parameters should be taken into consideration in clinical and experimental studies at 7.0 T.