Analysis of errors in diffusion kurtosis imaging caused by slice crosstalk in simultaneous multi-slice imaging

Diffusion propagator metrics are biased when simultaneous multi-slice acceleration is used

Advanced diffusion MRI models are being explored to study the complex microstructure of the brain with higher accuracy. However, these techniques require long acquisition times. Simultaneous Multi-Slice (SMS) accelerates data acquisition by exciting multiple image slices simultaneously and separating the overlapping slices using a mathematical model, which makes use of the distinct information coming from an array of receive coils. However, SMS acceleration introduces increased noise in reconstructed images and crosstalk between simultaneously excited slices. These compounded effects from SMS acceleration could affect quantitative MRI techniques such as diffusion imaging. In this study, the effects of SMS acceleration on the accuracy of propagator metrics obtained from a model-free advanced diffusion technique called Mean Apparent Propagator MRI (MAP-MRI) was investigated. Ten healthy volunteers were scanned with SMS accelerated multi-shell diffusion MRI acquisitions. Group analyses were performed to study brain regions affected by SMS acceleration. In addition, diffusion metrics from atlas-based fiber tracts of interest were analyzed to investigate how propagator metrics in major fiber tracts were biased by 2- and 3-band SMS acceleration. Both zero-displacement metrics and non-Gaussianity metrics were significantly altered when SMS acceleration was used. MAP-MRI metrics calculated from SMS-3 showed significant differences with respect to SMS-2. Furthermore, with the shorter TR afforded by SMS acceleration, the characteristics of this bias have changed. This has implications for studies using diffusion MRI with SMS acceleration to investigate the effects of a disease or injury on the brain tissues.

Simultaneous multi-slice image reconstruction using regularized image domain split slice-GRAPPA for diffusion MRI

The main goal of this work is to improve the quality of simultaneous multi-slice (SMS) reconstruction for diffusion MRI. We accomplish this by developing an image domain method that reaps the benefits of both SENSE and GRAPPA-type approaches and enables image regularization in an optimization framework. We propose a new approach termed regularized image domain split slice-GRAPPA (RI-SSG), which establishes an optimization framework for SMS reconstruction. Within this framework, we use a robust forward model to take advantage of both the SENSE model with explicit sensitivity estimations and the SSG model with implicit kernel relationship among coil images. The proposed approach also allows combining of coil images to increase the SNR and enables image domain regularization on estimated coil-combined single slices. We compare the performance of RI-SSG with that of SENSE and SSG using in-vivo diffusion EPI datasets with simulated and actual SMS acquisitions collected on a 3T MR scanner. Reconstructed diffusion-weighted images (DWIs) and the resulting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) maps are analyzed to evaluate the quantitative and qualitative performance of the three methods. The DWIs reconstructed by RI-SSG are closer to the single-band ground truth images than SENSE and SSG. Specifically, the proposed RI-SSG reduces the normalized root-mean-square-error (nRMSE) against ground truth images by ∼5% and increases the structural similarity index (SSIM) by ∼4% compared to SSG. All three methods produce similar fractional anisotropy (FA) maps using DTI representation, but mean diffusivity (MD) and fiber orientation estimates using RI-SSG are closer to the reference than SENSE and SSG. RI-SSG results in NODDI maps with noticeably smaller errors than those of SENSE and SSG and improves the accuracy of the mean value of orientation dispersion index (ODI) by ∼5% and the mean value of intracellular volume fraction by ∼7% in regions of interest in brain white matter compared to SSG.

Comparing multiband and singleband EPI in NODDI at 3 T: what are the implications for reproducibility and study sample sizes?

Objective

The reproducibility of Neurite orientation dispersion and density imaging (NODDI) metrics from time-saving multiband (MB) EPI compared with singleband (SB) has not been considered. This study aims to evaluate the reproducibility of NODDI parameters from SB and MB acquisitions, determine the agreement between acquisitions and estimate the sample sizes required to detect between-group change.

Methods

Brain diffusion MRI data were acquired using SB and MB (acceleration factors 2 (MB2) and 3 (MB3)) on 8 healthy subjects on 2 separate visits. NODDI maps of isotropic volume fraction (FISO), neurite density (NDI) and orientation dispersion index (ODI) were estimated. Region-of-interest analysis was performed; variability across subjects and visits was measured using coefficients of variation (CoV). Intraclass correlation coefficient and Bland–Altman analysis were performed to assess reproducibility and detect any systematic bias between SB, MB2 and MB3. Power calculations were used to determine sample sizes required to detect group differences.

Results

Both NDI and ODI were reproducible between visits; however, FISO was variable. All parameters were not reproducible across methods; a systematic bias was observed with the derived values decreasing as the MB factor increases. The number of subjects needed to detect a between-group change is not significantly different between methods; however, ODI needs considerably higher sample sizes than NDI.

Conclusions

Both SB and MB yield highly reproducible NDI and ODI measures, but direct comparison of these parameters between methods is complicated by systematic differences that exist between the two approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-020-00897-7.