Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition

Abstract

Purpose: To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI.

Methods: Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction.

Results: We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion.

Conclusion: The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.

Keywords: Diffusion-compartment imaging; diffusion-weighted MRI; image-based navigation; intra-volume motion; motion tracking; motion-robust; simultaneous multi-slice; slice registration.

Figure 1:

(a) Arrows point at intra-slice motion artifacts seen in simultaneously acquired slices in an interleaved, 2-band axial SMS DWI: fast motion happened at two time points (arrows of different colors) during this SMS DWI acquisition, and each time it affected four slices (two distant coupled slices and their subsequent coupled slices that appear one slice away from the first couple due to the interleaved acquisition scheme). Partial signal loss is observed in a corrupted axial slice on the right. (b) Outliers of the proposed mean and median metrics on a difference filtered image are used to detect intra-slice motion (x-axis is the slice number). This outlier detection framework is also strengthen by the interleaved acquisition scheme which reduces slice cross-talk and the correlation of motion-induced intra-slice distortion. As compared to a single slice, two rigidly-coupled slices in an SMS sequence provide a multi-plane 3D coverage of the anatomy, therefore they mitigate the ill-posed problem of slice-to-volume registration. This is particularly useful in brain boundaries where single slices do not have sufficient image features to guide registration. Simultaneously acquired slices in an SMS sequence are distant from each other, thus a slice in the brain boundary, that has limited anatomical features to guide registration, is coupled with a slice in the middle of the brain that is rich in content, and can regulate the registration process for self-navigated motion tracking.

Figure 2:

Axial (top), coronal (middle) and sagittal (bottom) views of the color FA maps obtained from DTIs of an adult volunteer. Our proposed method, MT-SMR, generated color FA nearly identical to the motion-free reference standard. VVR, SVR, and MT-SVR correction strategies show differences in color FA to a varying extent.

Figure 3:

FA value differences (Δ_FA) between the reference standard and each motion-correction method in the CC and Cg ROIs based on the extend of motion (mm). Regardless of the extend of motion, MT-SMR generated less errors in most experiments.

Figure 4:

The model selection map in an axial view (top row) and crossing fibers overlaid on the T1-weighted image in a coronal view (bottom row) of corona radiata where three major fiber pathways (callosal and corticospinal tracts, and the superior longitudinal fasciculus) intersect. The model selection map shows the number of estimated crossing fibers: cyan, tan, green and black colors represent 3, 2, 1, and 0 crossing fibers, respectively. The proposed method, MT-SMR, generated results that were most similar to the reference standard. The results were quantitatively evaluated in Table 2.

Figure 5:

DTI and DCI reconstruction results for DWI scan of a 10-years-old child. Similar regularization parameters were used in all DCI reconstructions. Comparing DTI (first row), DCI (second row), and model selection maps showing number of computed crossing fibers (third row) for five reconstructions: (a) Uncorrected, (b) VVR, (c) SVR, (d) MT-SVR, and (e) MT-SMR. Uncorrected and VVR are missing many crossing fibers because of motion. As can be seen inside the circle in the second row, estimated jittering motion parameters in SVR (second row in Figure 6) results in disordered crossing fiber estimation compared to MT-SVR and MT-SMR. Different colors in the model selection map show the estimated number of crossing fibers in this highlighted region of the brain where three major fiber bundles cross. Black, green, pink, and cyan correspond to 0, 1, 2, and 3 crossing fibers respectively. The model selection map obtained from MT-SMR reveals fiber microstructure that best matches our knowledge of anatomy among all compared reconstructions. The average number of estimated crossing fibers obtained from MT-SMR is larger than the other methods in this figure also confirming the results in Table 2.

Figure 6:

Estimated motion, rotation (a) and translation (b) parameters over time (only 19 volumes are shown) in a 10-years-old subject using VVR, SVR, MT-SVR and MT-SMR methods. The proposed method, MT-SMR, achieves accurate and smooth motion tracking through the registration of simultaneously-acquired distant slices and robust state-space estimation.

Figure 7:

The analysis of FA values obtained from five algorithms (Uncorrected, VVR, SVR, MT-SVR, and MT-SMR) in four regions-of-interest containing mainly unidirectional fiber pathways in pediatric patients. Boxplots show the median, maximum, minimum and inter-quartile ranges of FA values among 20 subjects. CC, Cin, LIC, and Pons refer to corpus callosum, cingulum, limbs of the internal capsule, and pons, respectively. Uncompensated motion results in image blur which lowers the FA values in these regions. The highest FA values in all regions were obtained from the proposed method (MT-SMR), which indicates motion-robust reconstruction.

Figure 8:

Comparing FA values obtained from Uncorrected data, VVR, SVR, MT-SVR, and MT-SMR methods in four regions (CC, Cin, LIC, and pons) in 20 pediatric subjects as a function of the amount of motion estimated for these cases. These results show that 1) the highest FA values in all these regions were almost always obtained from MT-SMR, while the values obtained from Uncorrected and VVR were low, and 2) FA values obtained from Uncorrected and VVR strongly decreased with increased motion. This was not the case for SVR, MT-SVR, and MT-SMR. This analysis shows that MT-SMR, in particular, was robust to motion.