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Submillimeter whole brain diffusion MRI at 7 Tesla using simultaneous multislice imaging
Xiaoping Wu1, Eddie J. Auerbach1, and Kamil Ugurbil1

1Center for Magnetic Resonance Research, Radiology, Medical School of the University of Minnesota, Minneapolis, MN, United States

Synopsis

Acquisition of whole-brain diffusion MRI at submillimeter resolutions has proven to be highly beneficial for probing the brain’s structure and connectivity. Here, we investigate how simultaneous multislice (SMS) imaging, a single-shot acquisition that has great success in large-scale cohort studies such as the Human Connectome Projects (HCP), can be used to achieve submillimeter whole-brain diffusion at 7 Tesla. We acquired SMS diffusion at 0.7-mm isotropic resolution using a standard body gradient. Our results show that these submillimeter data can be used to generate a whole-brain tractogram and a connectome comparable to those of the 1.05-mm HCP-style acquisition using denoising approaches.

Introduction

Acquisition of whole-brain diffusion MRI at submillimeter resolutions has proven highly beneficial for probing the brain’s structure and connectivity1.

Previous studies targeting in-vivo submillimeter diffusion have opted for 3D acquisitions (e.g., multi-slab2 or gSLIDER3) for improved SNR efficiency. However, these methods are intrinsically multi-shot and therefore prone to motion-induced shot-to-shot phase errors.

Here, we investigate how simultaneous multislice (SMS) imaging4,5, a pseudo-3D single-shot acquisition that has great success in large-scale cohort studies such as the Human Connectome Projects (HCP)6,7, can be used to achieve submillimeter whole-brain diffusion at 7 Tesla (7T).

We acquired SMS diffusion at 0.7-mm isotropic resolution using a standard body gradient and show that these submillimeter data can be used to generate a whole-brain tractogram and a structure connectome comparable to those of the 1.05-mm HCP-style acquisition8.

Methods

We collected human brain images on a Siemens 7T MR scanner, equipped with a standard body gradient (70 mT/m maximum strength and 200 T/m/s maximum slew rate) and 32-channel receive (32Rx) capability.

The system can be operated in a parallel-transmission (pTx) mode enabling up to 16 transmit channels.

A healthy subject who signed a consent form approved by the local IRB was scanned using the commercially-available Nova 8-channel transmit 32Rx coil and in the pTx “protected” mode to ensure RF safety.

Data acquisition:

We acquired whole-brain diffusion at 0.7-mm and 1.05-mm8,9 isotropic resolutions (the latter serving as a reference). The two imaging protocols are compared in Fig. 1.

Briefly, both acquisitions sampled the q-space in two shells with uniform angular coverage10 and utilized slab-wise pTx multi-band pulses for improved RF uniformity across the brain8.

While the 1.05-mm acquisition continued to use the GRE auto-calibration scans (ACS) for in-plane GRAPPA as in the HCP9, the 0.7-mm acquisition opted for the FLEET ACS11 which was found to minimize residual aliasing in final reconstructed images.

For both acquisitions, diffusion images of individual receive channels were reconstructed using the split slice-GRAPPA algorithm12 to minimize inter-slice signal leakage and were combined to form the final image via the SENSE1 method13 to reduce the noise floor.

Data processing and analysis:

Both datasets were processed as follows:

1) denoising with the MP-PCA method14;

2) preprocessing using the HCP minimal pipelines15;

3) estimation of fiber orientation distribution functions (fODFs) using a multi-shell multi-tissue constrained spherical deconvolution approach16,17 coupled with unsupervised multi-tissue response function estimation18 and followed by multi-tissue informed intensity normalization;

4) reconstruction of a whole-brain tractogram with improved biological accuracy by combining a probabilistic streamlines algorithm19 with the anatomically-constrained tractography framework20 and spherical-deconvolution-informed filtering of tractography (SIFT2)21;

5) generation of a structural connectome22,23 for which the edges were calculated based on the whole-brain tractogram whereas the nodes (or gray matter parcels) were defined by Desikan-Killiany atlas24.

All processing steps except for step 2, alongside associated visualization, were fulfilled using the MRtrix3 package (www.mrtrix.org). The two connectomes were also imported into MATLAB for further analysis and comparison.

Results

Despite the highly SNR-inefficient TR, the fODFs map estimated from the 0.7-mm diffusion images shared similar characteristic with that from the reference 1.05-mm data, effectively unraveling the fiber orientations in known anatomy (Fig. 2).

Correspondingly, the two resulting whole-brain tractograms presented a global pattern similar to each other (Fig. 3), both successfully delineating crossing fiber bundles as clearly identified when comparing exemplar zoom-ins.

The similarity in tractograms in turn translated into two similar connectomes (Fig. 4) sharing the same characteristics such as strong intra-hemispheric cortical connections (correlation coefficient (cc): 0.96 for the left and 0.93 for the right hemisphere), noticeable homologue connections (cc: 0.87), and strong connections with subcortical regions (cc: 0.77).

The similarity in connectome was further confirmed by inspecting selected streamlines connecting exemplar atlas regions: the left and right superior frontal regions (Fig. 5); the streamlines derived from 0.7-mm data characterized the homologue connection as well as that of the reference 1.05-mm data, effectively outlining how the two homologue regions are connected via corpus callosum.

Discussion and Conclusion

We have demonstrated that the SMS imaging can be used to accomplish submillimeter whole-brain diffusion at 7T with a standard body gradient.

Critical to this accomplishment was a synergistic combination of various techniques including pTx, FLEET ACS and denoising. Significant improvements are expected with a 64Rx coil enabling higher acceleration25.

Although largely improving the SNR, the current denoising strategy was found to not remove the noise effectively especially in subcortical regions (e.g., the thalamus).

Future work will examine alternative denoising strategies by concatenating multiple runs and/or removing the noise in the complex domain, the use of a 64Rx coil for improved SNR efficiency, and the utility of 0.7-mm diffusion for minimizing gyral bias26 and promoting the layer-specific analysis of intra-cortical architecture27,28.

Acknowledgements

The authors acknowledge Brian Hanna and John Strupp for setting up necessary computational resources. This work was supported by NIH grants U01 EB025144, P41 EB015894 and P30 NS076408.

References

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Figures

Fig. 1. Comparing imaging protocols for 0.7-mm vs 1.05-mm diffusion acquisitions. Other relevant imaging parameters common to both were: 3-fold in-plane GRAPPA, 2-fold slice acceleration, 6/8 partial Fourier, 210x210 mm2 FOV, ½ FOV inter-slice shift, sagittal slices. Both acquisitions sampled the q-space in two shells (b-values = 1000 and 2000 s/mm2), with the listed diffusion-weighted and b0 images acquired twice with inverted phase encode directions (anterior-posterior (AP) and PA). In each case, the total acquisition was divided into four runs (each of ~18.5 and ~10 minutes for 0.7-mm and 1.05-mm acquisitions, respectively).

Fig. 2. Comparing fiber orientation distribution functions (fODFs) estimated from diffusion at 0.7-mm (left) vs 1.05-mm (right) isotropic resolutions. The fODFs are displayed in directionally encoded colors with red, green and blue colors representing bidirectional orientations in the left-right, anterior-posterior and inferior-superior dimensions, respectively. In both cases, the voxel-specific fODFs were derived using the multi-tissue multi-shell constrained spherical deconvolution method. Note that the 0.7-mm data gave rise to an fODFs map that shared similar characteristics with that of the reference 1.05-mm data.

Fig. 3. Comparing whole-brain probabilistic tractography estimated from diffusion at 0.7-mm (top) vs 1.05-mm (bottom) isotropic resolutions. Shown are whole-brain tractograms (each displaying 200 thousand white-matter streamlines randomly selected from the respective full tractograms created with 10 million streamlines) in directionally encoded colors, overlaid on T1-weighted images. The full tractograms were reconstructed using the fODFs (Fig. 2), following the anatomically-constrained tractography framework and through dynamic seeding determined by the SIFT model. Note that the 0.7-mm data yielded a whole-brain tractogram exhibiting similar characteristics to that of the reference 1.05-mm data, which can be better appreciated by comparing the two zoom-ins (rightmost column).

Fig. 4. Comparing structural connectomes derived from diffusion at 0.7-mm (left) vs 1.05-mm (right) isotropic resolutions. Here, the full whole-brain tractogram (consisting of 10 million streamlines) was further used to estimate the connectome through spherical-deconvolution-informed filtering of tractograms (SIFT2) and using Desikan-Killiany atlas (with cerebellum removed). Note that both connectomes presented such characteristics as strong intra-hemispheric cortical connections (correlation coefficient (cc): 0.96 for the left and 0.93 for the right hemisphere), noticeable homologue connections (cc: 0.87), and strong connections with subcortical regions (cc: 0.77).

Fig. 5. Comparing exemplar homologue connections derived from diffusion at 0.7-mm (left) vs 1.05-mm (right) isotropic resolutions. Shown here are volume rendering of the streamlines connecting the left and right superior frontal cortical regions, overlaid on the T1-weighted image. These two homologue regions appeared to have a relatively strong connection as observed in the connectomes shown in Fig. 4. Note that the streamlines derived from 0.7-mm data characterized the homologue connection as well as that of the reference 1.05-mm data, showing how the two homologue regions are connected via corpus callosum.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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