Diffusion magnetic resonance imaging in the human hippocampal subfields using super-resolution HYDI
Nahla M H Elsaid1,2, Pierrick Coupé3,4, and Yu-Chien Wu1,2

1Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University, Indianapolis, IN, United States, 2Indiana Alzheimer Disease Center, Indianapolis, IN, United States, 3University of Bordeaux, LaBRI, UMR 5800, PICTURA, F-33400 Talence, France, 4CNRS, LaBRI, UMR 5800, PICTURA, F-33400 Talence, France


The hippocampal atrophy is known to be the most validated biomarker of Alzheimer’s disease. Accordingly, in this study, we develop a method to enable the structural connectivity mapping through tractography of the hippocampal subfields using super-resolution diffusion data.


The hippocampal atrophy is known to be the most validated biomarker of Alzheimer’s disease 1,2. Over the last few years, high-resolution anatomical magnetic resonance imaging (MRI) such as T1-weighted (T1W) or T2-weighted (T2W) imaging enabled the feasibility of submillimeter in-plane resolution of the hippocampus, which has allowed the automatic segmentation of hippocampal subfields 3,4. Using anatomical MRI, most of the previous studies focused on the volumetric changes in the hippocampal subfields. However, changes to specific hippocampal fiber pathways within the brain’s structural networks have not been closely investigated, largely due to the limited spatial resolution in diffusion MRI. With an adequate spatial resolution, diffusion MRI could provide unbiased microstructural measurements and the associated tractography for the in-vivo human hippocampal subfields. Accordingly, we developed a sub-millimeter super-resolution hybrid diffusion imaging (HYDI) 5,6 technique using the well-established collaborative patch-based super-resolution method 7. This enables the characterization of the structural connectivity of the hippocampal subfields and the delineation of the fiber tracts towards the cortex.


MR acquisition

High-resolution T1W images were acquired with the MPRAGE sequence at 0.8 x 0.8 x 0.8 mm3 resolution with TR= 2400 ms, TE= 2.22 ms, TI=1000 ms, flip-angle=8°, 256 mm field-of-view, 208 slices, and GRAPPA acceleration iPAT= 2. In addition, we acquired a high-resolution Turbo-Spin-Echo T2W images with high in-plane resolution of 0.4 x 0.4 mm in the oblique plane perpendicular to the main axis of the hippocampus, a slice thickness of 2 mm, TR= 8310 ms, TE= 50 ms, flip-angle=122°, 175 mm field-of-view, 32 slices, and GRAPPA acceleration iPAT= 2.

HYDI 5,6 was performed on a healthy volunteer. The diffusion images were acquired on a Siemens Prisma scanner using a single-shot spin-echo EPI with a multiband factor of 3, TE=74.2 ms, and TR=4164 ms, 220 mm field of view, 114 slices, isotropic resolution of 1.25mm, and 10:51 (min:sec) acquisition time. A four-shell diffusion imaging with monopolar diffusion scheme was used with b-values 500, 800, 1600, 2600 s/mm2, 134 diffusion directions, and 8 non-diffusion weighted volumes. Two sets of data were acquired with reversed phase-encode blips.


All the DW images were denoised from Rician noise using overcomplete local Principal Component Analysis as proposed in 8. FSL-topup and FSL-eddy 9, part of the FSL package version 5.0.11 (FMRIB, Oxford, UK), were used to correct motion, susceptibility and eddy current distortions. To achieve super resolution, a collaborative patch-based method 7 was then used to upsample the 1.25-cubic-mm resolution HYDI data to a submillimeter 0.625-mm-cubic resolution.


The high-resolution T1W and T2W acquisitions were used for segmenting the hippocampal subfields with the Automatic Segmentation of Hippocampal Subfields (ASHS) method 4 (Figure 1). HYDI data was used to compute DTI metrics and compartment modeling (results not shown). The hippocampal subfields were transformed from the T2W imaging space to the diffusion space using ANTs 10. In addition, DSI studio 11 was used to process the HYDI multishell data for tractography with a generalized q-sampling imaging (GQI) approach 12.

Results and Discussion

The zoomed-in box in Figure 2a demonstrates DTI eigenvectors in each hippocampal subfield. The means and standard deviations of DTI fractional anisotropy (FA) and mean diffusivity (MD) for each hippocampal subfield are shown in Figure 2b. While sulcus, a cell-free region, had lowest FA and highest MD, CA1, CA2, and subiculum had relatively high FA and low MD. White-matter connection as in tractography streamlines from the hippocampus to cortical gray matter are shown in Figure 3. It appears that the hippocampus has a direct connection to the temporal lobe, cingulum gyrus, and fornix. Tractography was performed between the hippocampal subfields to generating subfield connectivity (Figure 4). The strongest connections (as in streamline count) appear to be between the Brodmann areas 35 and 36 and between the CA1 and Subiculum. Similar information can be appreciated using the count connectogram generated using 13 (Figure 5).


We presented a feasibility study of diffusion analyses and tractography within the hippocampal subfields using super-resolution HYDI. Future directions are to refine the pipelines for clinical applications in the aging brain and Alzheimer’s disease.


The work is supported by grant NIH NIA R01 AG053993.


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11. http://dsi-studio.labsolver.org.

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Figure 1. Hippocampal subfields using the ASHS software 4, in a T2-weighted image (T2W).

Figure 2. (a) Hippocampal subfields using the ASHS method 4 then transformed to the diffusion-weighted image (DWI) with a zoomed-in copy overlaid on overlaid on the major eigenvectors of the diffusion data. (b) A validation showing the means and standard deviations of each of the FA and MD left hippocampal subfields shown in (a).

Figure 3. Delineation of the white-matter streamlines from the left hippocampus to cortical gray matter.

Figure 4. A 3D graph visualization of the connectivity matrix computed by counting the connecting streamlines between the left hippocampal subfields. The edge thickness is proportional to the value in the connectivity matrix, and the node size represents the size (in voxels) of each of the hippocampal subfields.

Figure 5. A connectogram of the streamlines count of the left hippocampal subfields generated using 13.

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