Joint Virtual Coil Reconstruction with Background Phase Matching for Highly Accelerated Diffusion Echo-Planar Imaging
Congyu Liao1,2, Mary Kate Manhard2, Berkin Bilgic2, Qiuyun Fan2, Haifeng Wang2,3, Sohyun Han2, Daniel Joseph Park2, Fuyixue Wang4, Jianhui Zhong1, Lawrence L Wald2, and Kawin Setsompop2

1Department of Biomedical Engineering, Zhejiang University, Hangzhou, China, 2Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 4Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, United States


We proposed a joint-virtual-coil (VC) acquisition/reconstruction method to improve accelerated single-shot EPI (SS-EPI) in diffusion imaging (DI). A background phase correction scheme for matching the phase of reference training data with accelerated diffusion-weighted data was developed for robust reconstruction. Additional Gy prewinding-blips were added to the EPIs, to create complementary shifted-ky sampling strategy across TRs, which help better utilizes smooth-phase and joint-information priors in the joint-virtual-coil (jVC) reconstruction. The proposed method was demonstrated in highly-accelerated DI with SS-EPI and extended to generalized slice dithered enhanced resolution (gSlider) acquisition to achieve efficient high-resolution DI.


Diffusion imaging (DI) with single-shot EPI (SS-EPI) is widely used for clinical and neuroscience applications. To reduce acquisition time and mitigate distortions, in-plane and slice accelerations are applied(1,2), where total acceleration is typically limited to 4-6 fold to minimize g-factor noise (e.g. Rinplane MB=2x3). The VC concept has been demonstrated to enable higher in-plane and multi-band accelerations in a number of applications (3–6). However, its usage in DI has been restricted by shot-to-shot background phase variations of DI, which violates VC assumption. To overcome this, we propose a VC reconstruction framework with background phase matching and demonstrates its utility in 9-fold accelerated DI (Rinplane MB=3x3). Furthermore, we combined joint-reconstruction methods (7–9) with VC concept (jVC) for joint-reconstruction across five RF-encoded volumes in gSlider-EPI(10). Shifted-ky sampling strategy was implemented to improve VC and jVC reconstructions.


Fig.1a shows the sequence diagram, where different Gy prewinding-blips are added across TRs to generate differing linear image phases or ky shifts (Fig.1b). Fig.1c shows the benefits of such ky shifts in creating more unique k-space source points for VC and JVC reconstructions. Fig.2a shows the flowchart of VC reconstruction, where conventional reconstructed EPI data are used for the phase matching process, to add diffusion-direction-specific background phase into the training data of VC-GRAPPA (Fig.2b). The under-sampled raw data are then re-reconstructed using VC-GRAPPA, where a set of VC-GRAPPA kernel weights is created specifically for each diffusion weighted acquisition/direction. For jVC-GRAPPA reconstruction, the multi-kyshift VC-reconstructed data from different TRs are utilized as training data, and the under-sampled raw EPI data are then re-reconstructed again by the estimated weights.

Data: the following were acquired using a Siemens Prisma 3T Scanner:

i) SS-EPI: 1.5mm and 1.2mm isotropic datasets, both with Rinplane MB=3x3, FOV: 230x230mm2, b=1000s/mm2, 64-directions, with constant 0.75 ky-shift across TRs. Total of 93 and 111 slices were acquired for whole-brain coverage at TE/TR/Tacq= 69ms/3.3s/4min and 78ms/4.4s/5min, respectively.

ii) gSlider-EPI: whole-brain 0.86mm isotropic, RinplanexMBxgSlider=4x2x5, FOV: 220x220x129mm2, b=1000s/mm2, 30-directions, Tacq12min. For each direction, 5 RF-encoded thin-slab volumes (4.3mm-slabs) are acquired sequentially and combined to reconstruct 0.86mm slices. Ky-shifts are looped by 1, 2 and 3 across TRs (RF-encodings).

Reconstructions: VC-GRAPPA was applied to SS-EPI, and both jVC- and VC-GRAPPA were applied to gSlider-EPI. For jVC, three consecutive RF-encodings with ky-shift of 1, 2 and 3 are jointly reconstructed. Reconstructions were compared to conventional-GRAPPA.


Figure 3 and Fig. 4(a) show that VC-GRAPPA improves the quality of both 1.5 and 1.2mm isotropic datasets at high acceleration factors, where conventional reconstruction results in undesirable dark patches at the center of images. The blue arrows highlight image structures that were successfully recovered through VC reconstruction. Figure 4(b) show the 64-directions averaged-DWIs and colored-FA maps obtained from conventional and VC-GRAPPA reconstructions of the 1.2mm data. The results demonstrate that both averaged-DWI and FA-map were improved through VC-GRAPPA, with significant reduction in artifacts and g-factor noise for this 9-fold accelerated acquisition. The total acquisition time was only 5min, and longer scan across more directions could improve the SNR of this high-isotropic resolution acquisition further still.

Figure 5 shows that jVC-GRAPPA can be used to improve the reconstruction of gSlider data, where (a) shows the reconstructed RF-encoded thin-slabs (3 out of 5 shown) and (b) shows representative 2 of 5 resolved slices at 0.86 mm slice thickness. It can be observed that joint-reconstruction across slab-encoding improves reconstruction quality without smoothing away structural differences that exist across the slices within each slab.


In this study, VC and jVC reconstructions were employed to achieved high-quality reconstructions at 8-9 fold acceleration in high resolution diffusion imaging. Complementary shifted-ky sampling strategy across TRs was used to better take advantage of smooth-phase and joint-information priors in the reconstruction. The proposed framework, which utilizes the conjugate phase information, can also be easily incorporated to SENSE(11) based single and multi-shot diffusion reconstructions (e.g., MUSE(12), low rank(13) and MUSSELS(14)), to effectively double the number of coil channels for reconstruction.


This work was supported in part by NIH research grants: R01EB020613, R01EB019437, R24MH106096, P41EB015896, and the shared instrumentation grants: S10RR023401, S10RR019307, S10RR019254, S10RR023043


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Figure 1. (a) The sequence diagram of EPI acquisition with additional Gy blips across TRs (marked in yellow, located in the right portion of the sub-figure). The SMS training data are acquired by SE-EPI and the in-plane training data are acquired by FLEET scan. (b) The shifted ky lines generate a linear ramp phase in the image domain, which improves VC reconstruction. (c) joint virtual coil reconstruction with different ky shifts.

Figure 2. (a) The flowchart of conventional reconstruction and virtual coil reconstruction. (b) The flowchart of background phase matching method. The phase maps of the training data are updated after Hamming filtering to generate unique VC-GRAPPA kernels to reconstruct data for each diffusion direction. (c) The flowchart of joint VC reconstruction, where VC reconstructed images are used as training data, and the VC extended EPI data are estimated by multi-kyshifted training data.

Figure 3. Reconstructions of whole-brain 1.5 mm3 diffusion imaging data, acquired with RinplanexMB =3x3. Artifacts in conventional GRAPPA reconstruction (highlighted by blue arrows) are mitigated through VC-GRAPPA. A ky-shift of 0.75 was used in the acquisition to help maximize the utility of phase prior in VC-GRAPPA reconstruction.

Figure 4. Conventional and VC-GRAPPA reconstructions of 1.2x1.2x1.2 mm3 data, (a) images at two representative diffusion directions, and (b) 64-direction averaged DWIs and FA maps. VC-GRAPPA provides significant reduction in artifacts and g-factor noise for this 9-fold accelerated acquisition.

Figure 5. Comparison of conventional and jVC reconstructions for gSlider acquisition at 860um isotropic resolution (Rinplane x MB x gSlider = 4x2x5). (a) Reconstructed gSlide-encoded thin-slabs at 0.86x0.86x4.3mm3 (3 out of 5 shown), and (b) final 860um isotropic resolution reconstruction of two representative slices within the same slab. jVC was able to improve the reconstruction performance while retaining structural differences that exist across the slices within each slab.

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)