Coil-combined split slice-GRAPPA for simultaneous multi-slice diffusion MRI |
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| Institution: | 1. Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children''s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA;2. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands;3. Department of Pediatrics, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA;4. Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA;5. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 MA Avenue, Cambridge, MA 02139, USA;6. Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, 77 MA Avenue, Cambridge, MA 02139, USA;7. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 MA Avenue, Cambridge, MA 02139, USA;8. Athinoula A. Martinos Center for Biomedical Imaging, MA General Hospital, 149 13th Street, Charlestown, MA 02129, USA |
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| Abstract: | Objective: To develop a kernel optimization method called coil-combined split slice-GRAPPA (CC-SSG) to improve the accuracy of the reconstructed coil-combined images for simultaneous multi-slice (SMS) diffusion weighted imaging (DWI) data.Methods: The CC-SSG method optimizes the tuning parameters in the k-space SSG kernels to achieve an optimal trade-off between the intra-slice artifact and inter-slice leakage after the root-sum-of-squares (rSOS) coil combining of the de-aliased SMS DWI data. A detailed analysis is conducted to evaluate the contributions of the intra-slice artifact and inter-slice leakage to the total reconstruction error after coil combining.Results: Comparisons of the proposed CC-SSG method with the slice-GRAPPA (SG) and split slice-GRAPPA (SSG) methods are provided using two in-vivo readout-segmented (RS) EPI datasets collected from stroke patients. The CC-SSG method demonstrates improved accuracy of the reconstructed coil-combined images and the estimated diffusion tensor imaging (DTI) maps.Conclusion: CC-SSG strikes a good balance between the intra-slice artifact and inter-slice leakage for rSOS coil combining, and so can yield better reconstruction performance compared to SG and SSG for rSOS reconstruction. The optimal trade-off between the two artifacts is robust to the contrast of SMS data and the choice of the coil combining method. |
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