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Abstract #1904

Enhancing Self-Navigated Interleaved Spiral with ESPIRiT (eSNAILS)

Xingwang Yong1,2,3, Shohei Fujita2,3,4,5, Yohan Jun2,3, Jaejin Cho2,3, Qiang Liu6, Yi Zhang1, and Berkin Bilgic2,3,7
1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, 2Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 3Department of Radiology, Harvard Medical School, Boston, MA, United States, 4Department of Radiology, Juntendo University, Tokyo, Japan, 5Department of Radiology, The University of Tokyo, Tokyo, Japan, 6Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States, 7Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States

Synopsis

Keywords: Image Reconstruction, Data Acquisition, multi-shot; self-navigation

Motivation: Current methods for estimation of shot-to-shot phase variations in multi-shot DWI may not fully exploit the correlations in data.

Goal(s): To propose a method which efficiently uses correlations between shots and coils to calculate composite sensitivities and then improve multi-shot DWI reconstruction.

Approach: A multi-shot, dual density spiral sequence was designed, with each shot having fully sampled k-space center and undersampled periphery. The center of all shots and all coils are concatenated and fed into ESPIRiT to estimate sensitivities.

Results: The proposed method successfully estimated shot-to-shot phase variations and yielded comparable results to the reference locally low-rank regularized reconstruction which requires parameter tuning.

Impact: The proposed eSNAILS demonstrated the ability of estimating composite sensitivities that incorporate shot-to-shot phase variations. Compared to low-rank modeling methods that assume phase smoothness, eSNAILS can handle cases where there are abrupt phase changes and does not require parameter tuning.

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Keywords