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Enhancing Self-Navigated Interleaved Spiral with ESPIRiT (eSNAILS)
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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.
Title: Enhancing Self-Navigated Interleaved Spiral with ESPIRiT (eSNAILS)
Description:
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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