Simultaneous Multi-Slice MRI using Cartesian and Radial FLASH and Regularized Nonlinear Inversion: SMS-NLINV
arXiv:1705.04135 · doi:10.1002/mrm.26878
Abstract
Purpose: The development of a calibrationless parallel imaging method for accelerated simultaneous multi-slice (SMS) MRI based on Regularized Nonlinear Inversion (NLINV), evaluated using Cartesian and radial FLASH. Theory and Methods: NLINV is a parallel imaging method that jointly estimates image content and coil sensitivities using a Newton-type method with regularization. Here, NLINV is extended to SMS-NLINV for reconstruction and separation of all simultaneously acquired slices. The performance of the extended method is evaluated for different sampling schemes using phantom and in-vivo experiments based on Cartesian and radial SMS-FLASH sequences. Results: The basic algorithm was validated in Cartesian experiments by comparison with ESPIRiT. For Cartesian and radial sampling, improved results are demonstrated compared to single-slice experiments, and it is further shown that sampling schemes using complementary samples outperform schemes with the same samples in each partition. Conclusion: The extension of the NLINV algorithm for SMS data was implemented and successfully demonstrated in combination with a Cartesian and radial SMS-FLASH sequence.
Part of this work has been presented at the ISMRM Annual Conference 2016 (Singapore) and 2017 (Honolulu). 25 pages, 8+4 figures
Cited by in corpus (7)
- Robust Compressed Sensing MRI with Deep Generative Priors
- Simple Auto-Calibrated Gradient Delay Estimation From Few Spokes Using Radial Intersections (RING)
- Cardiac and Respiratory Self-Gating in Radial MRI using an Adapted Singular Spectrum Analysis (SSA-FARY)
- Model-Based Reconstruction for Simultaneous Multi-Slice T1 Mapping using Single-Shot Inversion-Recovery Radial FLASH
- Frequency-modulated SSFP with radial sampling and subspace reconstruction: A time-efficient alternative to phase-cycled bSSFP
- Phase-Pole-Free Images and Smooth Coil Sensitivity Maps by Regularized Nonlinear Inversion
- Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priors