Simultaneous multi-slice MRI using cartesian and radial FLASH and regularized nonlinear inversion: SMS-NLINV.

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Medical Physics
• Image Reconstruction

Background:

• Parallel imaging techniques accelerate MRI acquisition by undersampling k-space.
• Simultaneous Multi-Slice (SMS) imaging further enhances acceleration by acquiring multiple slices in a single TR.
• Regularized Nonlinear Inversion (NLINV) is a model-based parallel imaging method that jointly reconstructs images and coil sensitivities.

Purpose of the Study:

• To develop and evaluate a calibrationless parallel imaging method for accelerated Simultaneous Multi-Slice (SMS) MRI.
• To extend the Regularized Nonlinear Inversion (NLINV) algorithm for SMS data reconstruction and slice separation.
• To assess the performance of the extended SMS-NLINV method using Cartesian and radial sampling schemes.

Main Methods:

• Extension of the NLINV algorithm to handle simultaneously acquired multi-slice data (SMS-NLINV).
• Joint estimation of image content and coil sensitivities using a regularized Newton-type method.
• Evaluation using phantom and in vivo experiments with Cartesian and radial SMS-FLASH sequences.

Main Results:

• Validation of the basic NLINV algorithm against ESPIRiT in Cartesian experiments.
• Demonstration of improved results for both Cartesian and radial SMS sampling compared to single-slice acquisitions.
• Evidence that complementary sampling schemes outperform schemes with identical samples across partitions.

Conclusions:

• Successful implementation and demonstration of the SMS-NLINV algorithm for accelerated MRI.
• The developed method enables efficient reconstruction and separation of simultaneously acquired slices.
• The study highlights the benefits of complementary sampling in SMS MRI for improved performance.