Keywords: Parallel Transmit & Multiband, High-Field MRI, deep learning, parallel transmit, pTx, diffusion, B1+, 7T
Motivation: Slice-specific pTx pulses can address B1+ inhomogeneity in 2D sequences at 7T however they suffer from extended calculation time making them practically unfeasible.
Goal(s): Develop a deep learning method for rapid slice-by-slice pTx pulse design.
Approach: An unsupervised deep learning pipeline using a ShuffleNet_V2 architecture and a novel Bloch simulation-based loss function with maximum local specific absorption rate (SAR) penalty.
Results: Deep learning achieved lower Normalised-Root-Mean-Square-Error (21.56%±0.41%) than B1+-shimming (26.56%±1.10%) and the Small-Tip-Angle approximation method (25.63%±0.44%) while consuming lower specific energy deposition (SED). In-vivo evaluation in the context of diffusion MRI showed improved image quality with a calculation time of only 0.018s/slice.
Impact: The proposed deep learning method enables subject and slice-specific pTx pulse design for 2D sequences with a large number of slices in a practical time, providing full brain coverage with improved homogeneity at 7T.
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