Application of Evolution Strategies to the Design of SAR Efficient Parallel Transmit Multi-Spoke Pulses for Ultra-High Field MRI

Abstract

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Robust RF shimming and small‐tip‐angle multispoke pulse design with finite‐difference regularization

“…Compared with other MLS‐based methods to address the null issue, the FD‐MLS alone calculates robust RF solutions without extra stacked steps such as random initialization, 9,18,19 evolution strategy, 22 or potentially machine learning 29 . It also does not rely on regional‐growing masking, 20 which can lead to suboptimal solutions 22 . Fundamentally, the FD‐MLS is a simple variant of MLS that can be further combined with these approaches to ensure more robust performance 18‐22 .…”

“…22 Fundamentally, the FD-MLS is a simple variant of MLS that can be further combined with these approaches to ensure more robust performance. [18][19][20][21][22] This study also found the null solutions can have smaller total cost or even smaller FA error than null-free solutions (Figures 1 and 3). The MLS results with 1 ∕N x = 3.16 × 10 − 3 in Figure 1 were validated to be the global minimum by enumeration F I G U R E 2 Comparison of MLS and FD-MLS for small-tip-angle (STA) multispoke pulse with different 1.…”

“…A robust RF shimming and STA multispoke pulse‐design algorithm FD‐MLS is presented by optimizing a better target phase with FD than MLS. Compared with other MLS‐based methods to address the null issue, the FD‐MLS alone calculates robust RF solutions without extra stacked steps such as random initialization, 9,18,19 evolution strategy, 22 or potentially machine learning 29 . It also does not rely on regional‐growing masking, 20 which can lead to suboptimal solutions 22 .…”

“…Some efforts to address this issue were reported in the literature, based on random initializations, 9,18,19 region growing, 20 quadratic programming with hard constraints, 18,21 and evolution strategy. 22 However, most of these algorithms can still generate unwanted null solutions, as reported by subsequent studies. 10,17,22 Here a finite-difference regularizer is proposed for MLS that effectively avoids unwanted null solutions for both RF shimming and STA multispoke pulses, and improves the monotonicity of the FA error and RF power trade-off relationship for the latter; hence, it enables simple operation for patient-tailored 2D multislice imaging with UHF MRI.…”

“…Some efforts to address this issue were reported in the literature, based on random initializations, 9,18,19 region growing, 20 quadratic programming with hard constraints, 18,21 and evolution strategy 22 . However, most of these algorithms can still generate unwanted null solutions, as reported by subsequent studies 10,17,22 …”