Rigid motion‐resolved prediction using deep learning for real‐time parallel‐transmission pulse design

Abstract

Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx B + 1 distributions following within-slice motion, which can then be used for tailored pTx pulse redesign.Methods: Using simulated data, conditional generative adversarial networks were trained to predict B + 1 distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground-truth (simulated, following motion) B 1 maps. Tailored pTx pulses were designed using B 1 maps at the original position (simulated, no motion) and evaluated using simulated B 1 maps at displaced position (ground-truth maps) to quantify motion-related excitation error.A second pulse was designed using predicted maps (also evaluated on groundtruth maps) to investigate improvement offered by the proposed method. Results: Predicted B + 1 maps corresponded well with ground-truth maps. Error in predicted maps was lower than motion-related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst-case flip-angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps. Conclusion:We propose a framework for predicting B + 1 maps online with deep neural networks. Predicted maps can then be used for real-time tailored pulse redesign, helping to overcome head motion-related error in pTx.

Rapid estimation of 2D relative B1+‐maps from localizers in the human heart at 7T using deep learning

“…The effect seems not to affect results or hinder shimming applications because the spatial variation of $$$ {\mathrm{B}}_1^{+} $$$ is smooth. Estimating the Tx phase turned out to be more difficult, which was also reported by Plumley et al, 29 and particularly the loss function and reference RF phase impacted the result. Although in a few cases very localized residual phase prediction errors are still observed at locations with rapidly changing phases or at singularities, an overall high similarity is observed between PR and GT phase patterns.…”

“…Some related DL approaches in UHF imaging have been recently reported for subject-specific calibration of the transmit B + 1 -fields. [25][26][27][28][29] Abbasi-Rad et al 28 1 -magnitudes from a standard localizer to determine a FA scaling factor for adiabatic RF pulses. Plumley et al 29 used DL to predict the complex, channel-wise B + 1 -maps of an 8 Tx channel head coil after motion based on an initially acquired set of B + 1 -maps.…”

“…Abbasi‐Rad et al 28 predicted channel‐combined $$$ {\mathrm{B}}_1^{+} $$$‐magnitudes from a standard localizer to determine a FA scaling factor for adiabatic RF pulses. Plumley et al 29 used DL to predict the complex, channel‐wise $$$ {\mathrm{B}}_1^{+} $$$‐maps of an 8 Tx channel head coil after motion based on an initially acquired set of $$$ {\mathrm{B}}_1^{+} $$$‐maps. Eberhardt et al 25 used a 2‐to‐16‐fold under‐sampled set of $$$ {\mathrm{B}}_1^{+} $$$‐maps for a 16 Tx channel head coil and used neural networks to augment a full set of 16 complex, channel‐wise $$$ {\mathrm{B}}_1^{+} $$$‐maps.…”

“…This is not unexpected because neural networks minimizing distance metrics tend to smooth the data. 45 The mean error for the magnitude (nRMSE, definition Plumley et al 29 ± SD) averaged over the cross-section amounts to 5.90% ± 8.87% for Tx channel 3 and 0.41% ± 1.01% for Tx channel 4, whereas the mean phase differences are 0.37 rad ± 0.38 rad for Tx channel 3 and 0.43 rad ± 0.50 rad for Tx channel 4.…”

“…Abbasi‐Rad et al 28 reduced the specific absorption rate (SAR) in T 2 ‐FLAIR imaging at UHF in the human head by determining a scaling factor for adiabatic RF pulses from a predicted channel‐combined $$$ {\mathrm{B}}_1^{+} $$$‐magnitude. Plumley et al 29 presented a system of conditional generative adversarial networks to predict how rigid motion changes complex, channel‐wise $$$ {\mathrm{B}}_1^{+} $$$‐distributions in the head. This framework is highly suited for real‐time pTx pulse re‐design , but requires the initial channel‐wise $$$ {\mathrm{B}}_1^{+} $$$‐maps.…”