Dongmyung Shin1, Jiye Kim1, Juhyung Park1, and Jongho Lee1
1Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of
1Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of
A neural net powered RF design, DeepRF, is investigated. The consistency of the method is confirmed by repeating the same design. The importance of the two modules, generation and refinement, is verified through the ablation. The scalability is validated by changing a design parameter.
An overview of DeepRF. (a) In the RF generation module, a series of RF values (i.e., actions) are generated from the RNN agent to shape an RF envelope (Nth RF), and the virtual MRI simulates a slice profile. Then, a value of the objective function (e.g., a difference between simulated and desired profiles) is calculated from which the agent changes its behavior to generate a next RF pulse ((N+1)th RF). (b) In the RF refinement module, an RF pulse is refined (Mth RF to (M+1)th RF) with respect to the objective function using RF value changes (∆RF) calculated from the Bloch graph.
The results of the DeepRF and SLR pulse designs with different TBWs (4.3, 5.6, 6.8, and 8.1). For all the designs, the pulse shapes of the DeepRF pulses are clearly different from those of the SLR pulses (1st and 2nd columns). The slice profiles from the DeepRF and SLR pulses are almost identical (3rd and 4th columns). The SARs of the DeepRF pulses are smaller than those of the SLR pulses in all TBWs (1st column).