Tailored and universal parallel transmit broadband pulses for homogeneous 3D excitation of the human heart at 7T.

PURPOSE: To research and evaluate the performance of broadband tailored kT-point pulses (TP) and universal pulses (UP) for homogeneous excitation of the human heart at 7T. METHODS: Relative 3D B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps of the thorax were acquired from 29 healthy volunteers. TP and UP were designed using the small-tip-angle approximation for a different composition of up to seven resonance frequencies. TP were computed for each of the 29 B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps, and UPs were calculated using 22 B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps and tested in seven testcases. The performance of the pulses was analyzed using the coefficient of variation (CV) in the 3D heart volumes. The 3D gradient-echo (GRE) scans were acquired for the seven testcases to qualitatively validate the B 1 + $$ {\mathrm{B}}_1^{+} $$ -predictions. RESULTS: Single- and double-frequency optimized pulses achieved homogeneity in flip angle (FA) for the frequencies they were optimized for, while the broadband pulses achieved uniformity in FA across a 1300 Hz frequency range. CONCLUSION: Broadband TP and UP can be used for homogeneous excitation of the heart volume across a 1300 Hz frequency range, including the water and the main six fat peaks, or with longer pulse durations and higher FAs for a smaller transmit bandwidth. Moreover, despite large inter-volunteer variations, broadband UP can be used for calibration-free 3D heart FA homogenization in time-critical situations.

Rapid estimation of 2D relative B 1 + -maps from localizers in the human heart at 7T using deep learning.

PURPOSE: Subject-tailored parallel transmission pulses for ultra-high fields body applications are typically calculated based on subject-specific B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps of all transmit channels, which require lengthy adjustment times. This study investigates the feasibility of using deep learning to estimate complex, channel-wise, relative 2D B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps from a single gradient echo localizer to overcome long calibration times. METHODS: 126 channel-wise, complex, relative 2D B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps of the human heart from 44 subjects were acquired at 7T using a Cartesian, cardiac gradient-echo sequence obtained under breath-hold to create a library for network training and cross-validation. The deep learning predicted maps were qualitatively compared to the ground truth. Phase-only B 1 + $$ {\mathrm{B}}_1^{+} $$ -shimming was subsequently performed on the estimated B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps for a region of interest covering the heart. The proposed network was applied at 7T to 3 unseen test subjects. RESULTS: The deep learning-based B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps, derived in approximately 0.2 seconds, match the ground truth for the magnitude and phase. The static, phase-only pulse design performs best when maximizing the mean transmission efficiency. In-vivo application of the proposed network to unseen subjects demonstrates the feasibility of this approach: the network yields predicted B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps comparable to the acquired ground truth and anatomical scans reflect the resulting B 1 + $$ {\mathrm{B}}_1^{+} $$ -pattern using the deep learning-based maps. CONCLUSION: The feasibility of estimating 2D relative B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps from initial localizer scans of the human heart at 7T using deep learning is successfully demonstrated. Because the technique requires only sub-seconds to derive channel-wise B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps, it offers high potential for advancing clinical body imaging at ultra-high fields.