Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany.
Technische Universität Berlin, Biomedical Engineering, Berlin, Germany.
Magn Reson Med. 2023 Mar;89(3):1002-1015. doi: 10.1002/mrm.29510. Epub 2022 Nov 6.
Subject-tailored parallel transmission pulses for ultra-high fields body applications are typically calculated based on subject-specific -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 -maps from a single gradient echo localizer to overcome long calibration times.
126 channel-wise, complex, relative 2D -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 -shimming was subsequently performed on the estimated -maps for a region of interest covering the heart. The proposed network was applied at 7T to 3 unseen test subjects.
The deep learning-based -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 -maps comparable to the acquired ground truth and anatomical scans reflect the resulting -pattern using the deep learning-based maps.
The feasibility of estimating 2D relative -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 -maps, it offers high potential for advancing clinical body imaging at ultra-high fields.
针对超高场体部应用，通常基于每个发射通道的个体特定的 -映射来计算针对个体的平行发射脉冲，这需要较长的调整时间。本研究旨在探讨使用深度学习从单个梯度回波定位器估计复杂的、通道特定的二维相对 -映射的可行性，以克服长校准时间。
在 7T 下使用笛卡尔、心脏梯度回波序列采集了 44 名受试者的 126 个通道的复杂、二维相对 -映射，该序列采用屏气采集以创建网络训练和交叉验证的库。定性比较了深度学习预测的映射与真实值。随后对包含心脏的感兴趣区域的估计的 -映射进行了仅相位 -匀场。该网络应用于 3 名未见过的测试对象。
基于深度学习的映射，大约在 0.2 秒内导出，其幅度和相位与真实值匹配。当最大化平均传输效率时，静态、仅相位的脉冲设计表现最佳。该方法在未见过的受试者中的应用，证明了这种方法的可行性：该网络产生的预测的 -映射与获取的真实值相当，并且解剖扫描反映了使用基于深度学习的映射的结果的 -模式。
成功证明了使用深度学习从 7T 人体心脏的初始定位器扫描中估计二维相对 -映射的可行性。由于该技术仅需几秒钟即可导出通道特定的 -映射，因此它为在超高场推进临床体部成像提供了很大的潜力。
