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10.5 特斯拉下用于人体头部成像的 8 通道辐射天线阵列的评估。

Evaluation of 8-Channel Radiative Antenna Arrays for Human Head Imaging at 10.5 Tesla.

机构信息

Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, USA.

Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea.

出版信息

Sensors (Basel). 2021 Sep 8;21(18):6000. doi: 10.3390/s21186000.

Abstract

For human head magnetic resonance imaging at 10.5 tesla (T), we built an 8-channel transceiver dipole antenna array and evaluated the influence of coaxial feed cables. The influence of coaxial feed cables was evaluated in simulation and compared against a physically constructed array in terms of transmit magnetic field (B) and specific absorption rate (SAR) efficiency. A substantial drop (23.1% in simulation and 20.7% in experiment) in B efficiency was observed with a tight coaxial feed cable setup. For the investigation of the feed location, the center-fed dipole antenna array was compared to two 8-channel end-fed arrays: monopole and sleeve antenna arrays. The simulation results with a phantom indicate that these arrays achieved ~24% higher SAR efficiency compared to the dipole antenna array. For a human head model, we observed 30.8% lower SAR efficiency with the 8-channel monopole antenna array compared to the phantom. Importantly, our simulation with the human model indicates that the sleeve antenna arrays can achieve 23.8% and 21% higher SAR efficiency compared to the dipole and monopole antenna arrays, respectively. Finally, we obtained high-resolution human cadaver images at 10.5 T with the 8-channel sleeve antenna array.

摘要

针对 10.5T 人体头部磁共振成像,我们构建了一个 8 通道收发偶极子天线阵列,并评估了同轴线馈线的影响。我们在模拟中评估了同轴线馈线的影响,并将其与物理构建的阵列在发射磁场 (B) 和比吸收率 (SAR) 效率方面进行了比较。在紧密的同轴线馈线设置下,B 效率显著下降(模拟中下降 23.1%,实验中下降 20.7%)。为了研究馈电位置,我们将中心馈电偶极子天线阵列与两个 8 通道端馈阵列:单极天线和套管天线阵列进行了比较。使用仿体的模拟结果表明,与偶极子天线阵列相比,这些阵列的 SAR 效率提高了约 24%。对于人体头部模型,我们观察到 8 通道单极天线阵列的 SAR 效率比仿体低 30.8%。重要的是,我们对人体模型的模拟表明,套管天线阵列与偶极子和单极天线阵列相比,SAR 效率分别提高了 23.8%和 21%。最后,我们使用 8 通道套管天线阵列在 10.5T 下获得了高分辨率的人体尸体图像。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c5b/8469352/56d6db03ae7e/sensors-21-06000-g001.jpg

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