Dual-Resonant RF Coil for Proton and Phosphorus Imaging at 7 Tesla MRI

IF 2.5 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC International Journal of Imaging Systems and Technology Pub Date : 2025-04-10 DOI:10.1002/ima.70081
Ashraf Abuelhaija, Gameel Saleh, Emad Awada, Sanaa Salama, Samer Issa, Osama Nashwan
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Abstract

Magnetic resonance spectroscopy (MRS) provides a non-invasive method for examining metabolic alterations associated with diseases. While 1H-based MRS is commonly employed, its effectiveness is often limited by signal interference from water, reducing the accuracy of metabolite differentiation. In contrast, X-nuclei MRS leverages the broader chemical shift dispersion of non-hydrogen nuclei to enhance the ability to distinguish between metabolites. This article presents the design and analysis of a dual-resonant meandered coil for 7 Tesla magnetic resonance imaging (MRI), to simultaneously help in image hydrogen protons (1H) and detect Phosphorus (31P) atomic nuclei at 298 MHz and 120.6 MHz, respectively. Both single-channel and four-channel configurations were designed and analyzed. The single-channel coil integrates an LC network for dual resonance, achieving excellent impedance matching (S11 < −10 dB) and a homogeneous magnetic field distribution within the region of interest. A transmission-line-based matching network was implemented to optimize performance at both frequencies. The four-channel coil was simulated using CST Microwave Studio and experimentally validated. Simulations demonstrated impedance matching and minimal mutual coupling of −38 dB at 298 MHz and −24 dB at 120.6 MHz. The measured S-parameters confirmed these results, showing high decoupling and robust performance across all channels. The prototype featured integrated LC networks and optimized meander structures, ensuring efficient power transmission and uniform field distribution. This work highlights the effectiveness of the proposed dual-resonant coil designs for MRS applications, offering promising potential for advanced clinical diagnostics.

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双共振射频线圈在7特斯拉磁共振成像中的质子和磷成像
磁共振波谱(MRS)为检查与疾病相关的代谢改变提供了一种非侵入性方法。虽然通常采用基于1h的MRS,但其有效性往往受到水信号干扰的限制,降低了代谢物分化的准确性。相比之下,x核MRS利用非氢核更广泛的化学位移分散来增强区分代谢物的能力。本文介绍了用于7特斯拉磁共振成像(MRI)的双共振弯曲线圈的设计和分析,以同时帮助在298 MHz和120.6 MHz分别成像氢质子(1H)和探测磷(31P)原子核。设计并分析了单通道和四通道两种结构。单通道线圈集成了用于双谐振的LC网络,实现了出色的阻抗匹配(S11 <−10 dB)和感兴趣区域内均匀的磁场分布。实现了一种基于传输线的匹配网络,以优化两个频率下的性能。利用CST Microwave Studio对四通道线圈进行了仿真,并进行了实验验证。仿真结果表明,阻抗匹配和最小互耦在298 MHz和120.6 MHz时分别为−38 dB和−24 dB。测量的s参数证实了这些结果,在所有通道中都显示出高解耦和稳健的性能。原型机采用集成的LC网络和优化的弯曲结构,确保了高效的电力传输和均匀的场分布。这项工作强调了所提出的双共振线圈设计在MRS应用中的有效性,为先进的临床诊断提供了有希望的潜力。
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来源期刊
International Journal of Imaging Systems and Technology
International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术
CiteScore
6.90
自引率
6.10%
发文量
138
审稿时长
3 months
期刊介绍: The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.
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