{"title":"Dual-Resonant RF Coil for Proton and Phosphorus Imaging at 7 Tesla MRI","authors":"Ashraf Abuelhaija, Gameel Saleh, Emad Awada, Sanaa Salama, Samer Issa, Osama Nashwan","doi":"10.1002/ima.70081","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Magnetic resonance spectroscopy (MRS) provides a non-invasive method for examining metabolic alterations associated with diseases. While <sup>1</sup>H-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 (<sup>1</sup>H) and detect Phosphorus (<sup>31</sup>P) 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 (S<sub>11</sub> < −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.</p>\n </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Imaging Systems and Technology","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ima.70081","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
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.
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.