磁共振成像谱仪作为磁共振成像系统的“中枢大脑”,其技术发展始终与磁共振整机性能提升、临床应用拓展和科研创新需求深度绑定。磁共振成像谱仪已从最初的时序控制与信号采集设备,发展为集实时控制、多通道信号处理、智能重建、安全监控和智能临床工作流管理于一体的复杂系统,对临床诊断和科研探索的支撑作用也愈发凸显。本文从系统架构和使用者的视角出发,剖析了谱仪各层级的核心功能与实际应用需求,并提出了更多物理通道、更紧密与人工智能融合、开源化和可裁剪的谱仪架构等几个谱仪未来发展的方向。
As the brain of the magnetic resonance imaging （MRI） system, the technological development of MRI spectrometer is always deeply bound to the improvement in the overall MRI performance, the expansion of clinical applications, and the demand for scientific research innovation. MRI spectrometers have evolved from an initial timing control and signal acquisition device into a complex system integrating real-time control, multi-channel signal processing, intelligent reconstruction, safety monitoring, and intelligent clinical workflow management, and their support role in clinical diagnosis and scientific research has become increasingly prominent. From the system architecture and users’ view, this paper analyzed the core functions and practical application requirements of spectrometers at each level, and proposed several future development directions for spectrometers, including more physical channels, closer integration with artificial intelligence, open-source design, and scalable spectrometer architectures.
[1] 吴林. 高性能磁共振谱仪关键技术研究与应用[D]. 成都:电子科技大学,2025.
[2] 胡霞飞. 基于PXIe架构数字谱仪关键技术的设计与实现[D]. 成都:电子科技大学,2021.
[3] 徐勤. 数字化磁共振成像谱仪[D]. 上海:华东师范大学,2006.
[4] 袁家辉. 基于FPGA与高速串行协议的磁共振光纤数据传输的实现[D]. 北京:北京化工大学, 2024.
[5] 余浩. 提升磁共振谱仪信噪比的关键技术研究[D]. 成都:电子科技大学, 2022.
[6] 宋春虎, 陈巧燕, 车韶, 等. 磁共振成像超柔射频接收线圈研究进展[J]. 生命科学仪器, 2022, 20(2):4-16.
[7] 肖遥,尧麒,揭平平,等. 超柔线圈臂丛神经MR扫描在1.5 T磁共振中的成像价值评估[J]. 影像技术, 2025,37(5):35-39.
[8] 王奥楠,许佳佳,李姝靖,等. 孕妇体位和线圈对舒适度和胎脑MR图像质量的影响[J]. 临床放射学杂志,2025,44(7): 1311-1318.
[9] Layton K, Kroboth S, Jia F, et al.Pulseq:a rapid and hardware-independent pulse sequence prototyping framework[J]. Magn Reson Med, 2017, 77(4): 1544-1552.
[10] Hansen M, Sorensen T.Gadgetron: an open source framework for medical image reconstruction[J]. Magn Reson Med, 2012, 69(6): 1768-1776.
[11] 周玉福. 19F肺部磁共振成像系统的研究[D]. 合肥:中国科学技术大学,2024.
[12] Wang S, Wu R, Jia S, et al.Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning[J]. Magn Reson Med, 2024, 92(2): 496-518.
[13] 朱洪伟. 小型床旁移动式头颈磁共振在神经外科的应用[D] 广州: 南方医科大学,2023.
[14] 万裁. 便携式磁共振土壤水分测量仪设计与实现[D]. 重庆:重庆大学,2021.
[15] 钱智勇. 面向小动物成像的便携式磁共振仪器的研制[D]. 南京:东南大学,2024.
