非笛卡尔并行磁共振成像重建技术研究新进展
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中国科学院 苏州生物医学工程技术研究所医学影像室苏州215163

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R445.2TH77

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中科院科研装备研制项目(YZ201445)、国家自然科学基金(11505281)项目资助


New research advances in nonCartesian parallel MRI reconstruction
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Medical Imaging Division, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China

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    摘要:

    相对于传统的k空间笛卡尔采样,非笛卡尔采样能够使得k空间具有更高的覆盖效率,同时可以更有效地利用梯度系统性能,减少dB/dt值,避免引起人体不良的生理反应。k空间非笛卡尔采样和并行成像技术结合能够进一步提高成像速度,但是也使得图像域中的伪影模式更加复杂,因此非笛卡尔并行磁共振成像重建具有更高的技术难度。综述了目前几种典型的非笛卡尔并行成像重建技术,具体讨论了每种方法的技术细节和优缺点,包括敏感度编码(SENSE)、共轭梯度敏感度编码(CGSENSE)、非笛卡尔自标定并行采集方法(nonCartesian GRAPPA)、基于数据一致性的迭代方法(SPIRiT)和近年来发展迅速的压缩感知技术。SENSE和CGSENSE理论上可以获得最优的重建结果,但受制于线圈敏感度分布的准确测量;NonCartesian GRAPPA无需测量线圈敏感度,但只能对特定的非笛卡尔采样模式进行近似计算;SPIRiT结合了SENSE和GRAPPA的优点,通过迭代优化方法可以获得较满意的结果;压缩感知技术利用图像的稀疏变换特性,配合现有的迭代优化并行成像方法可以进一步提升重建图像质量,将继续成为未来研究的热点。

    Abstract:

    Compared with conventional Cartesian kspace sampling, the nonCartesian sampling can enable higher coverage effeciency of kspace, more efficiently make use of the gradient system performance, and reduce dB/dt to prevent to cause the undesirable human physiological reactions. The combination of nonCartesian kspace sampling and parallel imaging can further accelerate imaging speed, however the artifact pattern in image domain would become much more complicated, which introduces a lot of technical difficulties to nonCartesian parallel MRI reconstruction. In this article, several typical nonCartesian parallel imaging reconstruction techniques including SENSE, CGSENSE, nonCartesian GRAPPA, SPIRiT and newlyemerging compressed sensing are reviewed, their technical details, advantages and disadvantages are discussed. SENSE and CGSENSE can achieve optimal reconstruction results theoretically, but both of them are restricted by the accurate measurement of coil sensitivity distribution. NonCartesian GRAPPA doesn’t rely on coil sensitivity measurement, but can only perform approximate calculation for specified nonCartesian sampling mode. SPIRiT combines the advantages of SENSE and GRAPPA, and can obtain satisfactory result by using iterative optimization algorithm. Taking the advantage of sparse transform characteristic of images, compressed sensing cooperating with existing iterative optimization parallel imaging method can further improve reconstructed image quality, and it will be a hotspot in the future study.

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吴振洲,常严,徐雅洁,王慧,杨晓冬.非笛卡尔并行磁共振成像重建技术研究新进展[J].仪器仪表学报,2017,38(8):1996-2006

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  • 在线发布日期: 2017-09-04
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