Wang Wenchen, Wang Yaohui, Wei Riyu, Weber Ewald, Liu Feng
School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Queensland, Australia.
Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China.
NMR Biomed. 2025 Aug;38(8):e70085. doi: 10.1002/nbm.70085.
In a magnetic resonance imaging (MRI) system, it is highly desirable to achieve superior homogeneity of the static magnetic field (B0) within the imaging region for high-performance imaging. Passive shimming (PS) is a widely used technique that employs ferromagnetic materials to reduce the B0 deviations. However, the PS installation process often involves multiple manual iterations due to system or human errors, causing efficiency and accuracy difficulties. Existing PS solutions usually use L1-norm-based optimization algorithms to minimize the total iron consumption and constrain the peak-to-peak field inhomogeneity within the desired range. This design scheme can lead to substantial modifications to shim pockets in each iteration, making the process time-consuming and prone to induce installation errors and even causing operation failures. This paper proposes a novel approach that utilizes the L0-norm to redesign the PS optimization model. Instead of focusing on reducing the total thickness of iron pieces, as done in conventional PS optimizations, the new design allows the explicit adjustment of the number of shim pockets, enhancing shimming efficiency and reducing manual errors in the installation process. To validate the effectiveness of the proposed method, we conducted several tests on shimming a 3-T superconducting magnet. The results demonstrate that our new scheme consistently generates sparse solutions that improve efficiency and accuracy compared to conventional solutions. Thus, our method offers a favorable solution to streamline the shimming process, addressing the long-standing technical restrictions of the standard LP model. This new PS is expected to achieve high B0 uniformity for various MRI applications.
在磁共振成像(MRI)系统中,为实现高性能成像,非常希望在成像区域内获得卓越的静磁场(B0)均匀性。被动匀场(PS)是一种广泛使用的技术,它采用铁磁材料来减少B0偏差。然而,由于系统或人为误差,PS安装过程通常需要多次手动迭代,导致效率和准确性方面存在困难。现有的PS解决方案通常使用基于L1范数的优化算法来最小化总铁消耗量,并将峰峰值场不均匀性限制在所需范围内。这种设计方案可能会导致每次迭代中对匀场腔进行大量修改,使过程耗时且容易引发安装错误,甚至导致操作失败。本文提出了一种新颖的方法,利用L0范数重新设计PS优化模型。与传统的PS优化不同,新设计不是专注于减少铁片的总厚度,而是允许明确调整匀场腔的数量,提高匀场效率并减少安装过程中的人为误差。为验证所提方法的有效性,我们对一个3-T超导磁体进行了多次匀场测试。结果表明,与传统解决方案相比,我们的新方案始终能生成稀疏解,提高了效率和准确性。因此,我们的方法为简化匀场过程提供了一个良好的解决方案,解决了标准LP模型长期存在的技术限制。这种新型PS有望在各种MRI应用中实现高B0均匀性。