Scholarlink

<p>MR data quality depends on static magnetic field (B<sub>0</sub>) homogeneity. At the beginning of each session, a brief field map quantifies subject-specific B<sub>0</sub> variation, and shim coils are then set to counteract it. Conventional spherical-harmonic (SPH) shims have limited shimming power, motivating localized multi-coil (AC/DC) systems. However, subject motion can perturb the optimized field, necessitating real-time shim updates that require rapid tracking of B<sub>0</sub> changes. We simulated real-time shimming under motion using jointly first-order SPH and a 31-channel AC/DC matrix coil. Measured B<sub>0</sub> data initially shimmed with SPH were augmented with AC/DC terms in simulation, and real-time control was evaluated. Shimming with AC/DC coils added to the SPH coils improved field homogeneity, but motion eroded these gains. With simulated real-time updates informed by deep learning, B<sub>0</sub> homogeneity was effectively maintained even during substantial motion. Performance matched simulated navigator-like real-time shimming with gradient-echo and echo-planar imaging, while adding no extra scan time in main imaging sequences. Multi-coil shimming offers clear benefits, but the gains may be reduced if shim terms are not updated in real time. Deep-learning-driven prediction of B<sub>0</sub> changes provides a practical path to sequence-agnostic, motion-robust shimming across a broad range of MR protocols.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Correcting motion-related B0 inhomogeneities in magnetic resonance imaging via combined spherical harmonics and AC/DC matrix coils using DL-based prediction–simulation study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

查看全文
  • Mohammad Khosravi,
  • Wolfgang Bogner,
  • Bernhard Strasser,
  • Jason Stockmann,
  • Christian Menard,
  • Georg Langs,
  • Günther Grabner,
  • Beata Bachrata,
  • Stanislav Motyka

摘要

MR data quality depends on static magnetic field (B0) homogeneity. At the beginning of each session, a brief field map quantifies subject-specific B0 variation, and shim coils are then set to counteract it. Conventional spherical-harmonic (SPH) shims have limited shimming power, motivating localized multi-coil (AC/DC) systems. However, subject motion can perturb the optimized field, necessitating real-time shim updates that require rapid tracking of B0 changes. We simulated real-time shimming under motion using jointly first-order SPH and a 31-channel AC/DC matrix coil. Measured B0 data initially shimmed with SPH were augmented with AC/DC terms in simulation, and real-time control was evaluated. Shimming with AC/DC coils added to the SPH coils improved field homogeneity, but motion eroded these gains. With simulated real-time updates informed by deep learning, B0 homogeneity was effectively maintained even during substantial motion. Performance matched simulated navigator-like real-time shimming with gradient-echo and echo-planar imaging, while adding no extra scan time in main imaging sequences. Multi-coil shimming offers clear benefits, but the gains may be reduced if shim terms are not updated in real time. Deep-learning-driven prediction of B0 changes provides a practical path to sequence-agnostic, motion-robust shimming across a broad range of MR protocols.