利用深度学习优化7T MRI中并行射频发射设计的发射场不均匀性
Optimizing Transmit Field Inhomogeneity of Parallel RF Transmit Design in 7T MRI using Deep Learning
AI总结：
本文提出一种基于残差网络的两步深度学习策略，从多通道B1+场直接预测RF匀场权重，以替代传统MLS优化，从而在7T MRI中实现更快、更准确的发射场均匀性校正并提升成像质量。
摘要：
超高场（UHF）磁共振成像（MRI）可提供更高的信噪比，从而获得更高的空间分辨率。然而，UHF MRI也带来了诸如发射射频（RF）场（B1+）不均匀性等挑战，导致翻转角不均匀和图像强度异常。这些问题会显著降低成像质量及其医学应用价值。本研究通过一种新颖的基于深度学习的策略来解决B1+场均匀性问题。传统方法如最小二乘幅度（MLS）优化虽然有效，但耗时且依赖于患者在场。最近的机器学习方法，例如通过迭代投影岭回归进行RF匀场预测以及深度学习框架，已显示出潜力，但仍面临训练时间长和架构过于简化等局限。我们提出了一种两步深度学习策略。首先，我们使用随机初始化的自适应矩估计从多通道B1+场中获得期望的参考RF匀场权重。然后，我们采用残差网络（ResNets）训练一个模型，将B1+场映射到目标RF匀场输出。我们的方法在测试过程中不依赖于预先计算的参考优化结果，并能高效地学习残差函数。与传统MLS优化的对比研究表明，我们的方法在速度和精度方面具有优势。所提出的策略实现了更快、更高效的RF匀场设计，显著提高了UHF下的成像质量。这一进展有望在医学成像和诊断中获得更广泛的应用。
英文摘要：
Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a higher signal-to-noise ratio and, thereby, higher spatial resolution. However, UHF MRI introduces challenges such as transmit radiofrequency (RF) field (B1+) inhomogeneities, leading to uneven flip angles and image intensity anomalies. These issues can significantly degrade imaging quality and its medical applications. This study addresses B1+ field homogeneity through a novel deep learning-based strategy. Traditional methods like Magnitude Least Squares (MLS) optimization have been effective but are time-consuming and dependent on the patient's presence. Recent machine learning approaches, such as RF Shim Prediction by Iteratively Projected Ridge Regression and deep learning frameworks, have shown promise but face limitations like extensive training times and oversimplified architectures. We propose a two-step deep learning strategy. First, we obtain the desired reference RF shimming weights from multi-channel B1+ fields using random-initialized Adaptive Moment Estimation. Then, we employ Residual Networks (ResNets) to train a model that maps B1+ fields to target RF shimming outputs. Our approach does not rely on pre-calculated reference optimizations for the testing process and efficiently learns residual functions. Comparative studies with traditional MLS optimization demonstrate our method's advantages in terms of speed and accuracy. The proposed strategy achieves a faster and more efficient RF shimming design, significantly improving imaging quality at UHF. This advancement holds potential for broader applications in medical imaging and diagnostics.
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