Introduction

The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the receiver coil sensitivity maps. Deep learning-based receiver coil sensitivity map estimation depends upon the size of training dataset and generalization capabilities of the trained neural network. When there is a mismatch between the training and testing datasets, retraining of the neural networks is required from a scratch which is costly and time consuming.

Materials and methods

A transfer learning approach, i.e., end-to-end fine-tuning is proposed to address the data scarcity and generalization problems of deep learning-based receiver coil sensitivity map estimation. First, generalization capabilities of a pre-trained U-Net (initially trained on 1.5T receiver coil sensitivity maps) are thoroughly assessed for 3T receiver coil sensitivity map estimation. Later, end-to-end fine-tuning is performed on the pre-trained U-Net to estimate the 3T receiver coil sensitivity maps.

Result and conclusion

Peak Signal-to-Noise Ratio, Root Mean Square Error and central line profiles (of the SENSE reconstructed images) show a successful SENSE reconstruction by utilizing the receiver coil sensitivity maps estimated by the proposed method.

中文翻译：

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基于深度神经网络的接收器线圈灵敏度图估计中的迁移学习

介绍

诸如灵敏度编码 (SENSE) 之类的并行磁共振成像算法的成功取决于对接收器线圈灵敏度图的准确估计。基于深度学习的接收器线圈灵敏度图估计取决于训练数据集的大小和训练神经网络的泛化能力。当训练和测试数据集不匹配时，需要从头开始重新训练神经网络，这既昂贵又耗时。

材料和方法

提出了一种迁移学习方法，即端到端的微调，以解决基于深度学习的接收器线圈灵敏度图估计的数据稀缺性和泛化问题。首先，针对 3T 接收器线圈灵敏度图估计，彻底评估了预训练 U-Net（最初在 1.5T 接收器线圈灵敏度图上训练）的泛化能力。之后，在预训练的 U-Net 上进行端到端的微调，以估计 3T 接收器线圈灵敏度图。

结果与结论

峰值信噪比、均方根误差和（SENSE 重建图像的）中心线轮廓通过利用所提出的方法估计的接收器线圈灵敏度图显示成功的 SENSE 重建。