物理信息驱动的定量低场MRI图像重建去噪方法

中文摘要 AI 辅助

低场磁共振成像（MRI）在医学成像中正变得越来越重要，因为它能在确保高诊断输出的同时降低医疗成本。然而，低场MRI中的定量成像面临诸多挑战，如低信噪比和长扫描时间。为克服这些挑战，已有研究提出基于深度学习的方法用于图像重建。尽管如此，深度学习通常需要大量高质量的训练数据集，而这些数据在低场应用中往往不可得。在此，我们提出一种模块化的展开式端到端深度学习方法，用于直接从低场MRI的k空间数据中重建去噪的定量参数图。该方法由三个子网络迭代应用组成，分别用于定量参数估计的正则化，以及基于模拟信号曲线的信号估计。该方法具有良好的泛化能力，可应用于不同场强甚至不同的定量MR序列，而无需新的训练数据。我们将所提方法应用于在0.55 T下采集的膝关节噪声数据，用于重建T2图，并将其与其他经典和深度学习方法进行了比较。我们还将所提方法应用于72 mT下膝关节的T1映射和0.6 T下大脑的T2映射。所提方法优于其他重建方法，与真实T2图的中位差异低于4毫秒。尽管网络是在0.55 T下采集的T2图上训练的，但它成功地对不同场强、序列和解剖部位采集的数据进行了去噪。因此，所提出的网络及其底层方法为低场MR数据去噪提供了一种高效灵活的解决方案，并使定量低场MRI成为临床应用中可行的诊断工具。

英文摘要

Low-field magnetic resonance imaging (MRI) is becoming increasingly important for medical imaging because it can reduce healthcare costs while ensuring high diagnostic output. Nevertheless, quantitative imaging in low-field MRI faces challenges, such as low signal-to-noise ratio and long scan durations. Deep learning approaches have been proposed for image reconstruction to overcome these challenges. Still, deep learning often requires large high-quality training datasets which are usually not available for low-field applications. Here we propose a modular unrolled end-to-end deep learning method for the denoised reconstruction of quantitative parameter maps directly from k-space data for low-field MRI. It consists of three sub-networks that are iteratively applied. They are used for the regularization of the quantitative parameter estimation, as well as for the signal estimation that is based on simulated signal curves. It generalises well and can be applied to different field strengths and even different quantitative MR sequences without the need for new training data. We applied the presented method to noisy data of knees acquired at 0.55 T for the reconstruction of $T_2$-maps and compared it to other classical and deep learning methods. We also applied the proposed approach to $T_1$-mapping of knees at 72 mT and $T_2$-mapping of brains at 0.6 T. The presented approach outperforms the other reconstruction methods with a median difference below 4 ms to the ground truth $T_2$-map. Even though the network was trained with $T_2$-maps acquired at 0.55 T, it successfully denoised data acquired at different field strengths, sequences, and of different anatomies. As a result, the proposed network and its underlying method offer an efficient and flexible solution to denoise low-field MR data and make quantitative low-field MRI a feasible diagnostic tool for clinical applications.

发表机构

• Pontificia Universidad Católica de Chile（智利天主教宗座大学）
• Physikalisch-Technische Bundesanstalt（德国联邦物理技术研究院）

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