Lei Li, Qingyuan He, Shufeng Wei, Huixian Wang, Zheng Wang, Zhao Wei, Hongyan He, Ce Xiang, Wenhui Yang
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Further, deep learning models with different task objectives (super-resolution and denoising) are strategically stacked for multi-level post-processing to enable fast and high-quality low-field MRI. Finally, extensive phantom and brain experiments were conducted on a home-built 0.2 T mobile brain scanner for the evaluation of the proposed strategy.</p><p><strong>Results: </strong>20 healthy volunteers were recruited to participate in the experiment. The results show that the proposed strategy enables the 0.2 T scanner to generate images with sufficient anatomical information and diagnostic value under unshielded conditions using a single sensing coil. In particular, the EMI elimination outperforms the state-of-the-art deep learning methods and numerical computation methods. 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引用次数: 0
摘要
目的提出一种基于深度学习的低场移动磁共振成像策略,利用最少的硬件资源实现快速、高质量、无屏蔽成像:首先,我们分析了传感线圈和磁共振成像线圈之间的电磁干扰信号的相关性,初步验证了使用单传感线圈进行主动电磁干扰屏蔽的可行性。然后,提出了一个功能强大的深度学习 EMI 消除模型,该模型可以利用至少一个传感线圈的 EMI 信号准确预测 MRI 线圈信号中的 EMI 成分。此外,具有不同任务目标(超分辨率和去噪)的深度学习模型被策略性地堆叠起来进行多级后处理,以实现快速、高质量的低场磁共振成像。最后,在自制的 0.2 T 移动脑部扫描仪上进行了大量的模型和脑部实验,以评估所提出的策略。结果表明,所提出的策略能使 0.2 T 扫描仪在无屏蔽条件下使用单传感线圈生成具有足够解剖信息和诊断价值的图像。特别是,EMI 消除效果优于最先进的深度学习方法和数值计算方法。此外,2 × 超分辨率(DDSRNet)和去噪(SwinIR)技术还能进一步提高成像速度和质量:所提出的策略可使低场移动磁共振成像扫描仪在无屏蔽条件下使用最少的硬件资源实现快速、高质量成像,这对低场移动磁共振成像扫描仪的广泛部署具有重要意义。
Fast, high-quality, and unshielded 0.2 T low-field mobile MRI using minimal hardware resources.
Objective: To propose a deep learning-based low-field mobile MRI strategy for fast, high-quality, unshielded imaging using minimal hardware resources.
Methods: Firstly, we analyze the correlation of EMI signals between the sensing coil and the MRI coil to preliminarily verify the feasibility of active EMI shielding using a single sensing coil. Then, a powerful deep learning EMI elimination model is proposed, which can accurately predict the EMI components in the MRI coil signals using EMI signals from at least one sensing coil. Further, deep learning models with different task objectives (super-resolution and denoising) are strategically stacked for multi-level post-processing to enable fast and high-quality low-field MRI. Finally, extensive phantom and brain experiments were conducted on a home-built 0.2 T mobile brain scanner for the evaluation of the proposed strategy.
Results: 20 healthy volunteers were recruited to participate in the experiment. The results show that the proposed strategy enables the 0.2 T scanner to generate images with sufficient anatomical information and diagnostic value under unshielded conditions using a single sensing coil. In particular, the EMI elimination outperforms the state-of-the-art deep learning methods and numerical computation methods. In addition, 2 × super-resolution (DDSRNet) and denoising (SwinIR) techniques enable further improvements in imaging speed and quality.
Discussion: The proposed strategy enables low-field mobile MRI scanners to achieve fast, high-quality imaging under unshielded conditions using minimal hardware resources, which has great significance for the widespread deployment of low-field mobile MRI scanners.
期刊介绍:
MAGMA is a multidisciplinary international journal devoted to the publication of articles on all aspects of magnetic resonance techniques and their applications in medicine and biology. MAGMA currently publishes research papers, reviews, letters to the editor, and commentaries, six times a year. The subject areas covered by MAGMA include:
advances in materials, hardware and software in magnetic resonance technology,
new developments and results in research and practical applications of magnetic resonance imaging and spectroscopy related to biology and medicine,
study of animal models and intact cells using magnetic resonance,
reports of clinical trials on humans and clinical validation of magnetic resonance protocols.