Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China.
Sci Adv. 2023 Sep 22;9(38):eadi9327. doi: 10.1126/sciadv.adi9327.
In recent years, there has been an intensive development of portable ultralow-field magnetic resonance imaging (MRI) for low-cost, shielding-free, and point-of-care applications. However, its quality is poor and scan time is long. We propose a fast acquisition and deep learning reconstruction framework to accelerate brain MRI at 0.055 tesla. The acquisition consists of a single average three-dimensional (3D) encoding with 2D partial Fourier sampling, reducing the scan time of T1- and T2-weighted imaging protocols to 2.5 and 3.2 minutes, respectively. The 3D deep learning leverages the homogeneous brain anatomy available in high-field human brain data to enhance image quality, reduce artifacts and noise, and improve spatial resolution to synthetic 1.5-mm isotropic resolution. Our method successfully overcomes low-signal barrier, reconstructing fine anatomical structures that are reproducible within subjects and consistent across two protocols. It enables fast and quality whole-brain MRI at 0.055 tesla, with potential for widespread biomedical applications.
近年来，便携式超低场磁共振成像（MRI）得到了深入发展，可用于低成本、无屏蔽、现场应用。然而，其成像质量较差，扫描时间较长。我们提出了一种快速采集和深度学习重建框架，以加速 0.055 特斯拉场强下的脑部 MRI。采集包括单次平均三维（3D）编码和二维部分傅里叶采样，使 T1 加权和 T2 加权成像协议的扫描时间分别缩短至 2.5 分钟和 3.2 分钟。3D 深度学习利用高场人脑数据中均匀的大脑解剖结构来提高图像质量、减少伪影和噪声，并提高空间分辨率至合成的 1.5 毫米各向同性分辨率。我们的方法成功克服了低信号障碍，重建了精细的解剖结构，这些结构在受试者内具有可重复性，且在两个协议之间具有一致性。它能够以 0.055 特斯拉场强实现快速、高质量的全脑 MRI，具有广泛的生物医学应用潜力。
