Xu Yihong, Farris Chad W, Anderson Stephan W, Zhang Xin, Brown Keith A
Department of Physics, Boston University, Boston, MA, 02215, USA.
Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA.
Sci Rep. 2023 Aug 2;13(1):12527. doi: 10.1038/s41598-023-39533-4.
A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations such as parallel imaging, compressed sensing, and deep learning-based reconstruction. Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnetic resonance (MR) images in k-space and use these libraries to identify optimal subsampling paths and reconstruction processes. Specifically, we compute a multivariate normal distribution based upon Gaussian processes using a publicly available library of T1-weighted images of healthy brains. We combine this library with physics-informed envelope functions to only retain meaningful correlations in k-space. This covariance function is then used to select a series of ring-shaped subsampling paths using Bayesian optimization such that they optimally explore space while remaining practically realizable in commercial MRI systems. Combining optimized subsampling paths found for a range of images, we compute a generalized sampling path that, when used for novel images, produces superlative structural similarity and error in comparison to previously reported reconstruction processes (i.e. 96.3% structural similarity and < 0.003 normalized mean squared error from sampling only 12.5% of the k-space data). Finally, we use this reconstruction process on pathological data without retraining to show that reconstructed images are clinically useful for stroke identification. Since the model trained on images of healthy brains could be directly used for predictions in pathological brains without retraining, it shows the inherent transferability of this approach and opens doors to its widespread use.
现代磁共振成像（MRI）的一个核心目标是减少生成高质量图像所需的时间。相关努力包括硬件和软件创新，如并行成像、压缩感知和基于深度学习的重建。在此，我们提出并展示一种贝叶斯方法，用于在k空间构建磁共振（MR）图像的统计库，并使用这些库来识别最优的子采样路径和重建过程。具体而言，我们使用一个公开可用的健康大脑T1加权图像库，基于高斯过程计算多元正态分布。我们将这个库与物理信息包络函数相结合，以仅保留k空间中有意义的相关性。然后，使用这个协方差函数通过贝叶斯优化来选择一系列环形子采样路径，以便它们能在最优地探索空间的同时，在商业MRI系统中仍切实可行。结合为一系列图像找到的优化子采样路径，我们计算出一条通用采样路径，当将其用于新图像时，与先前报道的重建过程相比，能产生卓越的结构相似性和误差（即结构相似性为96.3%，仅对k空间数据的12.5%进行采样时归一化均方误差<0.003）。最后，我们在病理数据上使用这种重建过程而无需重新训练，以表明重建图像在临床上对中风识别有用。由于在健康大脑图像上训练的模型无需重新训练即可直接用于病理大脑的预测，这表明了该方法固有的可转移性，并为其广泛应用打开了大门。
