Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Johns Hopkins Kavli Neuroscience Discovery Institute, Baltimore, MD 21218, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Med Image Anal. 2023 Jul;87:102829. doi: 10.1016/j.media.2023.102829. Epub 2023 Apr 26.
Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging technique that characterizes the anisotropic tissue magnetic susceptibility with a second-order tensor model. STI has the potential to provide information for both the reconstruction of white matter fiber pathways and detection of myelin changes in the brain at mm resolution or less, which would be of great value for understanding brain structure and function in healthy and diseased brain. However, the application of STI in vivo has been hindered by its cumbersome and time-consuming acquisition requirement of measuring susceptibility induced MR phase changes at multiple head orientations. Usually, sampling at more than six orientations is required to obtain sufficient information for the ill-posed STI dipole inversion. This complexity is enhanced by the limitation in head rotation angles due to physical constraints of the head coil. As a result, STI has not yet been widely applied in human studies in vivo. In this work, we tackle these issues by proposing an image reconstruction algorithm for STI that leverages data-driven priors. Our method, called DeepSTI, learns the data prior implicitly via a deep neural network that approximates the proximal operator of a regularizer function for STI. The dipole inversion problem is then solved iteratively using the learned proximal network. Experimental results using both simulation and in vivo human data demonstrate great improvement over state-of-the-art algorithms in terms of the reconstructed tensor image, principal eigenvector maps and tractography results, while allowing for tensor reconstruction with MR phase measured at much less than six different orientations. Notably, promising reconstruction results are achieved by our method from only one orientation in human in vivo, and we demonstrate a potential application of this technique for estimating lesion susceptibility anisotropy in patients with multiple sclerosis.
磁化率张量成像（STI）是一种新兴的磁共振成像技术，它采用二阶张量模型来描述各向异性组织的磁化率。STI 有可能提供有关白质纤维路径重建和大脑髓鞘变化检测的信息，其空间分辨率可达毫米或更小，这对于了解健康和患病大脑的结构和功能将具有重要意义。然而，由于需要在多个头部方向测量磁化率引起的磁共振相位变化，STI 的应用受到其繁琐和耗时的采集要求的限制。通常，需要采样超过六个方向才能为病态 STI 偶极子反演提供足够的信息。由于头部线圈的物理限制，头部旋转角度的限制增加了这种复杂性。因此，STI 尚未在人体研究中得到广泛应用。在这项工作中，我们通过提出一种利用数据驱动先验的 STI 图像重建算法来解决这些问题。我们的方法称为 DeepSTI，通过一个深度神经网络隐式地学习数据先验，该神经网络近似 STI 正则化函数的近端算子。然后使用学习到的近端网络迭代求解偶极子反演问题。使用模拟和体内人类数据的实验结果表明，与最先进的算法相比，我们的方法在重建张量图像、主特征向量图和轨迹重建结果方面有了很大的改进，同时允许仅在少于六个不同方向测量的磁共振相位进行张量重建。值得注意的是，我们的方法仅在一个方向上就能从体内人类数据中获得有希望的重建结果，并展示了该技术在估计多发性硬化症患者病变磁化率各向异性方面的潜在应用。
