Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois.
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, Illinois.
Magn Reson Med. 2020 Feb;83(2):377-390. doi: 10.1002/mrm.27980. Epub 2019 Sep 4.
To develop a subspace learning method for the recently proposed subspace-based MRSI approach known as SPICE, and achieve ultrafast H-MRSI of the brain.
A novel strategy is formulated to learn a low-dimensional subspace representation of MR spectra from specially acquired training data and use the learned subspace for general MRSI experiments. Specifically, the subspace learning problem is formulated as learning "empirical" distributions of molecule-specific spectral parameters (e.g., concentrations, lineshapes, and frequency shifts) by integrating physics-based model and the training data. The learned spectral parameters and quantum mechanical simulation basis can then be combined to construct acquisition-specific subspace for spatiospectral encoding and processing. High-resolution MRSI acquisitions combining ultrashort-TE/short-TR excitation, sparse sampling, and the elimination of water suppression have been performed to evaluate the feasibility of the proposed method.
The accuracy of the learned subspace and the capability of the proposed method in producing high-resolution 3D H metabolite maps and high-quality spatially resolved spectra (with a nominal resolution of ∼2.4 × 2.4 × 3 mm in 5 minutes) were demonstrated using phantom and in vivo studies. By eliminating water suppression, we are also able to extract valuable information from the water signals for data processing ( map, frequency drift, and coil sensitivity) as well as for mapping tissue susceptibility and relaxation parameters.
The proposed method enables ultrafast H-MRSI of the brain using a learned subspace, eliminating the need of acquiring subject-dependent navigator data (known as ) in the original SPICE technique. It represents a new way to perform MRSI experiments and an important step toward practical applications of high-resolution MRSI.
为最近提出的基于子空间的磁共振波谱成像方法 SPICE 开发一种子空间学习方法，并实现大脑的超快速 H-MRSI。
提出了一种新策略，用于从专门采集的训练数据中学习 MR 光谱的低维子空间表示，并将学习到的子空间用于一般的 MRSI 实验。具体来说，子空间学习问题被表述为通过整合基于物理的模型和训练数据来学习分子特异性光谱参数（例如浓度、线形状和频移）的“经验”分布。然后，可以将学习到的光谱参数和量子力学模拟基结合起来，为空间光谱编码和处理构建特定于采集的子空间。高分辨率 MRSI 采集结合了超短 TE/短 TR 激发、稀疏采样和消除水抑制，以评估所提出方法的可行性。
使用体模和体内研究证明了所提出的方法的学习子空间的准确性以及产生高分辨率 3D H 代谢物图和高质量空间分辨光谱（在 5 分钟内具有约 2.4×2.4×3mm 的标称分辨率）的能力。通过消除水抑制，我们还能够从水信号中提取有价值的信息用于数据处理（谱图、频率漂移和线圈灵敏度）以及映射组织磁化率和弛豫参数。
所提出的方法通过使用学习到的子空间实现了大脑的超快速 H-MRSI，无需在原始 SPICE 技术中获取依赖于受试者的导航数据（称为 ）。它代表了执行 MRSI 实验的一种新方法，也是实现高分辨率 MRSI 实际应用的重要一步。
