School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
Magn Reson Med. 2023 Nov;90(5):2089-2101. doi: 10.1002/mrm.29764. Epub 2023 Jun 22.
To develop a machine learning-based method for estimation of both transmitter and receiver B fields desired for correction of the B inhomogeneity effects in quantitative brain imaging.
A subspace model-based machine learning method was proposed for estimation of B and B fields. Probabilistic subspace models were used to capture scan-dependent variations in the B fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B inhomogeneity correction in quantitative brain imaging scenarios, including T and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data.
In both phantom and healthy subject data, the proposed method produced high-quality B maps. B correction on SPGR data using the estimated B maps produced significantly improved T and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B estimation and correction results than conventional methods. The B maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues.
This work presents a novel method to estimate B variations using probabilistic subspace models and machine learning. The proposed method may make correction of B inhomogeneity effects more robust in practical applications.
开发一种基于机器学习的方法，用于估计发射器和接收器 B 场，以校正定量脑成像中的 B 不均匀性效应。
提出了一种基于子空间模型的机器学习方法，用于估计 B 和 B 场。概率子空间模型用于捕获 B 场中的扫描相关变化；子空间基和系数分布是从预扫描的训练数据中学习到的。通过求解具有先验分布约束的线性优化问题，实现对新实验数据的 B 场估计。我们使用体模、健康受试者和脑肿瘤患者的数据，评估了该方法在定量脑成像场景中的 B 不均匀性校正性能，包括来自可变翻转角扰相梯度回波 (SPGR) 数据的 T 和质子密度 (PD) 映射，以及来自 MRSI 数据的神经代谢映射。
在体模和健康受试者数据中，所提出的方法产生了高质量的 B 图。使用估计的 B 图校正 SPGR 数据中的 B 可以显著改善 T 和 PD 图。在脑肿瘤患者中，该方法产生的 B 估计和校正结果比传统方法更准确和稳健。B 图还应用于肿瘤患者的 MRSI 数据，并产生了改进的神经代谢物图，更好地分离了病理组织和正常组织。
这项工作提出了一种使用概率子空间模型和机器学习来估计 B 变化的新方法。该方法可能使 B 不均匀性效应的校正在实际应用中更稳健。
