磁化转移
磁化
布洛赫方程
物理
放松(心理学)
统计物理学
编码(内存)
算法
核磁共振
计算机科学
人工智能
磁共振成像
磁场
量子力学
医学
心理学
社会心理学
放射科
作者
Jakob Assländer,Cem Gultekin,Andrew Mao,Xiaoxia Zhang,Quentin Duchemin,Kangning Liu*,Robert Charlson,Timothy M. Shepherd,Carlos Fernandez‐Granda,Sebastian Flassbeck
摘要
Abstract Purpose To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters. Theory and Methods We combine two recently proposed models in a Bloch‐McConnell equation: the dynamics of the free spin pool are confined to the hybrid state, and the dynamics of the semi‐solid spin pool are described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three qMT parameters while accounting for all eight parameters of the two‐pool model. We sparsely sample each time frame along this spin dynamics with a three‐dimensional radial koosh‐ball trajectory, reconstruct the data with subspace modeling, and fit the qMT model with a neural network for computational efficiency. Results We extracted qMT parameter maps of the whole brain with an effective resolution of 1.24 mm from a 12.6‐min scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi‐solid spin pool and longer relaxation times, consistent with previous reports. Conclusion The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for efficient quantitative magnetization transfer imaging with few constraints on model parameters.
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