A novel method is developed that adaptively generates tiny dictionaries for joint T1-T2 mapping in magnetic resonance imaging. This work breaks the bond between dictionary size and representation accuracy (i) by approximating the Bloch-response manifold by piece-wise linear functions and (ii) by adaptively refining the sampling grid depending on the locally-linear approximation error. Data acquisition is accomplished with use of an 2D radially sampled Inversion-Recovery Hybrid-State Free Precession sequence. Adaptive dictionaries are generated with different error tolerances and compared to a heuristically designed dictionary. Based on simulation results, tiny dictionaries were used for T1-T2 mapping in phantom and in vivo studies. Reconstruction and parameter mapping were performed entirely in subspace. All experiments demonstrated excellent agreement between the proposed mapping technique and template matching using heuristic dictionaries. Adaptive dictionaries in combination with manifold projection allow to reduce the necessary dictionary sizes by one to two orders of magnitude.

中文翻译：

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带有小词典和子空间约束的重构的联合T1和T2映射。

开发了一种新颖的方法，该方法可以自适应地生成微小字典以用于磁共振成像中的联合T1-T2映射。这项工作打破了字典大小和表示精度之间的纽带（i）通过分段线性函数逼近Bloch响应流形，并且（ii）根据局部线性逼近误差自适应地精炼采样网格。数据采集​​是使用2D径向采样的反转恢复混合状态自由进动序列完成的。自适应词典的生成具有不同的容错能力，并将其与启发式设计的字典进行比较。根据模拟结果，微型词典用于幻影和体内研究中的T1-T2定位。重构和参数映射完全在子空间中执行。所有实验都证明了拟议的映射技术和使用启发式字典的模板匹配之间的极佳一致性。自适应词典与流形投影相结合，可以将所需的词典大小减少一到两个数量级。