Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA.
Magn Reson Med. 2024 Jul;92(1):43-56. doi: 10.1002/mrm.30032. Epub 2024 Feb 1.
To introduce universal modes by applying the universal pulse concept to time-interleaved acquisition of modes (TIAMO), thereby achieving calibration-free inhomogeneity mitigation for body imaging at ultra-high fields.
Two databases of different RF arrays were used to demonstrate the feasibility of universal modes. The first comprised 31 cardiac in vivo data sets acquired at 7T while the second consisted of 6 simulated 10.5T pelvic data sets. Subject-specific solutions and universal modes were computed and subsequently evaluated alongside predefined default modes. For the cardiac database, subdivision into subpopulations was investigated. The optimization was performed using least-squares (LS) TIAMO and acquisition modes optimized for refocused echoes (AMORE). Finally, universal modes based on simulated pelvis data were applied in vivo at 10.5T.
In all studied cases, the universal modes yield improvements over the predefined default modes of up to 51% (cardiac) and 30% (pelvic) in terms of median excitation error when using two modes. The subpopulation-specific cardiac solutions revealed a further improvement of universal modes at the expense of increased errors when applied outside the appropriate subpopulation. Direct application of simulation-based universal modes in vivo resulted in up to a 14% reduction in excitation error compared to default modes and up to a 34% reduction in peak 10 g local specific absorption rate (SAR) compared to subject-specific solutions.
Universal modes are feasible for calibration-free inhomogeneity mitigation at ultra-high fields. In addition, simulation-based solutions can be applied directly in vivo, eliminating the need for large in vivo databases.
通过将通用脉冲概念应用于模式的时间交错采集(TIAMO),引入通用模式,从而实现超高场身体成像的无校准不均匀性缓解。
使用两个不同射频阵列的数据库来演示通用模式的可行性。第一个数据库包含在 7T 下采集的 31 个心脏体内数据集,第二个数据库由 6 个模拟的 10.5T 盆腔数据集组成。计算了针对特定个体的解决方案和通用模式,并与预定义的默认模式一起进行评估。对于心脏数据库,研究了子群体的细分。使用最小二乘(LS)TIAMO 进行优化,并针对重聚焦回波优化采集模式(AMORE)。最后,在 10.5T 下将基于模拟骨盆数据的通用模式应用于体内。
在所有研究的情况下,与预定义的默认模式相比,通用模式在使用两种模式时,在中位激励误差方面的改进高达 51%(心脏)和 30%(盆腔)。特定于子群体的心脏解决方案显示,在适用于适当子群体之外时,通用模式的进一步改进是以增加误差为代价的。直接在体内应用基于模拟的通用模式,与默认模式相比,激励误差最多可降低 14%,与针对特定个体的解决方案相比,峰值 10g 局部特定吸收率(SAR)最多可降低 34%。
通用模式可用于超高场的无校准不均匀性缓解。此外,基于模拟的解决方案可以直接在体内应用,无需大型体内数据库。