Love D Levi, Gryk Michael R, Schuyler Adam D
Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030, USA.
J Magn Reson Open. 2025 Jun;23. doi: 10.1016/j.jmro.2025.100187. Epub 2025 Jan 27.
In pursuit of an adaptive approach to nonuniform sampling (), two critical determinants arise: (1) the ability to determine an endpoint by way of quantitatively assessing spectral quality and (2) the ability to systematically determine what additional FIDs to collect if the aforementioned stop criteria is not met. As previously established, receiver operator characteristic (, (Zambrello et al., 2017)) assesses the recovery of injected synthetic ground truth signals to define spectral quality. The Nonuniform Sampling Contest (, (Pustovalova et al., 2021)), defines a workflow for processing NUS experiments and quantitatively evaluating spectral quality. We augmented that workflow by including an IROC module, which we believe to be an effective component of defining stop criteria for adaptive FID collection. As for the decision of what additional FIDs, this study builds off the work of prior studies on the influence the seed used to generate a nonuniform sample schedule has on the quality of a NUS reconstruction (Hyberts et al., 2011), i.e., whether a sampling method yields "high-variance" or "low-variance" schedules (Zambrello et al., 2020). Namely, existing work has been focused on reducing seed-dependence (Eddy et al., 2012; Mobli, 2015; Worley, 2016) or "optimizing" the seed (Hyberts and Wagner, 2022) by evaluating sample schedules using a computationally inexpensive metric based on the characterization of the point-spread function, like sidelobe-to-peak ratio (Lustig et al., 2007) and peak-to-sidelobe ratio (, (Eddy et al., 2012; Mobli, 2015; Worley, 2016; Craft et al., 2018)). This study assesses the ability of PSR, an metric that is based solely on the nonuniform sample schedule, to predict spectral quality as assessed by IROC. This work uses IROC to show that seed optimization via PSR does not result in better quality spectra. In addition, the trends observed in the spectral quality reported by IROC informs our future designs for adaptive FID selection.
为了寻求一种适用于非均匀采样的方法，出现了两个关键决定因素：（1）通过定量评估频谱质量来确定终点的能力；（2）如果未满足上述停止标准，系统地确定要采集哪些额外自由感应衰减信号（FID）的能力。如先前所述，接收者操作特征（ROC，（赞布雷洛等人，2017年））评估注入的合成真实信号的恢复情况以定义频谱质量。非均匀采样竞赛（NUS Contest，（普斯托瓦洛娃等人，2021年））定义了处理NUS实验和定量评估频谱质量的工作流程。我们通过纳入一个IROC模块对该工作流程进行了扩充，我们认为该模块是定义自适应FID采集停止标准的有效组成部分。至于要采集哪些额外的FID，本研究基于先前关于用于生成非均匀采样时间表的种子对NUS重建质量的影响的研究工作（海贝茨等人，2011年），即一种采样方法是否产生“高方差”或“低方差”时间表（赞布雷洛等人，2020年）。具体而言，现有工作一直专注于通过使用基于点扩散函数特征的计算成本较低的指标（如旁瓣与峰值比（卢斯蒂格等人，2007年）和峰值与旁瓣比（（埃迪等人，2012年；莫布利，2015年；沃利，2016年；克拉夫特等人，2018年））来评估采样时间表，从而降低对种子的依赖性（埃迪等人，2012年；莫布利，2015年；沃利，2016年）或“优化”种子（海贝茨和瓦格纳，2022年）。本研究评估了仅基于非均匀采样时间表的PSR指标预测由IROC评估的频谱质量的能力。这项工作使用IROC表明，通过PSR进行种子优化并不会产生质量更好的频谱。此外，IROC报告的频谱质量中观察到的趋势为我们未来自适应FID选择的设计提供了参考。
