Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.
Magn Reson Med. 2022 Apr;87(4):1673-1687. doi: 10.1002/mrm.29081. Epub 2021 Nov 14.
The goal of this study was to combine a specialized acquisition method with a new quantification pipeline to accurately and efficiently probe the metabolism of hyperpolarized C-labeled compounds in vivo. In this study, we tested our approach on [2- C]pyruvate and [1- C]α-ketoglutarate data in rat orthotopic brain tumor models at 3T.
We used a multiband metabolite-specific radiofrequency (RF) excitation in combination with a variable flip angle scheme to minimize substrate polarization loss and measure fast metabolic processes. We then applied spectral-temporal denoising using singular value decomposition to enhance spectral quality. This was combined with LCModel-based automatic C spectral fitting and flip angle correction to separate overlapping signals and rapidly quantify the different metabolites.
Denoising improved the metabolite signal-to-noise ratio (SNR) by approximately 5. It also improved the accuracy of metabolite quantification as evidenced by a significant reduction of the Cramer Rao lower bounds. Furthermore, the use of the automated and user-independent LCModel-based quantification approach could be performed rapidly, with the kinetic quantification of eight metabolite peaks in a 12-spectrum array achieved in less than 1 minute.
The specialized acquisition method combined with denoising and a new quantification pipeline using LCModel for the first time for hyperpolarized C data enhanced our ability to monitor the metabolism of [2- C]pyruvate and [1- C]α-ketoglutarate in rat orthotopic brain tumor models in vivo. This approach could be broadly applicable to other hyperpolarized agents both preclinically and in the clinical setting.
本研究的目的是结合专门的采集方法和新的定量分析流水线,准确、高效地探测体内 ¹³C 标记化合物的代谢情况。在这项研究中,我们在 3T 下的大鼠原位脑肿瘤模型中测试了我们的方法对 [2-¹³C]丙酮酸和 [1-¹³C]α-酮戊二酸数据的应用。
我们使用多频带代谢物特异性射频(RF)激发技术,结合可变翻转角方案,以最小化底物极化损耗并测量快速代谢过程。然后,我们使用奇异值分解进行谱时去噪,以增强谱质量。这与基于 LCModel 的自动 ¹³C 谱拟合和翻转角校正相结合,用于分离重叠信号并快速定量不同代谢物。
去噪使代谢物的信噪比(SNR)提高了约 5 倍。它还通过显著降低克拉默-劳尔下限来提高代谢物定量的准确性。此外,使用自动化和用户独立的基于 LCModel 的定量方法可以快速进行,在不到 1 分钟的时间内完成 12 个谱数组中 8 个代谢物峰的动力学定量。
专门的采集方法结合去噪以及使用 LCModel 的新定量分析流水线,首次用于 ¹³C 极化数据,增强了我们在体内监测大鼠原位脑肿瘤模型中 [2-¹³C]丙酮酸和 [1-¹³C]α-酮戊二酸代谢的能力。这种方法可以广泛应用于临床前和临床环境中的其他 ¹³C 极化试剂。