UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California, USA.
Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Magn Reson Med. 2024 Oct;92(4):1698-1713. doi: 10.1002/mrm.30142. Epub 2024 May 22.
Metabolite-specific balanced SSFP (MS-bSSFP) sequences are increasingly used in hyperpolarized [1-C]Pyruvate (HP C) MRI studies as they improve SNR by refocusing the magnetization each TR. Currently, pharmacokinetic models used to fit conversion rate constants, k and k, and rate constant maps do not account for differences in the signal evolution of MS-bSSFP acquisitions.
In this work, a flexible MS-bSSFP model was built that can be used to fit conversion rate constants for these experiments. The model was validated in vivo using paired animal (healthy rat kidneys n = 8, transgenic adenocarcinoma of the mouse prostate n = 3) and human renal cell carcinoma (n = 3) datasets. Gradient echo (GRE) acquisitions were used with a previous GRE model to compare to the results of the proposed GRE-bSSFP model.
Within simulations, the proposed GRE-bSSFP model fits the simulated data well, whereas a GRE model shows bias because of model mismatch. For the in vivo datasets, the estimated conversion rate constants using the proposed GRE-bSSFP model are consistent with a previous GRE model. Jointly fitting the lactate T with k resulted in less precise k estimates.
The proposed GRE-bSSFP model provides a method to estimate conversion rate constants, k and k, for MS-bSSFP HP C experiments. This model may also be modified and used for other applications, for example, estimating rate constants with other hyperpolarized reagents or multi-echo bSSFP.
代谢物特异性平衡稳态自由进动(MS-bSSFP)序列在高极化 [1-C]丙酮酸(HP C)MRI 研究中越来越多地被使用,因为它们通过在每个 TR 重新聚焦磁化来提高 SNR。目前,用于拟合转化率常数 k 和 k 以及速率常数图的药代动力学模型并未考虑 MS-bSSFP 采集信号演化的差异。
在这项工作中,构建了一个灵活的 MS-bSSFP 模型,可用于拟合这些实验的转化率常数。该模型在体内通过配对动物(健康大鼠肾脏 n=8,转基因小鼠前列腺腺癌 n=3)和人类肾细胞癌(n=3)数据集进行了验证。使用先前的 GRE 模型进行梯度回波(GRE)采集,并与所提出的 GRE-bSSFP 模型的结果进行比较。
在模拟中,所提出的 GRE-bSSFP 模型很好地拟合了模拟数据,而 GRE 模型由于模型不匹配而存在偏差。对于体内数据集,使用所提出的 GRE-bSSFP 模型估计的转化率常数与先前的 GRE 模型一致。联合拟合乳酸 T 和 k 导致 k 估计不太精确。
所提出的 GRE-bSSFP 模型为 MS-bSSFP HP C 实验提供了一种估计转化率常数 k 和 k 的方法。该模型还可以修改并用于其他应用,例如,使用其他极化试剂或多回波 bSSFP 估计速率常数。