IEEE Trans Med Imaging. 2020 Feb;39(2):320-327. doi: 10.1109/TMI.2019.2926437. Epub 2019 Jul 2.
Kinetic modeling of the in vivo pyruvate-to-lactate conversion is crucial to investigating aberrant cancer metabolism that demonstrates Warburg effect modifications. Non-invasive detection of alterations to metabolic flux might offer prognostic value and improve the monitoring of response to treatment. In this clinical research project, hyperpolarized [1-C] pyruvate was intravenously injected in a total of 10 brain tumor patients to measure its rate of conversion to lactate ( k ) and bicarbonate ( k ) via echo-planar imaging. Our aim was to investigate new methods to provide k and k maps with whole-brain coverage. The approach was data-driven and addressed two main issues: selecting the optimal model for fitting our data and determining an appropriate goodness-of-fit metric. The statistical analysis suggested that an input-less model had the best agreement with the data. It was also found that selecting voxels based on post-fitting error criteria provided improved precision and wider spatial coverage compared to using signal-to-noise cutoffs alone.
对体内丙酮酸向乳酸转化的动力学建模对于研究表现出瓦博格效应改变的异常癌症代谢至关重要。对代谢通量变化的非侵入性检测可能具有预后价值，并改善对治疗反应的监测。在这个临床研究项目中，共向 10 名脑肿瘤患者静脉注射了[1-C]高极化丙酮酸，通过回波平面成像测量其转化为乳酸（k）和碳酸氢盐（k）的速率。我们的目的是研究提供全脑覆盖的 k 和 k 图谱的新方法。该方法是数据驱动的，并解决了两个主要问题：为拟合我们的数据选择最佳模型和确定适当的拟合优度度量。统计分析表明，无输入模型与数据的一致性最好。还发现，与仅使用信噪比截止值相比，根据拟合后误差标准选择体素可提供更高的精度和更广泛的空间覆盖范围。
