一种以数据为导向的方法,以更好地量化高极化[1-13C]pyruvate的体内代谢转化率
Yaewon Kim1, Tanner M Nickles1, Philip M Lee1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Magnetic resonance in medicine
|February 18, 2025
概括
这项研究引入了一种新的动力适配方法,用于高极化CMRI,以准确测量新陈代谢. 该方法纠正了翻转角度错误,改善了临床环境中酸盐到乳酸盐转化率 (kPL) 的量化.
科学领域:
- 医疗成像医学成像
- 代谢成像 - 代谢成像
- 生物物理学的生物物理.
背景情况:
- 使用高极化 (HP) 13C MRI精确量化新陈代谢对于临床应用至关重要.
- 射频翻转角度和其他动力模型参数的不确定性往往会影响代谢测量.
研究的目的:
- 开发和验证HP13C-pyruvateMRI的数据驱动动动力学配合方法.
- 为了弥补B1+领域的不确定性,并提高pyruvate-to-lactate转化率 (kPL) 确定率的准确性.
主要方法:
- 提出了一种新的数据驱动动力学配套方法,其中包含一个翻转角度缩放因子,以最大限度地减少配套残留物.
- 进行了数值模拟,以评估在不同翻转角度,kPL和T1放松条件下的性能.
- 该方法使用HP13C-pyruvate来自老鼠肝脏和脏的MRI数据进行了验证.
主要成果:
- 模拟显示,当翻转角度不同60%时,kPL误差显著减少 (从60%降至1%).
- 该方法表现出对T1不确定性的稳定性,中位数kPL误差在±3%内,即使T1变化是2倍.
- 与未经纠正的方法相比,大鼠研究显示乳酸信号适配 (1.4倍RMSE下降) 和kPL分布更紧密 (3.1倍标准偏差下降) 的改善.
结论:
- 拟议的数据驱动动力学拟合方法能够准确量化HP的C-酸盐代谢,尽管B不均.
- 这个模型显示了纠正其他错误源的潜力,例如T1放松和流动.
- 该方法可能对瘤分期和治疗反应评估具有临床价值.
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