Department of Radiology, Stanford University, Stanford, California 94304, USA.
Med Phys. 2010 Sep;37(9):5014-26. doi: 10.1118/1.3475943.
Magnetic resonance thermometry using the proton resonance frequency (PRF) shift is a promising technique for guiding thermal ablation. For temperature monitoring in moving organs, such as the liver and the heart, problems with motion must be addressed. Multi-baseline subtraction techniques have been proposed, which use a library of baseline images covering the respiratory and cardiac cycle. However, main field shifts due to lung and diaphragm motion can cause large inaccuracies in multi-baseline subtraction. Referenceless thermometry methods based on polynomial phase regression are immune to motion and susceptibility shifts. While referenceless methods can accurately estimate temperature within the organ, in general, the background phase at organ/tissue interfaces requires large polynomial orders to fit, leading to increased danger that the heated region itself will be fitted by the polynomial and thermal dose will be underestimated. In this paper, a hybrid method for PRF thermometry in moving organs is presented that combines the strengths of referenceless and multi-baseline thermometry.
The hybrid image model assumes that three sources contribute to image phase during thermal treatment: Background anatomical phase, spatially smooth phase deviations, and focal, heat-induced phase shifts. The new model and temperature estimation algorithm were tested in the heart and liver of normal volunteers, in a moving phantom HIFU heating experiment, and in numerical simulations of thermal ablation. The results were compared to multi-baseline and referenceless methods alone.
The hybrid method allows for in vivo temperature estimation in the liver and the heart with lower temperature uncertainty compared to multi-baseline and referenceless methods. The moving phantom HIFU experiment showed that the method accurately estimates temperature during motion in the presence of smooth main field shifts. Numerical simulations illustrated the method's sensitivity to algorithm parameters and hot spot features.
This new hybrid method for MR thermometry in moving organs combines the strengths of both multi-baseline subtraction and referenceless thermometry and overcomes their fundamental weaknesses.
利用质子共振频率(PRF)偏移进行磁共振测温是指导热消融的一种很有前途的技术。对于肝脏和心脏等运动器官的温度监测,必须解决运动问题。已经提出了多基线减法技术,该技术使用涵盖呼吸和心脏周期的基线图像库。然而,由于肺部和横膈膜运动引起的主磁场偏移会导致多基线减法产生很大的误差。基于多项式相位回归的无参考测温方法不受运动和磁化率偏移的影响。虽然无参考方法可以准确估计器官内的温度,但通常情况下,器官/组织界面的背景相位需要大的多项式阶数来拟合,这增加了加热区域本身被多项式拟合的风险,从而低估了热剂量。本文提出了一种用于运动器官 PRF 测温的混合方法,该方法结合了无参考和多基线测温的优点。
混合图像模型假设在热治疗过程中,有三个来源会导致图像相位变化:背景解剖相位、空间平滑相位偏差和聚焦、热诱导相位变化。新模型和温度估计算法在正常志愿者的心脏和肝脏、移动体模 HIFU 加热实验以及热消融的数值模拟中进行了测试。结果与单独的多基线和无参考方法进行了比较。
与多基线和无参考方法相比,该混合方法允许在体内对肝脏和心脏进行温度估计,且温度不确定性更低。移动体模 HIFU 实验表明,该方法在存在平滑主磁场偏移的情况下能够准确估计运动过程中的温度。数值模拟说明了该方法对算法参数和热点特征的敏感性。
这种新的用于运动器官磁共振测温的混合方法结合了多基线减法和无参考测温的优点,并克服了它们的根本弱点。