Dillon Christopher, Roemer Robert, Payne Allison
University of Utah, Radiology, Salt Lake City, UT, USA.
University of Utah, Mechanical Engineering, Salt Lake City, UT, USA.
NMR Biomed. 2015 Jul;28(7):840-51. doi: 10.1002/nbm.3318. Epub 2015 May 14.
This study presents a new approach for evaluating bioheat transfer equation (BHTE) models used in treatment planning, control and evaluation of all thermal therapies. First, 3D magnetic resonance temperature imaging (MRTI) data are used to quantify blood flow-related energy losses, including the effects of perfusion and convection. Second, this information is used to calculate parameters of a BHTE model: in this paper the widely used Pennes BHTE. As a self-consistency check, the BHTE parameters are utilized to predict the temperatures from which they were initially derived. The approach is evaluated with finite-difference simulations and implemented experimentally with focused ultrasound heating of an ex vivo porcine kidney perfused at 0, 20 and 40 ml/min (n = 4 each). The simulation results demonstrate accurate quantification of blood flow-related energy losses, except in regions of sharp blood flow discontinuities, where the transitions are spatially smoothed. The smoothed transitions propagate into estimates of the Pennes perfusion parameter but have limited effect on the accuracy of temperature predictions using these estimates. Longer acquisition time periods mitigate the effects of MRTI noise, but worsen the effect of flow discontinuities. For the no-flow kidney experiments the estimates of a uniform, constant Pennes perfusion parameter are approximately zero, and at 20 and 40 ml/min the average estimates increase with flow rate to 3.0 and 4.2 kg/m(3) /s, respectively. When Pennes perfusion parameter values are allowed to vary spatially, but remain temporally constant, BHTE temperature predictions are more accurate than when using spatially uniform, constant Pennes perfusion values, with reductions in RMSE values of up to 79%. Locations with large estimated perfusion values correspond to high flow regions of the kidney observed in T1 -weighted MR images. This novel, MRTI-based technique holds promise for improving understanding of thermal therapy biophysics and for evaluating biothermal models.
本研究提出了一种新方法，用于评估在所有热疗法的治疗计划、控制和评估中使用的生物热传递方程（BHTE）模型。首先，利用三维磁共振温度成像（MRTI）数据来量化与血流相关的能量损失，包括灌注和对流的影响。其次，该信息用于计算BHTE模型的参数：在本文中为广泛使用的Pennes BHTE。作为一种自洽性检验，利用BHTE参数来预测最初推导这些参数时所依据的温度。通过有限差分模拟对该方法进行了评估，并在体外以0、20和40 ml/min的灌注速率（每组n = 4）对猪肾进行聚焦超声加热的实验中予以实施。模拟结果表明，除了在血流急剧不连续的区域（其中过渡在空间上被平滑处理）外，与血流相关的能量损失能够得到准确量化。平滑后的过渡传播到Pennes灌注参数的估计值中，但对使用这些估计值进行温度预测的准确性影响有限。更长的采集时间段可减轻MRTI噪声的影响，但会加剧血流不连续的影响。对于无血流的肾脏实验，均匀、恒定的Pennes灌注参数估计值约为零，在20和40 ml/min时，平均估计值随流速增加，分别达到3.0和4.2 kg/m³/s。当允许Pennes灌注参数值在空间上变化，但在时间上保持恒定时，BHTE温度预测比使用空间均匀、恒定的Pennes灌注值时更准确，均方根误差值降低高达79%。估计灌注值较大的位置对应于在T1加权磁共振图像中观察到的肾脏高血流区域。这种基于MRTI的新技术有望增进对热疗法生物物理学的理解并评估生物热模型。
