Magnetic resonance temperature imaging-based quantification of blood flow-related energy losses.

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.