Hyperthermia induced 3D temperature distribution in a human sarcoma with tumor perfusion reconstructed using fractal interpolation functions

Essential to the success of optimized thermal treatment duringhyperthermia is accurate modeling. Advection of energy due to bloodperfusion significantly perturbs the temperature and without accurateestimates of the magnitude of the local tissue blood perfusion it isunlikely that accurate estimates of the temperature distribution can bemade. It is shown here that the blood mass flow rate per unit volumeof tissue in the Pennes' bio-heat equation can be modeled using arelative perfusion index (RPI) determined with dynamic-enhancedmagnetic resonance imaging (DE-MRI). The existing technologylimits the DE-MRI perfusion data to be acquired in a limited numberof slices. Consequently, the tumor perfusion data is interpolatedusing fractal interpolation functions (FIFs), as it has been shown thatthe RPI data is fractal, and that fractal interpolation is superior tolinear interpolation when a 3D fractal-like scaling exists. Forillustration, a patient treated with hyperthermia at Duke UniversityMedical Center for a high-grade leg tissue sarcoma is modeled. Forcontrol, the resultant temperatures are compared to non-invasivelymeasured temperatures using the MR thermometry technique. Strongcorrelation is found between the DE-MRI perfusion images, the MRchemical shift images during heating, and the numerical simulation ofthe temperature field, emphasizing the relation between the DE-MRImeasured values and advective heat loss in tissue. The fractalinterpolation of DE-MRI data to obtain the 3D perfusion gives amore accurate temperature distribution compared to linearinterpolation. For even a better temperature reconstruction, furthermodels need to include large vessels.

Duke Scholars

Author Oana Craciunescu Radiation Oncology

Author Shiva Kumar Das Radiation Oncology