Highlighting the effect of heterogeneous blood perfusion on radio-frequency ablation of human brain tumors: An image-based numerical investigation

Radio-frequency ablation (RFA) is a minimally invasive treatment that can provide highly localized and precise cancer therapy. Despite these advantages, its application in clinical practice remains limited. This is primarily due to the insufficient ablation volume, where blood perfusion plays a significant role. In this study, computational modeling is applied to examine the effects of heterogeneous blood perfusion on the tissue temperature, resultant thermal damage, and ablation volume in the RFA treatment. Two patients' heterogeneous blood perfusion and 3D brain tumor geometry are extracted from their dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data. This patient-specific information is imported into the bioheat transfer model developed in OpenFOAM to predict thermal effects. Results show that the heterogeneous blood perfusion leads to deeper heat penetration in the tumor tissue compared to the simulation in which blood perfusion is assumed to be uniform across the entire tumor. On average, the predicted tissue temperature and ablation volume are 10–11% and 30–38% higher for both patients, respectively, when the heterogeneous blood perfusion is adopted. In addition, the ablation volume is highly susceptible to the applied RF voltage and coolant temperature. These two factors can be optimized to maximize the treatment. However, the optimal settings vary for each patient, highlighting the demand for patient-specific design. Results obtained from this study can assist clinicians and surgeons in optimizing RFA parameters for a specific patient that can improve the treatment efficacy.