A distribution-based method for thermal damage model analysis and optimization in brain laser interstitial thermal therapy

Abstract

MR-guided laser interstitial thermal therapy (LITT) has been regarded as a promising treatment option in neurosurgical oncology. One major advantage of MR-guided LITT is that temperature measurement can be provided to monitor the ablation in nearly real-time. The intraoperative temperature images make it possible to estimate thermal damage during the ablation. Several thermal damage models have been developed and used in LITT procedure, but the model parameters were mainly derived from laboratories in vitro experiments and varied in a wide range. As the frequency of MR-guided LITT procedure increases, there is a growing need to optimize thermal damage models utilizing clinical data. In this study, we dug into the relationship between the temperature history and postoperative ablation image collected in brain LITT surgery. Compared with the previous studies where the thermal damage models could only predict two tissue states: normal and ablated, our study considered three tissue states for thermal damage prediction: ablated, transitional and normal. We noticed that for different tissue states, the damage values calculated according to the Arrhenius formula were in different distribution patterns. Therefore, we developed a distribution-based thermal damage model. MR images of 15 patients were used in our study. The results showed that the proposed method could predict three tissue states and had higher prediction accuracy than the traditional Arrhenius model in clinical application.