A ConvLSTM-based model for predicting thermal damage during laser interstitial thermal therapy.

Abstract

Objective: Accurate prediction of thermal damage extent is essential for effective and precise thermal therapy, especially in brain laser interstitial thermal therapy (LITT). Immediate postoperative contrast-enhanced T1-weighted imaging (CE-T1WI) is the primary method for clinically assessing in vivo thermal damage after image-guided LITT. CE-T1WI reveals a hyperintense enhancing rim surrounding the target lesion, which serves as a key radiological marker for evaluating the thermal damage extent. Although widely used in clinical practice, traditional thermal damage models rely on empirical parameters from in vitro experiments, which can lead to inaccurate predictions of thermal damage in vivo. Additionally, these models predict only two tissue states (damaged or undamaged), failing to capture three tissue states observed on post-CE-T1WI images, highlighting the need for improved thermal damage prediction methods.

Approach: This study proposes a novel Convolutional Long Short-Term Memory (ConvLSTM)-based model that utilizes intraoperative temperature distribution history data measured by magnetic resonance temperature imaging (MRTI) during LITT to predict the enhancing rim on post-CE-T1WI images. This method was implemented and evaluated on retrospective data from 56 patients underwent brain LITT. Main results: The proposed model effectively predicts the enhancing rim on postoperative images, achieving an average Dice Similarity Coefficient (DSC) of 0.82 (±0.063) on the test dataset. Furthermore, it generates real-time predicted thermal damage area variation trends that closely resemble those of the traditional thermal damage model, suggesting potential for real-time prediction of thermal damage extent.

Significance: This method could provide a valuable tool for visualizing and assessing intraoperative thermal damage extent. .