Visual Ensemble Analysis with Deep Learning Prediction for Studying the Effect of Tissue Properties on Radiofrequency Ablation

Radiofrequency (RF) ablation refers to a minimally invasive tumour ablation treatment using RF electromagnetic waves. A needle is placed inside the tumour, and an electrical current is applied, which is heating the tissue to burn the tumour. For treatment planning, the heat propagation is simulated, but the treatment volumes are significantly affected by the tissue properties, which vary between different patients, based upon both the vasculature and levels of fat and water content. Undertreatment can lead to tumour recurrence, while over-treatment damages healthy tissue. We propose to study the effect of tissue properties on the ablation based on an interactive visual analysis of simulation ensembles, where the tissue properties form the parameter space of the ensemble. The proposed Tissue Property Analysis Tool (TPAT) uses 2D and 3D spatial visualizations for comparative analysis of simulation outcomes for different parameter settings. A 3D parameter-space visualization allows for the analysis of the effect on the ablation result when modifying a selected parameter for the three involved tissues (tumour, liver and vessels). During the analysis, any parameter setting shall be accessible. When no simulation outcome has been generated for a selected parameter setting, we deploy a deep-learning-based surrogate model to predict an ablation outcome. Using our deep learning approach, we achieve an improved prediction accuracy both within and across spatial configurations, which outperforms interpolation-based schemes in 94.32% and 99.07% of the test data, respectively. We discuss our approach with domain experts for developing simulation models and demonstrate the usefulness of our approach for analysing the effect of tissue properties on RF ablation of liver tumours.