Fast Automatic Liver Tumor Radiofrequency Ablation Planning via Learned Physics Model

Abstract

Radiofrequency ablation is a minimally-invasive therapy recommended for the treatment of primary and secondary liver cancer in early stages and when resection or transplantation is not feasible. To significantly reduce chances of local recurrences, accurate planning is required, which aims at finding a safe and feasible needle trajectory to an optimal electrode position achieving full coverage of the tumor as well as a safety margin. Computer-assisted algorithms, as an alternative to the time-consuming manual planning performed by the clinicians, commonly neglect the underlying physiology and rely on simplified, spherical or ellipsoidal ablation estimates. To drastically speed up biophysical simulations and enable patient-specific ablation planning, this work investigates the use of non-autoregressive operator learning. The proposed architecture, trained on 1,800 biophysics-based simulations, is able to match the heat distribution computed by a finite-difference solver with a root mean squared error of 0.51 ± 0.50 $^\circ$C and the estimated ablation zone with a mean dice score of 0.93 ± 0.05, while being over 100 times faster. When applied to single electrode automatic ablation planning on retrospective clinical data, our method achieves patient-specific results in less than 4 mins and closely matches the finite-difference-based planning, while being at least one order of magnitude faster. Run times are comparable to those of sphere-based planning while accounting for the perfusion of liver tissue and the heat sink effect of large vessels.