Predicting Histotripsy Focal Shifts in the Liver From Acoustic Aberrations Using a Deep Learning Model

To develop a deep learning model for predicting histotripsy focal shifts in the liver caused by acoustic aberrations for real-time treatment optimization.

A modified VGG19 CNN regression model was trained using 12,870 scenarios derived from 243 segmented human CT volumes. Input to the model consisted of 6-channel maps representing the distance through specific tissue types (bone, air, fat, muscle, liver, total tissue) along transducer-to-focus rays. Ground truth focus locations were determined via acoustic simulations (k-Wave) based on the minimum pressure location. The network was trained to output predicted focal shifts relative to the geometric focus location. Accuracy was evaluated as the mean absolute deviation between CNN predictions and ground truth simulations. An ablation analysis determined dominant features.

The simulation predicted aberration-induced focal shifts ranging from -12.9 to 3 mm. Across five-fold cross-validation, the model predicted shifts with a mean absolute deviation (standard deviation) across the folds of 0.3 (0.3), 0.3 (0.3), 0.5 (0.5) mm in X, Y, and Z, respectively. The corresponding mean bias was 0.10 mm, 0.08 mm, and 0.04 mm. A single one-sided t-test confirmed the absolute prediction errors were significantly less than the 1 mm clinical margin (p $\mathbf {< }$ 0.001). Ablation analysis revealed fat, liver, and total tissue as dominant predictors. Notably, CNN inference time was 13.8 ms, compared to a median 10.3 (IQR 3.6) hours for acoustic simulations performed on a high-performance cluster.

The proposed CNN accurately predicts aberration shifts in focus location comparable to acoustic simulations with millisecond-scale inference speeds, enabling real-time aberration correction.