Multiphysics modeling toward enhanced guidance in hepatic microwave ablation: a preliminary framework

Abstract

We compare a surface-driven, model-based deformation correction method to a clinically relevant rigid registration approach within the application of image-guided microwave ablation for the purpose of demonstrating improved localization and antenna placement in a deformable hepatic phantom. Furthermore, we present preliminary computational modeling of microwave ablation integrated within the navigational environment to lay the groundwork for a more comprehensive procedural planning and guidance framework. To achieve this, we employ a simple, retrospective model of microwave ablation after registration, which allows a preliminary evaluation of the combined therapeutic and navigational framework. When driving registrations with full organ surface data (i.e., as could be available in a percutaneous procedure suite), the deformation correction method improved average ablation antenna registration error by 58.9% compared to rigid registration (i.e., $2.5\pm 1.1\mathrm{mm}$ , $5.6\pm 2.3\mathrm{mm}$ of average target error for corrected and rigid registration, respectively) and on average improved volumetric overlap between the modeled and ground-truth ablation zones from $67.0\pm 11.8\%$ to $85.6\pm 5.0\%$ for rigid and corrected, respectively. Furthermore, when using sparse-surface data (i.e., as is available in an open surgical procedure), the deformation correction improved registration error by 38.3% and volumetric overlap from $64.8\pm 12.4\%$ to $77.1\pm 8.0\%$ for rigid and corrected, respectively. We demonstrate, in an initial phantom experiment, enhanced navigation in image-guided hepatic ablation procedures and identify a clear multiphysics pathway toward a more comprehensive thermal dose planning and deformation-corrected guidance framework.

Keywords: deformation; image-guided surgery; liver; microwave ablation; registration.

Fig. 1

(a) Agar-albumin phantom liver in its predeformation state. MWA antenna is seen inserted into the right lobe. (b) Mock gross pathology of an ablation within the agar-albumin phantom. The outer ablation contour, ablation antenna tip location, and ablation antenna shaft are clearly visible. (c) Slice from the ${\mathrm{T}}_2$-weighted MRI of the ablation zone from which the outer ablation contour, ablation antenna tip location, and ablation antenna shaft were segmented.

Fig. 2

Representation of the degree of deformation achieved in the deformable hepatic ablation phantom. The colormap represents the signed surface error after rigidly registering the pre- and postdeformation phantom image segmentations. The ablation zones are presented as green volumes. In this case of deformation, 20- to 30-mm thick support material was placed beneath the superior right lobe to simulate surgical packing for organ presentation. In total, four applications of deformation were applied and imaged within the phantom.

Fig. 3

Model-predicted temperature maps, observed (black line), and model-predicted (red dashed line) ablation zones are presented for each case of ablation with the Perseon ST antenna within the agar-albumin hepatic deformation phantom. The observed ablation zone extent was gathered from mock gross pathology and used to drive the inverse MWA model. It is important to note that each ablation occurred in a different area of the phantom with varying tissue thickness and antenna depth.

Fig. 4

Histograms of the target errors resulting from the two methods of registration applied to the eight image-to-physical registration scenarios within our agar-albumin deformation phantom. Results of the rigid registration using the weighted salient feature ICP method of Clements et al. are presented in blue/gray. Results of the deformation correction method of Heiselman et al. are presented in yellow/orange. (a) Results from registering with full-surface data. (b) Results from registering with sparse-surface data.

Fig. 5

Distributions of the volumetric overlap of observed and predicted ablation zones represented by the PPV. The box and whiskers represent the mean, median, upper and lower quartiles, maximum, and minimum PPV for the rigid registration method of Clements et al. in blue/gray and the deformation correction method of Heiselman et al. in yellow/orange. Presented results are from registering with full-surface data (blue/yellow) and sparse-surface data (gray/orange).

Fig. 6

The PPV is presented for each registered ablation (24 total) as a function of the average target registration of the corresponding ablation antenna. Antenna TRE was calculated as the average error of the antenna tip, insertion point, and ablation centroid. Results of the rigid registration method of Clements et al. are presented in blue and the deformation correction method of Heiselman et al. are presented in yellow. MWA model results in the condition of perfect registration (manual alignment) are presented for comparison in red. Presented results are from registering with full-surface data.

Fig. 7

An example of ablation model predictions following registration with sparse anterior surface data. In each panel, green represents the ground truth ablation zone as observed in MRI. The rigidly registered ablation model is presented in (a) and detailed views in (b). The registered ablation model following deformation correction is presented in (c) and detailed views in (d). Additionally, in each panel the registered ablation antenna are indicated by lines with color corresponding to the registration method.