Digital twin forecasting of microwave ablation via fat quantification image-to-grid computational methods

Abstract

Computational tools, such as "digital twin" modeling, are beginning to enable patient-specific surgical planning of ablative therapies to treat hepatocellular carcinoma. Digital twins models use patient functional data and biomarker imaging to build anatomically accurate models to forecast therapeutic outcomes through simulation, i.e., providing accurate information for guiding clinical decision-making. In microwave ablation (MWA), tissue-specific factors (e.g., tissue perfusion, material properties, disease state, etc.) can affect ablative therapies, but current thermal dosing guidelines do not account for these differences. This study establishes an imaging-data-driven framework to construct digital twin biophysical models to predict ablation extents in livers with varying fat content in MWA. Patient anatomic scans were segmented to develop customized three-dimensional computational biophysical models, and fat-quantification images were acquired to reconstruct spatially accurate biophysical material properties. Simulated patient-specific microwave ablations of homogenous digital-twin models (control) and enhanced digital twin models were performed at four levels of fatty liver disease. When looking at the short diameter (SD), long diameter (LD), ablation volume, and spherical index of the ablation margins -the heterogenous digital-twin models did not produce significantly different ablation margins compared to the control models. Both models produced results that report ablation margins for patients with high-fat livers are larger than low-fat livers (LD of 6.17cm vs. 6.30cm and SD of 2.10 vs. 1.99, respectively). Overall, the results suggest that modeling heterogeneous clinical fatty liver disease using fat-quantitative imaging data has the potential to improve patient specificity for this treatment modality.

Simulation of Image-Guided Microwave Ablation Therapy Using a Digital Twin Computational Model

“…For this determination, the average fat percentage (determined by mDIXON image analysis) across the entire liver (excluding vasculature) was calculated and then used within the context of Equation ( 7) to estimate the respective liver parenchymal material property. This approach reflects previous work in which it was found that the difference in therapy simulations was minima when comparing a homogeneous description versus a mapping of properties voxelby-voxel within the context of diffuse disease [48]. Given that the patients in this study presented with diffuse fatty liver disease, this approach was appropriate for the liver parenchyma.…”

“…In mDIXON imaging, the fat-to-water signal ratio establishes a fat fraction parameter, which represents the percentage of fat in a given volume of tissue [22] . The intensities of the fat fraction image were directly mapped into material properties and imported into the digital twin finite element model [47] . A material mixture equation to describe fat-dependent dielectric and thermal material properties is expressed as follows, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*} m\left( {f{\mathrm{\% }}} \right) = \left( {\left( {{m}_{liver} - {m}_{fat}} \right){e}^{ - {\tau }_k{\mathrm{*}}f{\mathrm{\% }}}} \right) + {m}_{fat}\ \tag{7} \end{equation*}\end{document} …”

“…It is noteworthy to observe the considerable variance in fat percentages within an individual liver for each patient and among the patient cohort. In previous work, it was demonstrated that in the case of distributed (non-focal) fat deposition, a voxel-by-voxel mapping of changes due to disease performed very similarly to a model that assumed homogeneity but at the corresponding diseased state grade [48]. While it may be sufficient to model the parenchyma using homogenous material properties under some conditions, fat quantification values associated with the tumor, however, demonstrated to be a significant heterogeneity as compared to the surrounding parenchyma.…”

“…In mDIXON imaging, the fat-to-water signal ratio establishes a fat fraction parameter, which represents the percentage of fat in a given volume of tissue [22]. The intensities of the fat fraction image were directly mapped into material properties and imported into the digital twin finite element model [48]. A material mixture equation to describe fat-dependent dielectric and thermal material properties is expressed as follows, Steatosis grade index and the corresponding average percent fat derived from the fat quantification imaging data for each patient.…”