Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy

Abstract

Computational tools are beginning to enable patient-specific surgical planning to localize and prescribe thermal dosing for liver cancer ablation therapy. Tissue-specific factors (e.g., tissue perfusion, material properties, disease state, etc.) have been found to affect ablative therapies, but current thermal dosing guidance practices do not account for these differences. Computational modeling of ablation procedures can integrate these sources of patient specificity to guide therapy planning and delivery. This paper establishes an imaging-data-driven framework for patient-specific biophysical modeling to predict ablation extents in livers with varying fat content in the context of microwave ablation (MWA) therapy. Patient anatomic scans were segmented to develop customized three-dimensional computational biophysical models and mDIXON fat-quantification images were acquired and analyzed to establish fat content and determine biophysical properties. Simulated patient-specific microwave ablations of tumor and healthy tissue were performed at four levels of fatty liver disease. Ablation models with greater fat content demonstrated significantly larger treatment volumes compared to livers with less severe disease states. More specifically, the results indicated an eightfold larger difference in necrotic volumes with fatty livers vs. the effects from the presence of more conductive tumor tissue. Additionally, the evolution of necrotic volume formation as a function of the thermal dose was influenced by the presence of a tumor. Fat quantification imaging showed multi-valued spatially heterogeneous distributions of fat deposition, even within their respective disease classifications (e.g., low, mild, moderate, high-fat). Altogether, the results suggest that clinical fatty liver disease levels can affect MWA, and that fat-quantitative imaging data may improve patient specificity for this treatment modality.

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

“…After ~5 minutes, ablation volume trajectories in the enhanced digital twin models do have some distinction from the control models, but it is limited. Looking across disease states, higher fat content corresponds to larger ablations in both model types, a trend established in previous work [13]. To further illustrate the effects of fat content and heterogeneity on the ablation zone, the long-axis and short-axis diameters were calculated by fitting an ellipsoidal equation to the ablation zone at the end of the 15-minute ablation.…”

“…Modeling Framework: Patients' MR images were retrospectively reviewed by an experienced radiologist on one of two clinical scannersa clinical Philips Intera Achieva 3T MRI or a 1.5T Siemens Magnetom Sola MR scanner. The patient images, image sequences, and fat sampling methods to produce the homogenous digital twin models (control) are the same as those reported in previous work [13]. Figure 1 is the framework used in the study reported herein.…”

“…Shows the disease state index and the corresponding percent fat derived from the fat quantification imaging data for each patient. Material properties at a particular disease state were determined using the material characteristic curves established in previous work but with a modified scale based on the human data range [13], [15]. The specific values were only applied to the homogenous-tissue models.…”

“…Contrary to these experiments, patients with HCC often present with fatty liver disease and/or fibrosis, and it is well understood that fatty tissue has lower dielectric and thermal properties than healthy tissue [11]. Furthermore, evidence suggests that even if patients are stratified in disease groups, each patient may have significantly heterogeneous liver fat spatial distribution [12], [13]. Ultimately, there is a clinical gap in our understanding of the effects of disease presentation on ablation forecasting.…”

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

“…In previous work, we proposed a computational framework to transform patient diagnostic and anatomical imaging data into a digital twin of thermal ablation with patient-specific but homogeneous tissue parameters [43] . The imaging data were used in two roles: to segment the liver parenchyma, and to sample the liver fat and map material properties using characteristic equations derived from prior work in mock tissue phantoms.…”