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.
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.
Breast conserving surgery is a common treatment option for early-stage breast cancer, and it relies on complete tumor excision such that no residual cancer is left in the resection cavity. However, the use of preoperative imaging to inform excision is compromised by intraoperative deformations that change the location, volume, and shape of the tumor compared to the imaging configuration. For intra-procedural guidance specifically, incision and retraction alter the tumor presentation and geometry. Being able to compensate for retraction deformations intraoperatively may increase the utility of image guidance technologies. In this work, a breast retraction phantom and deformation modeling approach are developed to explore the potential of modeling retraction for image guidance during BCS. Surface and subsurface beads were embedded in a realistic silicone breast phantom, and CT images were acquired in undeformed and retracted states. A reconstructive, sparse-data registration method was used to model retraction. Modeling accuracy was evaluated by comparing model-predicted and ground-truth bead displacements. The average surface bead registration error after retraction modeling in a region of interest was 0.5 ± 0.1 mm (maximum 0.5 mm). The average subsurface bead registration error in a region of interest was 1.2 ± 0.6 mm (maximum 2.6 mm). A biomechanical modeling method that includes retraction may improve the accuracy of image guidance for breast conserving surgery, but more work is needed to evaluate its utility.
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