ABSTRACT
Purpose: To study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human in vivo liver surgery. Approach: A Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation. Data from n=10 patients undergoing liver resection were analyzed under an Institutional Review Board-approved study at Memorial Sloan Kettering Cancer Center. Organ surface coverage of each surface acquisition method was compared. Registration accuracies resulting from the acquisition techniques were compared for (1) rigid registration method (RRM), (2) model-based nonrigid registration method (NRM) using surface data only, and (3) NRM with one subsurface feature (vena cava) from tracked intraoperative ultrasound (NRM-VC). Novel vessel centerline and tumor targets were segmented and compared to their registered preoperative counterparts for accuracy validation. Results: Surface data coverage collected by stylus and Conoprobe were 24.6%±6.4% and 19.6%±5.0%, respectively. The average difference between stylus data and Conoprobe data using NRM was -1.05 mm and using NRM-VC was -1.42 mm, indicating the registrations to Conoprobe data performed worse than to stylus data with both NRM approaches. However, using the stylus and Conoprobe acquisition methods led to significant improvement of NRM-VC over RRM by average differences of 4.48 and 3.66 mm, respectively. Conclusion: The first use of a sterile-field amenable Conoprobe surface acquisition strategy in the OR is reported for open liver surgery. Under clinical conditions, the nonrigid registration significantly outperformed standard-of-care rigid registration, and acquisition by contact-based stylus and noncontact-based Conoprobe produced similar registration results. The accuracy benefits of noncontact surface acquisition with a Conoprobe are likely obscured by inferior data coverage and intrinsic noise within acquisition systems.
ABSTRACT
Emerging computational tools such as healthcare digital twin modeling are enabling the creation of patient-specific surgical planning, including microwave ablation to treat primary and secondary liver cancers. Healthcare digital twins (DTs) are anatomically one-to-one biophysical models constructed from structural, functional, and biomarker-based imaging data to simulate patient-specific therapies and guide clinical decision-making. In microwave ablation (MWA), tissue-specific factors including tissue perfusion, hepatic steatosis, and fibrosis affect therapeutic extent, but current thermal dosing guidelines do not account for these parameters. This study establishes an MR imaging framework to construct three-dimensional biophysical digital twins to predict ablation delivery in livers with 5 levels of fat content in the presence of a tumor. Four microwave antenna placement strategies were considered, and simulated microwave ablations were then performed using 915 MHz and 2450 MHz antennae in Tumor Naïve DTs (control), and Tumor Informed DTs at five grades of steatosis. Across the range of fatty liver steatosis grades, fat content was found to significantly increase ablation volumes by approximately 29-l42% in the Tumor Naïve and 55-60% in the Tumor Informed DTs in 915 MHz and 2450 MHz antenna simulations. The presence of tumor did not significantly affect ablation volumes within the same steatosis grade in 915 MHz simulations, but did significantly increase ablation volumes within mild-, moderate-, and high-fat steatosis grades in 2450 MHz simulations. An analysis of signed distance to agreement for placement strategies suggests that accounting for patient-specific tumor tissue properties significantly impacts ablation forecasting for the preoperative evaluation of ablation zone coverage.
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.
