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• W2972028005 abstract "Objective: Accurate prospective modeling of microwave ablation (MWA) procedures can provide powerful planning and navigational information to physicians. However, patient-specific tissue properties are generally unavailable and can vary based on factors such as relative perfusion and state of disease. Therefore, a need exists for modeling frameworks that account for variations in tissue properties. Methods: In this study, we establish an inverse modeling approach to reconstruct a set of tissue properties that best fit the model-predicted and observed ablation zone extents in a series of phantoms of varying fat content. We then create a model of these tissue properties as a function of fat content and perform a comprehensive leave-one-out evaluation of the predictive property model. Furthermore, we validate the inverse-model predictions in a separate series of phantoms that include co-recorded temperature data. Results: This model-based approach yielded thermal profiles in close agreement with experimental measurements in the series of validation phantoms (average root-mean-square error of 4.8 °C). The model-predicted ablation zones showed compelling overlap with observed ablations in both the series of validation phantoms (93.4 ± 2.2%) and the leave-one-out cross validation study (86.6 ± 5.3%). These results demonstrate an average improvement of 17.3% in predicted ablation zone overlap when comparing the presented property-model to properties derived from phantom component volume fractions. Conclusion: These results demonstrate accurate model-predicted ablation estimates based on image-driven determination of tissue properties. Significance: The work demonstrates, as a proof-of-concept, that physical modeling parameters can be linked with quantitative medical imaging to improve the utility of predictive procedural modeling for MWA." @default.
• W2972028005 created "2019-09-12" @default.
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• W2972028005 date "2020-06-01" @default.
• W2972028005 modified "2023-09-27" @default.
• W2972028005 title "Toward Image Data-Driven Predictive Modeling for Guiding Thermal Ablative Therapy" @default.
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• W2972028005 doi "https://doi.org/10.1109/tbme.2019.2939686" @default.
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