ArticleIEEE transactions on bio-medical engineering2020
Toward Image Data-Driven Predictive Modeling for Guiding Thermal Ablative Therapy.
Article in IEEE transactions on bio-medical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Personalization of liver microwave ablation simulation.International journal of computer assisted radiology and surgery · 2026Article
- Simulation of Image-Guided Microwave Ablation Therapy Using a Digital Twin Computational Model.IEEE open journal of engineering in medicine and biology · 2024Article
- Review
- Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveAccurate 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.
methodsIn 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.
resultsThis 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.
conclusionThese 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.
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