成像体模
烧蚀
烧蚀区
背景(考古学)
微波消融
均方误差
计算机科学
生物医学工程
统计
核医学
数学
医学
古生物学
内科学
生物
作者
Jerry C. Collins,Jon S. Heiselman,Logan W. Clements,Jared A. Weis,Daniel B. Brown,Michael I. Miga
出处
期刊:IEEE Transactions on Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2020-06-01
卷期号:67 (6): 1548-1557
被引量:4
标识
DOI:10.1109/tbme.2019.2939686
摘要
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.
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