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Microwave thermal ablation using CT-scanner for predicting the variation of ablated region over time: Advantages and limitations

TitleMicrowave thermal ablation using CT-scanner for predicting the variation of ablated region over time: Advantages and limitations
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2019
AuthorsStrigari, L., Minosse S., D'Alessio D., Farina L., Cavagnaro M., Cassano B., Pinto Rosanna, Vallati G., and Lopresto V.
JournalPhysics in Medicine and Biology
Volume64
ISSN00319155
KeywordsAblation, Antennas, Computerized tomography, CT imaging, Forecasting, Image segmentation, Measured temperatures, Multi variate analysis, Multivariant analysis, Probes, Temperature prediction, Temperature variation, The region of interest (ROI), Thermal ablation, Tissue, treatment planning
Abstract

This study aims at investigating in real-time the structural and dynamical changes occurring in an ex vivo tissue during a microwave thermal ablation (MTA) procedure. The experimental set-up was based on ex vivo liver tissue inserted in a dedicated box, in which 3 fibre-optic (FO) temperature probes were introduced to measure the temperature increase over time. Computed tomography (CT) imaging technique was exploited to experimentally study in real-time the Hounsfield Units (HU) modification occurring during MTA. The collected image data were processed with a dedicated MATLAB tool, developed to analyse the FO positions and HU modifications from the CT images acquired over time before and during the ablation procedures. The radial position of a FO temperature probe (rFO) and the value of HU in the region of interest (ROI) containing the probe (HUo), along with the corresponding value of HU in the contralateral ROI with respect to the MTA antenna applicator (HUc), were determined and registered over time during and after the MTA procedure. Six experiments were conducted to confirm results. The correlation between temperature and the above listed predictors was investigated using univariate and multivariate analysis. At the multivariate analysis, the time, rFO and HUc resulted significant predictive factors of the logarithm of measured temperature. The correlation between predicted and measured temperatures was 0.934 (p < 0.001). The developed tool allows identifying and registering the image-based parameters useful for predicting the temperature variation over time in each investigated voxel by taking into consideration the HU variation. © 2019 Institute of Physics and Engineering in Medicine.

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85067270683&doi=10.1088%2f1361-6560%2fab1a67&partnerID=40&md5=fe0f34d526294e949e769b06975c93a7
DOI10.1088/1361-6560/ab1a67
Citation KeyStrigari2019