Spatiotemporal Thermal Contours mapping of ex-vivo Bovine Liver Radiofrequency Thermal Ablation utilizing Hyperspectral Image and its Associated K-Mean Clustering Algorithm

Abstract

Significance: Hepatocellular carcinoma (HCC) is considered as worldwide health problem with a poor diagnosis due to limited detection techniques. Thermal ablation is the dominant modality to treat liver tumors for discriminating patients who are not allowed to have surgical intervention. Knowing that, observing or foreseeing the size of the subsequent tissue putrefaction during the Thermal Ablation techniques is a difficult undertaking. Aim: To examine the impacts of ablation zone volume following Radiofrequency ablation (RFA) of an ex-vivo bovine liver to correlate the impacts of thermal ablation with target organ perfusion; by exploiting the unique properties of Hyperspectral Imaging (HSI).where, Vessels may source cooling in the adjacent tumor target (heat‑sink‑effect) with risk of cancer recurrence and the infiltration profundity estimations consider the lessening of the tissue. Materials and Methods: Radiofrequency ablation was perfused on ex-vivo bovine livers at peripheral and central‑vessel‑adjacent locations, and monitored by HSI with a spectral range from 400 to 1000 nm. The system contains k-means clustering (K=8) algorithms combining spectral and spatial information. Labeled spectral signatures datasets were used as training data. Statistical analysis (10 samples) was computed to calculate the highest variance between six spectral images for determining the optimum wavelength for discrimination between the affected regions after thermal ablation (normal, thermal, and ablated liver tissue regions). Results: The change of the optical properties of ex-vivo liver tissues provides different responses to light transmission, scattering, absorption and particularly the reflection over the spectrum range. The spectral reflectance signatures were measured and evaluated using designed K-mean clustering algorithm after image reconstructed. Trials showed that spectral region 650~650 nm was proposed as optimum spectral range. Where, these results successfully distinguishes the Surface Thermal ablation region (x,y-axis),as well as the Thermal penetration Depth (z-axis) for Tissue characterization and Contour mapping for the unwanted thermal damage. Conclusions: Hyperspectral imaging is a powerful tool in real-time monitoring the thermal ablation and more accurate compared to the conventional imaging modality.