Noninvasive microwave ablation zone radii estimation using x‐ray CT image analysis. Issue 8 (14th July 2016)

• Record Type: Journal Article
• Authors: Weiss, Noam; Goldberg, S. Nahum; Nissenbaum, Yitzhak; Sosna, Jacob; Azhari, Haim
• Is Part Of: Medical physics. Volume 43:Issue 8(2016)Part 1
• Page Start: 4476
• Page End: 4482
• Publication Date: 2016-07-14
• Subjects: biological tissues -- computerised tomography -- diagnostic radiography -- entropy -- image texture -- liver -- mean square error methods -- medical image processing -- microwave antennas -- patient monitoring -- radiation therapy; Computed tomography -- Computed radiography -- Image analysis -- General statistical methods -- Therapeutic applications; Computerised tomographs -- Radiation therapy -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of texture; microwave -- thermal ablation -- x‐ray CT -- thermal monitoring; Medical imaging -- Tissue ablation -- Computed tomography -- Antennas -- Entropy -- Liver -- Image analysis -- Microwaves -- Pathology -- Laser ablation; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4954843
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: The aims of this study were to noninvasively and automatically estimate both the radius of the ablated liver tissue and the radius encircling the treated zone, which also defines where the tissue is definitely untreated during a microwave (MW) thermal ablation procedure. Methods: Fourteen ex vivo bovine fresh liver specimens were ablated at 40 W using a 14 G microwave antenna, for durations of 3, 6, 8, and 10 min. The tissues were scanned every 5 s during the ablation using an x‐ray CT scanner. In order to estimate the radius of the ablation zone, the acquired images were transformed into a polar presentation by displaying the Hounsfield units (HU) as a function of angle and radius. From this polar presentation, the average HU radial profile was analyzed at each time point and the ablation zone radius was estimated. In addition, textural analysis was applied to the original CT images. The proposed algorithm identified high entropy regions and estimated the treated zone radius per time. The estimated ablated zone radii as a function of treatment durations were compared, by means of correlation coefficient and root mean square error (RMSE) to gross pathology measurements taken immediately post‐treatment from similarly ablated tissue. Results: Both the estimated ablation radii and the treated zone radii demonstrated strong correlation with the measured gross pathology values ( R 2 ≥ 0.89 and R 2 ≥ 0.86, respectively). The automated ablation radii estimation had an average discrepancy of less than 1 mm (RMSE = 0.65 mm) from the gross pathology measured values, while the treated zone radii showed a slight overestimation of approximately 1.5 mm (RMSE = 1.6 mm). Conclusions: Noninvasive monitoring of MW ablation using x‐ray CT and image analysis is feasible. Automatic estimations of the ablation zone radius and the radius encompassing the treated zone that highly correlate with actual ablation measured values can be obtained. This technique can therefore potentially be used to obtain real time monitoring and improve the clinical outcome.