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Cieri, Enrico; Simonte, Gioele; Costarelli, Danilo; Fiorucci, Beatrice; Isernia, Giacomo; Seracini, Marco; Vinti, Gianluca (2019): Computed Tomography Postprocessing for Abdominal Aortic Aneurysm Lumen Recognition in Unenhanced Examinations. In: Annals of Vascular Surgery, Vol. 60: pp. 407-414
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Introduction: Contrast medium (CM) use in computed tomography (CT) is limited by nephrotoxicity and possible allergic reactions. The purpose of this study is to introduce a tool for the diagnosis of abdominal aortic aneurysms (AAAs) by avoiding the use of CM. Methods: With and without CM CTs of patients with AAA were evaluated. A mathematical algorithm was implemented to allow visualization of the inner aortic lumen in the series without CM. The first step of the algorithm consisted in manually highlighting a squared region of interest (ROI) close to the target aortic area. The rest of the algorithm is completely automated and performs the following flow of operations: The "Kantorovich'' algorithm is applied to the ROI for image enhancement. Then, a wavelet decomposition method is applied to identify the different frequency components of the image. Exploiting the wavelet decomposition, the system selects the low-frequency components of the image, corresponding to the major structures. Thresholding method, based on the analysis of the gray-level histogram, is then performed to extract the contours of the vessel. At this point, the extraction of the pervious area is completed. Final images were compared with the contrast enhanced scans, valued as gold standard. To validate the algorithm, an analysis of the results has been performed considering the following types of error: E-n = #m/#ROI Delta A = vertical bar 1-(#CM/# CEX) j (#(m) = number of misclassified pixels;#(ROI) = number of pixels in the ROI;#(CM) = number of pixels belonging to the CM;#CEX = extracted areas). E-n provided a measure on the pixels wrongly classified, and Delta(A) the difference of extracted areas. Results: The algorithm was applied to 233 CT images. Extracted images were compared with the ones with CM. Mean values of the numerical errors ranged from 0.12 to 0.17 for E-n and 0.11 to 0.35 for Delta(A). For all the 233 CT images in the analyzed sequences, the mean error was 0.14 (E-n) and 0.28 (Delta A). Conclusions: The developed mathematical algorithm allows to detect the position of the vessel lumen and to extract its contours with a good accuracy. Our experience shows encouraging results and suggests a possible future clinical application to reduce CT CM use.