CREATING CONTRAST-SUPPRESSED ABDOMINAL AORTA CT DATASETS FOR TRAINING AND TESTING ARTIFICIAL INTELLIGENCE ALGORITHMS
Annotation
An approach to the automated acquisition of non-contrast computed tomography (CT) images containing abdominal aortic markings derived from contrast-enhanced phase scanning data is presented. An algorithm for suppressing contrast enhancement in the area of the abdominal aorta on a CT image is developed. The scientific novelty of the approach lies in the conversion of marked contrast images into non-contrast images using a developed mathematical model that allows for isolation and suppression of the component of X-ray absorption of the contrast agent. The algorithm was tested on an open data set consisting of 4 CT studies of the abdominal aorta, the balance of “aneurysm: normal” classes was 1:1. The results demonstrate the comparability of the X-ray density values in the study area with literature data, as well as the similarity of this area with the surrounding muscle tissue. Expert classification of a mixed sample containing real and generated images demonstrates the realism of the latter (accuracy of detection of artificial images - 35%, Fleiss kappa - 0.12). The resulting images are intended for training and testing artificial intelligence algorithms in the field of opportunistic screening of aortic aneurysm.
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