Reference for citation: Kodenko M. R., Samorodov A. V., Kulberg N. S., Reshetnikov R. V. Creating contrast-suppressed abdominal aorta CT datasets for training and testing artificial intelligence algorithms. Journal of Instrument Engineering. 2023. Vol. 66, N 11. P. 968—981 (in Russian). DOI: 10.17586/0021-3454-2023-66-11-968-981.

Abstract. 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.

Keywords: computed tomography, image processing, training datasets, artificial intelligence, synthetic non-contrast phase

Acknowledgement: The work was carried out within the framework of research and development (EGISU No.: 123031500002-1) in accordance with Order No. 1196 dated December 21, 2022 “On approval of government tasks, the financial support of which is carried out from the budget of the city of Moscow, to state budgetary (autonomous) institutions, subordinate to the Moscow City Health Department, for 2023 and the planning period of 2024 and 2025". The authors express their gratitude to the staff of the Center for Diagnostics and Telemedicine Technologies, Radiologists I. A. Blokhin, A. K. Smorchkova, and A. N. Khoruzhaya for expert validation of CT images.

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