Abstract

The image quality of coronary CT angiography (CCTA) is important for the correct diagnosis of patients with suspected coronary artery disease, which is heavily influenced by image acquisition. Timing of the contrast media injection specifically influences the level of arterial enhancement, and it is aimed to allow optimal assessment of the coronary artery morphology. However, a consensus on an optimal acquisition protocol that can account for the large variety in patient cohorts has not been reached, commonly resulting in suboptimal arterial enhancement. In this work, we propose a generative adversarial network for the retrospective correction of contrast media attenuation in CCTA, thus reducing the dependency on an optimal timing protocol at acquisition. We develop and evaluate the method in a set of 1,179 CCTA scans with varying levels of contrast enhancement. We evaluate the consistency of intensity values in the coronary arteries and evaluate performance of coronary centerline extraction as a commonly performed analysis task. Results show that correction of contrast media attenuation values in CCTA scans is feasible, and that it improves the performance of automatic centerline extraction. The method may allow improved analysis of coronary arteries in CCTA scans with suboptimal contrast enhancement.
Original languageEnglish
Publication statusPublished - 4 Apr 2023
EventInternational Conference on Medical Imaging with Deep Learning, MIDL 2023 - Vanderbilt University, Nashville, United States
Duration: 9 Jul 202313 Jul 2023
https://2023.midl.io/

Conference

ConferenceInternational Conference on Medical Imaging with Deep Learning, MIDL 2023
Abbreviated titleMIDL
Country/TerritoryUnited States
CityNashville
Period9/07/202313/07/2023
Internet address

Keywords

  • contrast media timing
  • coronary CT angiography
  • coronary attenuation
  • deep learning
  • generative adversarial network

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