Is There Any Improvement in Image Quality in Obese Patients When Using a New X-ray Tube and Deep Learning Image Reconstruction in Coronary Computed Tomography Angiography?

Anne Sofie Brunebjerg Pfeffer*, Svea Deppe Mørup, Thomas Rueskov Andersen, Roda Abdulkadir Mohamed, Jess Lambrechtsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Deep learning image reconstruction (DLIR) is a technique that should reduce noise and improve image quality. This study assessed the impact of using both higher tube currents as well as DLIR on the image quality and diagnostic accuracy. The study consisted of 51 symptomatic obese (BMI > 30 kg/m2) patients with low to moderate risk of coronary artery disease (CAD). All patients underwent coronary computed tomography angiography (CCTA) twice, first with the Revolution CT scanner and then with the upgraded Revolution Apex scanner with the ability to increase tube current. Images were reconstructed using ASiR-V 50% and DLIR. The image quality was evaluated by an observer using a Likert score and by ROI measurements in aorta and the myocardium. Image quality was significantly improved with the Revolution Apex scanner and reconstruction with DLIR resulting in an odds ratio of 1.23 (p = 0.017), and noise was reduced by 41%. A total of 88% of the image sets performed with Revolution Apex + DLIR were assessed as good enough for diagnosis compared to 69% of the image sets performed with Revolution Apex/CT + ASiR-V. In obese patients, the combination of higher tube current and DLIR significantly improves the subjective image quality and diagnostic utility and reduces noise.

Original languageEnglish
Article number1428
JournalLife
Volume12
Issue number9
Number of pages13
ISSN2075-1729
DOIs
Publication statusPublished - 13. Sept 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Keywords

  • coronary artery disease (CAD)
  • coronary computed tomography angiography (CCTA)
  • deep learning image reconstruction (DLIR)
  • diagnostic utility
  • image quality

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