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Njølstad T, Schulz A, Godt JC, et al. Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique - initial clinical experience. Acta Radiol Open. 2021;10(4):20584601211008391doi: 10.1177/20584601211008391.
Njølstad, T., Schulz, A., Godt, J. C., Brøgger, H. M., Johansen, C. K., Andersen, H. K., & Martinsen, A. C. T. (2021). Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique - initial clinical experience. Acta radiologica open, 10(4), 20584601211008391. https://doi.org/10.1177/20584601211008391
Njølstad, Tormund, et al. "Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique - initial clinical experience." Acta radiologica open vol. 10,4 (2021): 20584601211008391. doi: https://doi.org/10.1177/20584601211008391
Njølstad T, Schulz A, Godt JC, Brøgger HM, Johansen CK, Andersen HK, Martinsen ACT. Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique - initial clinical experience. Acta Radiol Open. 2021 Apr 09;10(4):20584601211008391. doi: 10.1177/20584601211008391. eCollection 2021 Apr. PMID: 33889427; PMCID: PMC8040588.
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Acta Radiol Open. 2021 Apr 09;10(4):20584601211008391. doi: 10.1177/20584601211008391. eCollection 2021 Apr.

Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique - initial clinical experience.

Acta radiologica open

Tormund Njølstad, Anselm Schulz, Johannes C Godt, Helga M Brøgger, Cathrine K Johansen, Hilde K Andersen, Anne Catrine T Martinsen

Affiliations

  1. Department of Radiology and Nuclear Medicine, Oslo University Hospital Ullevål, Oslo, Norway.
  2. Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway.
  3. Department of Radiology, Haukeland University Hospital, Bergen, Norway.
  4. Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.

PMID: 33889427 PMCID: PMC8040588 DOI: 10.1177/20584601211008391

Abstract

BACKGROUND: A novel Deep Learning Image Reconstruction (DLIR) technique for computed tomography has recently received clinical approval.

PURPOSE: To assess image quality in abdominal computed tomography reconstructed with DLIR, and compare with standardly applied iterative reconstruction.

MATERIAL AND METHODS: Ten abdominal computed tomography scans were reconstructed with iterative reconstruction and DLIR of medium and high strength, with 0.625 mm and 2.5 mm slice thickness. Image quality was assessed using eight visual grading criteria in a side-by-side comparative setting. All series were presented twice to evaluate intraobserver agreement. Reader scores were compared using univariate logistic regression. Image noise and contrast-to-noise ratio were calculated for quantitative analyses.

RESULTS: For 2.5 mm slice thickness, DLIR images were more frequently perceived as equal or better than iterative reconstruction across all visual grading criteria (for both DLIR of medium and high strength, p < 0.001). Correspondingly, DLIR images were more frequently perceived as better (as opposed to equal or in favor of iterative reconstruction) for visual reproduction of liver parenchyma, intrahepatic vascular structures as well as overall impression of image noise and texture (p < 0.001). This improved image quality was also observed for 0.625 mm slice images reconstructed with DLIR of high strength when directly comparing to traditional iterative reconstruction in 2.5 mm slices. Image noise was significantly lower and contrast-to-noise ratio measurements significantly higher for images reconstructed with DLIR compared to iterative reconstruction (p < 0.01).

CONCLUSIONS: Abdominal computed tomography images reconstructed using a DLIR technique shows improved image quality when compared to standardly applied iterative reconstruction across a variety of clinical image quality criteria.

© The Foundation Acta Radiologica 2021.

Keywords: Abdominal computed tomography; deep learning image reconstruction; image quality

Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: This study is p

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