| Autor: |
Cathrine K Johansen, Tormund S. Njolstad, Anselm Schulz, Johannes Clemens Godt, Hilde Kjernlie Andersen, Anne Catrine Trægde Martinsen, Helga Brøgger |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Acta Radiologica Open |
| ISSN: |
2058-4601 |
| Popis: |
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 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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