Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction
| Autor: | Oostveen, L.J., Smit, E.J., Dekker, H.M., Buckens, C.F.M., Pegge, S.A.H., Lange, F. de, Sechopoulos, I., Prokop, M. |
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| Přispěvatelé: | TechMed Centre, Multi-Modality Medical Imaging |
| Rok vydání: | 2022 |
| Předmět: |
Tumours of the digestive tract Radboud Institute for Health Sciences [Radboudumc 14]
All institutes and research themes of the Radboud University Medical Center Urological cancers Radboud Institute for Health Sciences [Radboudumc 15] Other Research Radboud Institute for Health Sciences [Radboudumc 0] Vascular damage Radboud Institute for Health Sciences [Radboudumc 16] deep learning reconstruction Radiology Nuclear Medicine and imaging General Medicine n/a OA procedure Women's cancers Radboud Institute for Health Sciences [Radboudumc 17] abdomen CT |
| Zdroj: | American Journal of Roentgenology, 220(3), 381-388. American Roentgen Ray Society American Journal of Roentgenology, 220, 3, pp. 381-388 American Journal of Roentgenology, 220, 381-388 |
| ISSN: | 1546-3141 0361-803X |
| Popis: | Item does not contain fulltext BACKGROUND. Because thick-section images (typically 3-5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5-0.625 mm) for problem solving. Deep learning reconstruction (DLR) can yield thin-section images with low noise. OBJECTIVE. The purpose of this study is to compare abdominopelvic CT image quality between thin-section DLR images and thin- and thick-section hybrid iterative reconstruction (HIR) images. METHODS. This retrospective study included 50 patients (31 men and 19 women; median age, 64 years) who underwent abdominopelvic CT between June 15, 2020, and July 29, 2020. Images were reconstructed at 0.5-mm section using DLR and at 0.5-mm and 3.0-mm sections using HIR. Five radiologists independently performed pairwise comparisons (0.5-mm DLR and either 0.5-mm or 3.0-mm HIR) and recorded the preferred image for subjective image quality measures (scale, -2 to 2). The pooled scores of readers were compared with a score of 0 (denoting no preference). Image noise was quantified using the SD of ROIs on regions of homogeneous liver. RESULTS. For comparison of 0.5-mm DLR images and 0.5-mm HIR images, the median pooled score was 2 (indicating a definite preference for DLR) for noise and overall image quality and 1 (denoting a slight preference for DLR) for sharpness and natural appearance. For comparison of 0.5-mm DLR and 3.0-mm HIR, the median pooled score was 1 for the four previously mentioned measures. These assessments were all significantly different (p < .001) from 0. For artifacts, the median pooled score for both comparisons was 0, which was not significant for comparison with 3.0-mm HIR (p = .03) but was significant for comparison with 0.5-mm HIR (p < .001) due to imbalance in scores of 1 (n = 28) and -1 (slight preference for HIR, n = 1). Noise for 0.5-mm DLR was lower by mean differences of 12.8 HU compared with 0.5-mm HIR and 4.4 HU compared with 3.0-mm HIR (both p < .001). CONCLUSION. Thin-section DLR improves subjective image quality and reduces image noise compared with currently used thin- and thick-section HIR, without causing additional artifacts. CLINICAL IMPACT. Although further diagnostic performance studies are warranted, the findings suggest the possibility of replacing current use of both thin- and thick-section HIR with the use of thin-section DLR only during clinical interpretations. |
| Databáze: | OpenAIRE |
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