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Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction
- Source :
- 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
- Publication Year :
- 2022
-
Abstract
- 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.
- Subjects :
- 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
Subjects
Details
- ISSN :
- 15463141 and 0361803X
- Database :
- OpenAIRE
- Journal :
- AJR. American journal of roentgenology
- Accession number :
- edsair.doi.dedup.....ac85dec0103362b3e09f0b1b346f0257