Evaluation of Abdominal CT Obtained Using a Deep Learning-Based Image Reconstruction Engine Compared with CT Using Adaptive Statistical Iterative Reconstructionopen access
- Authors
- Yoo, Yeo Jin; Choi, In Young; Yeom, Suk Keu; Cha, Sang Hoon; Jung, Yunsub; Han, Hyun Jong; Shim, Euddeum
- Issue Date
- 2022
- Publisher
- UBIQUITY PRESS LTD
- Keywords
- deep learning-based image reconstruction; computed tomography; image quality
- Citation
- JOURNAL OF THE BELGIAN SOCIETY OF RADIOLOGY, v.106, no.1
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF THE BELGIAN SOCIETY OF RADIOLOGY
- Volume
- 106
- Number
- 1
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/141161
- DOI
- 10.5334/jbsr.2638
- ISSN
- 2514-8281
- Abstract
- Purpose: To compare the image quality of CT obtained using a deep learning-based image reconstruction (DLIR) engine with images with adaptive statistical iterative reconstruction-V (AV). Materials and Methods: Using a phantom, the noise power spectrum (NPS) and taskbased transfer function (TTF) were measured in images with different reconstructions (filtered back projection [FBP], AV30, 50, 100, DLIR-L, M, H) at multiple doses. One hundred and twenty abdominal CTs with 30% dose reduction were processed using AV30, AV50, DLIR-L, M, H. Objective and subjective analyses were performed. Results: The NPS peak of DLIR was lower than that of AV30 or AV50. Compared with AV30, the NPS average spatial frequencies were higher with DLIR-L or DLIR-M. For lower contrast objects, TTF in images with DLIR were higher than those with AV. The standard deviation in DLIR-H and DLIR-M was significantly lower than AV30 and AV50. The overall image quality was the best for DLIR-M (p < 0.001). Conclusions: DLIR showed improved image quality and decreased noise under a decreased radiation dose.
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Collections - College of Medicine > Department of Medical Science > 1. Journal Articles
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