Combination of Deep Learning–Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation

2020 ◽  
Vol 215 (6) ◽  
pp. 1321-1328
Author(s):  
Akinori Hata ◽  
Masahiro Yanagawa ◽  
Yuriko Yoshida ◽  
Tomo Miyata ◽  
Mitsuko Tsubamoto ◽  
...  
2015 ◽  
Vol 204 (6) ◽  
pp. 1197-1202 ◽  
Author(s):  
Yookyung Kim ◽  
Yoon Kyung Kim ◽  
Bo Eun Lee ◽  
Seok Jeong Lee ◽  
Yon Ju Ryu ◽  
...  

2017 ◽  
Vol 59 (5) ◽  
pp. 553-559 ◽  
Author(s):  
Yun Hye Ju ◽  
Geewon Lee ◽  
Ji Won Lee ◽  
Seung Baek Hong ◽  
Young Ju Suh ◽  
...  

Background Reducing radiation dose inevitably increases image noise, and thus, it is important in low-dose computed tomography (CT) to maintain image quality and lesion detection performance. Purpose To assess image quality and lesion conspicuity of ultra-low-dose CT with model-based iterative reconstruction (MBIR) and to determine a suitable protocol for lung screening CT. Material and Methods A total of 120 heavy smokers underwent lung screening CT and were randomly and equally assigned to one of five groups: group 1 = 120 kVp, 25 mAs, with FBP reconstruction; group 2 = 120 kVp, 10 mAs, with MBIR; group 3 = 100 kVp, 15 mAs, with MBIR; group 4 = 100 kVp, 10 mAs, with MBIR; and group 5 = 100 kVp, 5 mAs, with MBIR. Two radiologists evaluated intergroup differences with respect to radiation dose, image noise, image quality, and lesion conspicuity using the Kruskal–Wallis test and the Chi-square test. Results Effective doses were 61–87% lower in groups 2–5 than in group 1. Image noises in groups 1 and 5 were significantly higher than in the other groups ( P < 0.001). Overall image quality was best in group 1, but diagnostic acceptability of overall image qualities in groups 1–3 was not significantly different (all P values > 0.05). Lesion conspicuities were similar in groups 1–4, but were significantly poorer in group 5. Conclusion Lung screening CT with MBIR obtained at 100 kVp and 15 mAs enables a ∼60% reduction in radiation dose versus low-dose CT, while maintaining image quality and lesion conspicuity.


Author(s):  
ryoji mikayama ◽  
Takashi Shirasaka ◽  
Tsukasa Kojima ◽  
Yuki Sakai ◽  
Hidetake Yabuuchi ◽  
...  

Objectives The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting. Methods Artificial ground-glass nodules (6 mm and 10 mm diameters, −660 HU) placed at the lung-apex and the middle-lung field in chest phantom were scanned by 320-row CT with the ultra-low-dose setting of 6.3 mAs. Each scan data set was reconstructed by DLR, MBIR, and HIR. The volumes of nodules were measured semi-automatically, and the absolute percent volumetric error (APEvol) was calculated. The APEvol provided by each reconstruction were compared by the Tukey-Kramer method. Inter- and intraobserver variabilities were evaluated by a Bland-Altman analysis with limits of agreements. Results DLR provided a lower APEvol compared to MBIR and HIR. The APEvol of DLR (1.36%) was significantly lower than those of the HIR (8.01%, p = 0.0022) and MBIR (7.30%, p = 0.0053) on a 10-mm-diameter middle-lung nodule. DLR showed narrower limits of agreement compared to MBIR and HIR in the inter- and intraobserver agreement of the volumetric measurement. Conclusions DLR showed higher accuracy compared to MBIR and HIR for the volumetric measurement of artificial ground-glass nodules by ultra-low-dose CT. Advances in knowledge DLR with ultra-low-dose setting allows a reduction of dose exposure, maintaining accuracy for the volumetry of lung nodule, especially in patients which deserve a long-term follow-up.


2016 ◽  
Vol 58 (6) ◽  
pp. 702-709 ◽  
Author(s):  
Muhammed Alshamari ◽  
Mats Geijer ◽  
Eva Norrman ◽  
Mats Lidén ◽  
Wolfgang Krauss ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document