169 related articles for article (PubMed ID: 34079706)
1. Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules.
Ye K; Chen M; Zhu Q; Lu Y; Yuan H
Quant Imaging Med Surg; 2021 Jun; 11(6):2344-2353. PubMed ID: 34079706
[TBL] [Abstract][Full Text] [Related]
2. Ultra-low-dose CT reconstructed with ASiR-V using SmartmA for pulmonary nodule detection and Lung-RADS classifications compared with low-dose CT.
Ye K; Chen M; Li J; Zhu Q; Lu Y; Yuan H
Clin Radiol; 2021 Feb; 76(2):156.e1-156.e8. PubMed ID: 33293025
[TBL] [Abstract][Full Text] [Related]
3. A feasibility study of pulmonary nodule detection by ultralow-dose CT with adaptive statistical iterative reconstruction-V technique.
Ye K; Zhu Q; Li M; Lu Y; Yuan H
Eur J Radiol; 2019 Oct; 119():108652. PubMed ID: 31521879
[TBL] [Abstract][Full Text] [Related]
4. Reproducibility of radiomic features of pulmonary nodules between low-dose CT and conventional-dose CT.
Gao Y; Hua M; Lv J; Ma Y; Liu Y; Ren M; Tian Y; Li X; Zhang H
Quant Imaging Med Surg; 2022 Apr; 12(4):2368-2377. PubMed ID: 35371962
[TBL] [Abstract][Full Text] [Related]
5. Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.
Yoo SJ; Park YS; Choi H; Kim DS; Goo JM; Yoon SH
PLoS One; 2024; 19(2):e0297390. PubMed ID: 38386632
[TBL] [Abstract][Full Text] [Related]
6. Adaptive Statistical Iterative Reconstruction-Applied Ultra-Low-Dose CT with Radiography-Comparable Radiation Dose: Usefulness for Lung Nodule Detection.
Yoon HJ; Chung MJ; Hwang HS; Moon JW; Lee KS
Korean J Radiol; 2015; 16(5):1132-41. PubMed ID: 26357505
[TBL] [Abstract][Full Text] [Related]
7. [The influence of CT acquisition and reconstruction parameters on the stability of radiomic features of pure ground-glass nodules].
Yang SX; Wu N; Zhang L; Li M
Zhonghua Zhong Liu Za Zhi; 2022 Sep; 44(9):981-986. PubMed ID: 36164701
[No Abstract] [Full Text] [Related]
8. Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: Comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis.
Nagatani Y; Takahashi M; Murata K; Ikeda M; Yamashiro T; Miyara T; Koyama H; Koyama M; Sato Y; Moriya H; Noma S; Tomiyama N; Ohno Y; Murayama S;
Eur J Radiol; 2015 Jul; 84(7):1401-12. PubMed ID: 25892051
[TBL] [Abstract][Full Text] [Related]
9. Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules.
Liu J; Xu H; Qing H; Li Y; Yang X; He C; Ren J; Zhou P
Front Oncol; 2020; 10():634298. PubMed ID: 33604303
[TBL] [Abstract][Full Text] [Related]
10. Model-based iterative reconstruction technique for ultralow-dose computed tomography of the lung: a pilot study.
Yamada Y; Jinzaki M; Tanami Y; Shiomi E; Sugiura H; Abe T; Kuribayashi S
Invest Radiol; 2012 Aug; 47(8):482-9. PubMed ID: 22766910
[TBL] [Abstract][Full Text] [Related]
11. Clinical value of a new generation adaptive statistical iterative reconstruction (ASIR-V) in the diagnosis of pulmonary nodule in low-dose chest CT.
Tang H; Liu Z; Hu Z; He T; Li D; Yu N; Jia Y; Shi H
Br J Radiol; 2019 Nov; 92(1103):20180909. PubMed ID: 31469289
[TBL] [Abstract][Full Text] [Related]
12. Value of deep learning reconstruction at ultra-low-dose CT for evaluation of urolithiasis.
Zhang G; Zhang X; Xu L; Bai X; Jin R; Xu M; Yan J; Jin Z; Sun H
Eur Radiol; 2022 Sep; 32(9):5954-5963. PubMed ID: 35357541
[TBL] [Abstract][Full Text] [Related]
13. Model-based iterative reconstruction and adaptive statistical iterative reconstruction: dose-reduced CT for detecting pancreatic calcification.
Yasaka K; Katsura M; Akahane M; Sato J; Matsuda I; Ohtomo K
Acta Radiol Open; 2016 Jan; 5(1):2058460116628340. PubMed ID: 27110389
[TBL] [Abstract][Full Text] [Related]
14. Evaluation of ultralow-dose computed tomography on detection of pulmonary nodules in overweight or obese adult patients.
Guo X; Jia D; He L; Jia X; Zhang D; Dou Y; Shen S; Ji H; Zhang S; Chen Y
J Appl Clin Med Phys; 2022 Apr; 23(4):e13589. PubMed ID: 35293673
[TBL] [Abstract][Full Text] [Related]
15. Improving Image Quality and Nodule Characterization in Ultra-low-dose Lung CT with Deep Learning Image Reconstruction.
Ma G; Dou Y; Dang S; Yu N; Guo Y; Han D; Fan Q
Acad Radiol; 2024 Feb; ():. PubMed ID: 38429189
[TBL] [Abstract][Full Text] [Related]
16. Emphysema Quantification Using Ultralow-Dose CT With Iterative Reconstruction and Filtered Back Projection.
Nishio M; Koyama H; Ohno Y; Negi N; Seki S; Yoshikawa T; Sugimura K
AJR Am J Roentgenol; 2016 Jun; 206(6):1184-92. PubMed ID: 27058307
[TBL] [Abstract][Full Text] [Related]
17. Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses.
Choe J; Lee SM; Do KH; Lee G; Lee JG; Lee SM; Seo JB
Radiology; 2019 Aug; 292(2):365-373. PubMed ID: 31210613
[TBL] [Abstract][Full Text] [Related]
18. Ultralow-dose CT with knowledge-based iterative model reconstruction (IMR) in evaluation of pulmonary tuberculosis: comparison of radiation dose and image quality.
Yan C; Liang C; Xu J; Wu Y; Xiong W; Zheng H; Xu Y
Eur Radiol; 2019 Oct; 29(10):5358-5366. PubMed ID: 30927099
[TBL] [Abstract][Full Text] [Related]
19. Application of computer-aided detection (CAD) software to automatically detect nodules under SDCT and LDCT scans with different parameters.
Hu Q; Chen C; Kang S; Sun Z; Wang Y; Xiang M; Guan H; Xia L; Wang S
Comput Biol Med; 2022 Jul; 146():105538. PubMed ID: 35751192
[TBL] [Abstract][Full Text] [Related]
20. Lung cancer screening with ultra-low dose CT using full iterative reconstruction.
Fujita M; Higaki T; Awaya Y; Nakanishi T; Nakamura Y; Tatsugami F; Baba Y; Iida M; Awai K
Jpn J Radiol; 2017 Apr; 35(4):179-189. PubMed ID: 28197820
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
