Quantitative Analysis of Emphysema in Ultra-high-resolution CT by Using Deep Learning Reconstruction: Comparison with Hybrid Iterative Reconstruction
Author:
Affiliation:
1. Department of Radiology, Ohara General Hospital
2. Health Sciences, Tohoku University Graduate School of Medicine
Publisher
Japanese Society of Radiological Technology
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/jjrt/76/11/76_2020_JSRT_76.11.1163/_pdf
Reference62 articles.
1. 1) Pauwels RA, Buist AS, Calverley PM, et al. Global Strategy for the Diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO Global Initiative for Chronic Obstructive Lung Disease (GOLD) Workshop summary. Am J Respir Crit Care Med 2001; 163 (5): 1256-1276.
2. 2) Nakano Y, Muro S, Sakai H, et al. Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function. Am J Respir Crit Care Med 2000; 162(3 Pt 1): 1102-1108.
3. 3) Mishima M, Hirai T, Itoh H, et al. Complexity of terminal airspace geometry assessed by lung computed tomography in normal subjects and patients with chronic obstructive pulmonary disease. Proc Natl Acad Sci U S A 1999; 96(16): 8829- 8834.
4. 4) Mets OM, de Jong PA, van Ginneken B, et al. Quantitative computed tomography in COPD: possibilities and limitations. Lung 2012; 190(2): 133-145.
5. 5) Boedeker KL, McNitt-Gray MF, Rogers SR, et al. Emphysema: effect of reconstruction algorithm on CT imaging measures. Radiology 2004; 232(1): 295-301.
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