Texture-based quantification of pulmonary emphysema on high-resolution computed tomography: Comparison with density-based quantification and correlation with pulmonary function test

被引:102
作者
Park, Yang Shin [1 ,2 ]
Seo, Joon Beom [1 ,2 ]
Kim, Namkug [1 ,2 ]
Chae, Eun Jin [1 ,2 ]
Oh, Yeon Mok [3 ]
Do Lee, Sang [3 ]
Lee, Youngjoo [4 ]
Kang, Suk-Ho [4 ]
机构
[1] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Radiol, Seoul 138736, South Korea
[2] Univ Ulsan, Coll Med, Asan Med Ctr, Res Inst Radiol, Seoul 138736, South Korea
[3] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Internal Med, Seoul, South Korea
[4] Seoul Natl Univ, Dept Ind Engn, Seoul, South Korea
关键词
emphysema; computed tomography; quantitative; texture;
D O I
10.1097/RLI.0b013e31816901c7
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 [临床医学]; 100207 [影像医学与核医学]; 1009 [特种医学];
摘要
Purpose: To develop a system for texture-based quantification of emphysema on high-resolution computed tomography (HRCT) and to compare it with density-based quantification in correlation with pulmonary function test (PFT). Materials and Methods: Two hundred sixty-one circular regions of interest (ROI) with 16-pixel diameter [66 ROIs representing typical area of normal lung; 69 representing bronchiolitis obliterans (130); 64, mild emphysema (ME); and 62, severe emphysema (SE)] were used to train the automated classification system based on the Support Vector Machine classifier and on variable texture and shape features. An automated quantification system was developed with a moving ROI in the lung area, which classified each pixel into 4 categories. To validate the system, the HRCT and standard-kernel-reconstructed volumetric CT data of 39 consecutive patients with emphysema were included. Using this system, the whole lung area was evaluated, and the area fractions of each class were calculated (normal lung%, BO%, ME%, SE%, respectively). The emphysema index (EI) of texture-based quantification was defined as follows: (0.3 x ME% + SE%) (TEI). EIs from density-based quantification with a threshold of -950 Hounsfield Units, were measured on both HRCT (DEI_HR_213) and on volumetric CT (DEI_standard_3D). The agreement between TEI, DEI_HR_21), and DEI_standard_3D was assessed using interclass correlation coefficients (ICC). Correlation of the results on the TEI with the PFT results was compared with the results of the DEI_standard_31) and the DEI_HR_2D with Spearman's correlation test. To evaluate the contribution of each texture-based quantification lesion (BO%, ME%, SE%) on PFT, multiple linear regression analysis was performed. Results: The calculated TEI (19.71% +/- 17.98%) was well correlated with the DEI_standard_3D (19.42% +/- 14.30%) (ICC = 0.95), whereas the ICC with DEI_HR_2D (37.22% +/- 9.42%) was 0.43. TEI showed better correlation with PFT than DEI_standard_3D or DEI_HR_2D did [R = 0.71 vs. 0.67 vs. 0.61 for forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC); 0.54 vs. 0.50 vs. 0.43 for diffusing capacity (DLco), respectively]. Multiple linear regression analysis revealed that the BO% and SE% areas were independent determinants of FEV1/FVC, whereas the ME% and the SE% were determinants of DLco. Conclusion: Texture-based quantification of emphysema using an automated system showed better correlation with the PFT results than density-based quantification. Separate quantification of the BO, ME, and SE areas showed a different contribution of each component to the FEV1/FVC and the DLco. The proposed system can be successfully used for detailed regional and global evaluation of lung lesions on HRCT scanning for emphysema.
引用
收藏
页码:395 / 402
页数:8
相关论文
共 33 条
[2]
[Anonymous], 1995, AM J RESP CRIT CARE, V152, P2185
[3]
Relationship between extent of pulmonary emphysema by high-resolution computed tomography and lung elastic recoil in patients with chronic obstructive pulmonary disease [J].
Baldi, S ;
Miniati, M ;
Bellina, CR ;
Battolla, L ;
Catapano, G ;
Begliomini, E ;
Giustini, D ;
Giuntini, C .
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2001, 164 (04) :585-589
[4]
BERGIN C, 1986, AM REV RESPIR DIS, V133, P541
[5]
Emphysema: Effect of reconstruction algorithm on CT imaging measures [J].
Boedeker, KL ;
McNitt-Gray, MF ;
Rogers, SR ;
Truong, DA ;
Brown, MS ;
Gjertson, DW ;
Goldin, JG .
RADIOLOGY, 2004, 232 (01) :295-301
[6]
Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401
[7]
A tutorial on Support Vector Machines for pattern recognition [J].
Burges, CJC .
DATA MINING AND KNOWLEDGE DISCOVERY, 1998, 2 (02) :121-167
[8]
BURGES CJC, 1997, IMPROVING OVERALL AC
[9]
Obstructive lung diseases: Texture classification for differentiation at CT [J].
Chabat, F ;
Yang, GZ ;
Hansell, DM .
RADIOLOGY, 2003, 228 (03) :871-877
[10]
Dumais S, 1998, IEEE INTELL SYST APP, V13, P21