Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing

被引:203
作者
Chen, Yang [1 ,2 ,3 ,4 ]
Yin, Xindao [5 ]
Shi, Luyao [1 ,2 ]
Shu, Huazhong [1 ,2 ]
Luo, Limin [1 ,2 ]
Coatrieux, Jean-Louis [1 ,2 ,3 ,4 ]
Toumoulin, Christine [2 ,3 ,4 ]
机构
[1] Southeast Univ, Lab Image Sci & Technol, Nanjing 210096, Jiangsu, Peoples R China
[2] Ctr Rech Informat Biomed Sinofrancais LIA CRIBs, Rennes, France
[3] Univ Rennes 1, LTSI, F-35042 Rennes, France
[4] INSERM, U1099, F-35042 Rennes, France
[5] Nanjing Med Univ, Nanjing Hosp, Dept Radiol, Nanjing 210096, Peoples R China
关键词
COMPUTED-TOMOGRAPHY; ABDOMINAL CT; ITERATIVE RECONSTRUCTION; NOISE-REDUCTION; IMPROVEMENT; SPARSE; REPRESENTATIONS; QUALITY; LESIONS;
D O I
10.1088/0031-9155/58/16/5803
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors.
引用
收藏
页码:5803 / 5820
页数:18
相关论文
共 35 条
[1]   Current concepts - Computed tomography - An increasing source of radiation exposure [J].
Brenner, David J. ;
Hall, Eric J. .
NEW ENGLAND JOURNAL OF MEDICINE, 2007, 357 (22) :2277-2284
[2]   Nonlocal prior Bayesian tomographic reconstruction [J].
Chen, Yang ;
Ma, Jianhua ;
Feng, Qianjin ;
Luo, Limin ;
Shi, Pengcheng ;
Chen, Wufan .
JOURNAL OF MATHEMATICAL IMAGING AND VISION, 2008, 30 (02) :133-146
[3]   Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means [J].
Chen, Yang ;
Yang, Zhou ;
Hu, Yining ;
Yang, Guanyu ;
Zhu, Yongcheng ;
Li, Yinsheng ;
Luo, Limin ;
Chen, Wufan ;
Toumoulin, Christine .
PHYSICS IN MEDICINE AND BIOLOGY, 2012, 57 (09) :2667-2688
[4]   Improving low-dose abdominal CT images by Weighted Intensity Averaging over Large-scale Neighborhoods [J].
Chen, Yang ;
Chen, Wufan ;
Yin, Xindao ;
Ye, Xianghua ;
Bao, Xudong ;
Luo, Limin ;
Feng, Qianjing ;
Li, Yinsheng ;
Yu, Xiaoe .
EUROPEAN JOURNAL OF RADIOLOGY, 2011, 80 (02) :E42-E49
[5]   Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior [J].
Chen, Yang ;
Gao, Dazhi ;
Nie, Cong ;
Luo, Limin ;
Chen, Wufan ;
Yin, Xindao ;
Lin, Yazhong .
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2009, 33 (07) :495-500
[6]   Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization [J].
Donoho, DL ;
Elad, M .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2003, 100 (05) :2197-2202
[7]   Noise Reduction to Decrease Radiation Dose and Improve Conspicuity of Hepatic Lesions at Contrast-Enhanced 80-kV Hepatic CT Using Projection Space Denoising [J].
Ehman, Eric C. ;
Guimaraes, Luis S. ;
Fidler, Jeff L. ;
Takahashi, Naoki ;
Ramirez-Giraldo, Juan Carlos ;
Yu, Lifeng ;
Manduca, Armando ;
Huprich, James E. ;
McCollough, Cynthia H. ;
Holmes, David, III ;
Harmsen, W. Scott ;
Fletcher, Joel G. .
AMERICAN JOURNAL OF ROENTGENOLOGY, 2012, 198 (02) :405-411
[8]   Image denoising via sparse and redundant representations over learned dictionaries [J].
Elad, Michael ;
Aharon, Michal .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2006, 15 (12) :3736-3745
[9]   Segmenting CT prostate images using population and patient-specific statistics for radiotherapy [J].
Feng, Qianjin ;
Foskey, Mark ;
Chen, Wufan ;
Shen, Dinggang .
MEDICAL PHYSICS, 2010, 37 (08) :4121-4132
[10]   Improvement of low-contrast detectability in low-dose hepatic multidetector computed tomography using a novel adaptive filter - Evaluation with a computer-simulated liver including tumors [J].
Funama, Y ;
Awai, K ;
Miyazaki, O ;
Nakayama, Y ;
Goto, T ;
Omi, Y ;
Shimonobo, T ;
Liu, D ;
Yamashita, Y ;
Hori, S .
INVESTIGATIVE RADIOLOGY, 2006, 41 (01) :1-7