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Motion correction and noise removing in lung diffusion-weighted MRI using low-rank decomposition.
Med Biol Eng Comput. 2020 Sep; 58(9):2095-2105.MB

Abstract

Lung diffusion-weighted magnetic resonance imaging (DWI) has shown a promising value in lung lesion detection, diagnosis, differentiation, and staging. However, the respiratory and cardiac motion, blood flow, and lung hysteresis may contribute to the blurring, resulting in unclear lung images. The image blurring could adversely affect diagnosis performance. The purpose of this study is to reduce the DWI blurring and assess its positive effect on diagnosis. The retrospective study includes 71 patients. In this paper, a motion correction and noise removal method using low-rank decomposition is proposed, which can reduce the DWI blurring by exploit the spatiotemporal continuity sequences. The deblurring performances are evaluated by qualitative and quantitative assessment, and the performance of diagnosis of lung cancer is measured by area under curve (AUC). In the view of the qualitative assessment, the deformation of the lung mass is reduced, and the blurring of the lung tumor edge is alleviated. Noise in the apparent diffusion coefficient (ADC) map is greatly reduced. For quantitative assessment, mutual information (MI) and Pearson correlation coefficient (Pearson-Coff) are 1.30 and 0.82 before the decomposition and 1.40 and 0.85 after the decomposition. Both the difference in MI and Pearson-Coff are statistically significant (p < 0.05). For the positive effect of deblurring on diagnosis of lung cancer, the AUC was improved from 0.731 to 0.841 using three-fold cross validation. We conclude that the low-rank matrix decomposition method is promising in reducing the errors in DWI lung images caused by noise and artifacts and improving diagnostics. Further investigations are warranted to understand the full utilities of the low-rank decomposition on lung DWI images. Graphical abstract.

Authors+Show Affiliations

School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China. hjchen@bjtu.edu.cn.Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32654016

Citation

Wang, Xinhui, et al. "Motion Correction and Noise Removing in Lung Diffusion-weighted MRI Using Low-rank Decomposition." Medical & Biological Engineering & Computing, vol. 58, no. 9, 2020, pp. 2095-2105.
Wang X, Chen H, Wan Q, et al. Motion correction and noise removing in lung diffusion-weighted MRI using low-rank decomposition. Med Biol Eng Comput. 2020;58(9):2095-2105.
Wang, X., Chen, H., Wan, Q., Li, Y., Cai, N., Li, X., & Peng, Y. (2020). Motion correction and noise removing in lung diffusion-weighted MRI using low-rank decomposition. Medical & Biological Engineering & Computing, 58(9), 2095-2105. https://doi.org/10.1007/s11517-020-02224-7
Wang X, et al. Motion Correction and Noise Removing in Lung Diffusion-weighted MRI Using Low-rank Decomposition. Med Biol Eng Comput. 2020;58(9):2095-2105. PubMed PMID: 32654016.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Motion correction and noise removing in lung diffusion-weighted MRI using low-rank decomposition. AU - Wang,Xinhui, AU - Chen,Houjin, AU - Wan,Qi, AU - Li,Yanfeng, AU - Cai,Naxin, AU - Li,Xinchun, AU - Peng,Yahui, Y1 - 2020/07/11/ PY - 2019/03/26/received PY - 2020/06/27/accepted PY - 2020/7/13/pubmed PY - 2020/7/13/medline PY - 2020/7/13/entrez KW - Diffusion magnetic resonance imaging KW - Image deblurring KW - Lung KW - Motion-correction KW - Sparse and low-rank decomposition SP - 2095 EP - 2105 JF - Medical & biological engineering & computing JO - Med Biol Eng Comput VL - 58 IS - 9 N2 - Lung diffusion-weighted magnetic resonance imaging (DWI) has shown a promising value in lung lesion detection, diagnosis, differentiation, and staging. However, the respiratory and cardiac motion, blood flow, and lung hysteresis may contribute to the blurring, resulting in unclear lung images. The image blurring could adversely affect diagnosis performance. The purpose of this study is to reduce the DWI blurring and assess its positive effect on diagnosis. The retrospective study includes 71 patients. In this paper, a motion correction and noise removal method using low-rank decomposition is proposed, which can reduce the DWI blurring by exploit the spatiotemporal continuity sequences. The deblurring performances are evaluated by qualitative and quantitative assessment, and the performance of diagnosis of lung cancer is measured by area under curve (AUC). In the view of the qualitative assessment, the deformation of the lung mass is reduced, and the blurring of the lung tumor edge is alleviated. Noise in the apparent diffusion coefficient (ADC) map is greatly reduced. For quantitative assessment, mutual information (MI) and Pearson correlation coefficient (Pearson-Coff) are 1.30 and 0.82 before the decomposition and 1.40 and 0.85 after the decomposition. Both the difference in MI and Pearson-Coff are statistically significant (p < 0.05). For the positive effect of deblurring on diagnosis of lung cancer, the AUC was improved from 0.731 to 0.841 using three-fold cross validation. We conclude that the low-rank matrix decomposition method is promising in reducing the errors in DWI lung images caused by noise and artifacts and improving diagnostics. Further investigations are warranted to understand the full utilities of the low-rank decomposition on lung DWI images. Graphical abstract. SN - 1741-0444 UR - https://www.unboundmedicine.com/medline/citation/32654016/Motion_correction_and_noise_removing_in_lung_diffusion-weighted_MRI_using_low-rank_decomposition L2 - https://dx.doi.org/10.1007/s11517-020-02224-7 DB - PRIME DP - Unbound Medicine ER -
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