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.
Similar content being viewed by others
Abbreviations
- MR:
-
Magnetic resonance
- MRI:
-
Magnetic resonance imaging
- DWI:
-
Diffusion-weighted imaging
- ADC:
-
Apparent diffusion coefficient
- T2WI:
-
T1-weighted imaging
- T2WI:
-
T2-weighted imaging
- CTP:
-
Computed tomography perfusion
- PET:
-
Positron emission computed tomography
- MI:
-
Mutual information
- Pearson-Coff:
-
Pearson correlation coefficient
- ROI:
-
Region of interest
- ROC:
-
Receiver operating characteristic
- AUC:
-
Area under curve
References
Huber RM (2006) Lung cancer. Internist (Berl) 47:611–620; quiz 621. https://doi.org/10.1007/s00108-006-1581-3
Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7–34. https://doi.org/10.3322/caac.21551
Cakir C, Genchellac H, Temizoz O, Polat A, Sengul E, Duygulu G (2015) Diffusion weighted magnetic resonance imaging for the characterization of solitary pulmonary lesions. Balkan Medical Journal 32:403–409. https://doi.org/10.5152/balkanmedj.2015.15663
Li B, Li Q, Chen C, Guan Y, Liu S (2014) A systematic review and meta-analysis of the accuracy of diffusion-weighted MRI in the detection of malignant pulmonary nodules and masses. Acad Radiol 21:21–29. https://doi.org/10.1016/j.acra.2013.09.019
Mori T, Nomori H, Ikeda K, Kawanaka K, Shiraishi S, Katahira K, Yamashita Y (2008) Diffusion-weighted magnetic resonance imaging for diagnosing malignant pulmonary nodules/masses: comparison with positron emission tomography. Journal of Thoracic Oncology 3:358–364. https://doi.org/10.1097/JTO.0b013e318168d9ed
Wu LM, Xu JR, Hua J, Gu HY, Chen J, Haacke EM, Hu J (2013) Can diffusion-weighted imaging be used as a reliable sequence in the detection of malignant pulmonary nodules and masses? Magn Reson Imaging 31:235–246. https://doi.org/10.1016/j.mri.2012.07.009
Tsuchiya N, Doai M, Usuda K, Uramoto H, Tonami H (2017) Non-small cell lung cancer: whole-lesion histogram analysis of the apparent diffusion coefficient for assessment of tumor grade, lymphovascular invasion and pleural invasion. PLoS One 12:e0172433. https://doi.org/10.1371/journal.pone.0172433
Cai RF, Cui L, Yin JB, Jiang JQ, Liu J (2018) Value of LSR and ADCs in differential diagnosis of hilar and mediastinal lymph nodes in lung cancer. Zhonghua Yi Xue Za Zhi 98:3009–3013. https://doi.org/10.3760/cma.j.issn.0376-2491.2018.37.012
Wan Q, Deng YS, Lei Q, Bao YY, Wang YZ, Zhou JX, Zou Q, Li XC (2019) Differentiating between malignant and benign solid solitary pulmonary lesions: are intravoxel incoherent motion and diffusion kurtosis imaging superior to conventional diffusion-weighted imaging? Eur Radiol 29:1607–1615. https://doi.org/10.1007/s00330-018-5714-6
Zhou SC, Wang YJ, Ai T, Huang L, Zhu TT, Zhu WZ, Xia LM (2019) Diagnosis of solitary pulmonary lesions with intravoxel incoherent motion diffusion-weighted MRI and semi-quantitative dynamic contrast-enhanced MRI. Clinical Radiology 74:409 e407–409 e416. https://doi.org/10.1016/j.crad.2018.12.022
Guan HX, Pan YY, Wang YJ, Tang DZ, Zhou SC, Xia LM (2018) Comparison of various parameters of DWI in distinguishing solitary pulmonary nodules. Current Medical Science 38:920–924. https://doi.org/10.1007/s11596-018-1963-5
Gou S, Wang Y, Wu J, Lee P, Sheng K (2015) Lung dynamic MRI deblurring using low-rank decomposition and dictionary learning. Med Phys 42:1917–1925. https://doi.org/10.1118/1.4915543
Hao L, Huang Y, Gao Y, Chen X, Wang P (2017) Nonrigid registration of prostate diffusion-weighted MRI. Journal of Healthcare Engineering 2017:9296354–9296312. https://doi.org/10.1155/2017/9296354
Marchesseau S, Totman JJ, Fadil H, Leek FAA, Chaal J, Richards M, Chan M, Reilhac A (2019) Cardiac motion and spillover correction for quantitative PET imaging using dynamic MRI. Med Phys 46:726–737. https://doi.org/10.1002/mp.13345
Bush MA, Ahmad R, Jin N, Liu Y, Simonetti OP (2019) Patient specific prospective respiratory motion correction for efficient, free-breathing cardiovascular MRI. Magn Reson Med 81:3662–3674. https://doi.org/10.1002/mrm.27681
Chu LL, Knebel RJ, Shay AD, Santos J, Badawi RD, Gandara DR, Knollmann FD (2018) CT perfusion imaging of lung cancer: benefit of motion correction for blood flow estimates. Eur Radiol 28:5069–5075. https://doi.org/10.1007/s00330-018-5492-1
Novillo F, Van Eyndhoven S, Moeyersons J, Bogaert J, Claessen G, La Gerche A, Van Huffel S, Claus P (2019) Unsupervised respiratory signal extraction from ungated cardiac magnetic resonance imaging at rest and during exercise. Phys Med Biol 64:065001. https://doi.org/10.1088/1361-6560/ab02cd
Johansson A, Balter JM, Cao Y (2018) Abdominal DCE-MRI reconstruction with deformable motion correction for liver perfusion quantification. Med Phys 45:4529–4540. https://doi.org/10.1002/mp.13118
Dong W, Zhang L, Shi G, Wu X (2011) Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans Image Process 20:1838–1857. https://doi.org/10.1109/TIP.2011.2108306
Zhou R, Yang Y, Mathew RC, Mugler JP 3rd, Weller DS, Kramer CM, Ahmed AH, Jacob M, Salerno M (2019) Free-breathing cine imaging with motion-corrected reconstruction at 3T using SPiral Acquisition with Respiratory correction and Cardiac Self-gating (SPARCS). Magn Reson Med 82:706–720. https://doi.org/10.1002/mrm.27763
Qiu W, Li D, Jin X, Liu F, Nguyen TD, Prince MR, Wang Y, Spincemaille P (2019) Sliding motion compensated low-rank plus sparse (SMC-LS) reconstruction for high spatiotemporal free-breathing liver 4D DCE-MRI. Magn Reson Imaging 58:56–66. https://doi.org/10.1016/j.mri.2019.01.012
Tianyi Zhou DT (2011) GoDec: randomized low-rank & sparse matrix decomposition in noisy case. Paper presented at the international conference on machine learning. Bellevue, WA
Sarma M, Hu P, Rapacchi S, Ennis D, Thomas A, Lee P, Kupelian P, Sheng K (2014) Accelerating dynamic magnetic resonance imaging (MRI) for lung tumor tracking based on low-rank decomposition in the spatial-temporal domain: a feasibility study based on simulation and preliminary prospective undersampled MRI. Int J Radiat Oncol Biol Phys 88:723–731. https://doi.org/10.1016/j.ijrobp.2013.11.217
G. H G, C. F VL (1996). In: matrix computations. Johns Hopkins University Press, p 470
Matoba M, Tonami H, Kondou T, Yokota H, Higashi K, Toga H, Sakuma T (2007) Lung carcinoma: diffusion-weighted MR imaging--preliminary evaluation with apparent diffusion coefficient. Radiology 243:570–577. https://doi.org/10.1148/radiol.2432060131
Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise Reduction in Speech Processing. Springer Topics in Signal Processing. pp 1–4. doi:https://doi.org/10.1007/978-3-642-00296-0_5
Schober P, Boer C, Schwarte LA (2018) Correlation coefficients: appropriate use and interpretation. Anesth Analg 126:1763–1768. https://doi.org/10.1213/ANE.0000000000002864
Kraskov A, Stogbauer H, Grassberger P (2004) Estimating mutual information. Phys Rev E Stat Nonlinear Soft Matter Phys 69:066138. https://doi.org/10.1103/PhysRevE.69.066138
King AP, Buerger C, Tsoumpas C, Marsden PK, Schaeffter T (2012) Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator. Med Image Anal 16:252–264. https://doi.org/10.1016/j.media.2011.08.003
Manber R, Thielemans K, Hutton BF, Barnes A, Ourselin S, Arridge S, O'Meara C, Wan S, Atkinson D (2015) Practical PET respiratory motion correction in clinical PET/MR. Journal of Nuclear Medicine 56:890–896. https://doi.org/10.2967/jnumed.114.151779
Kolbitsch C, Neji R, Fenchel M, Mallia A, Marsden P, Schaeffter T (2018) Respiratory-resolved MR-based attenuation correction for motion-compensated cardiac PET-MR. Phys Med Biol 63:135008. https://doi.org/10.1088/1361-6560/aaca15
Pathak R, Tian J, Thacker NA, Morris DM, Ragheb H, Saunders C, Saunders M, Jackson A (2019) Considering tumour volume for motion corrected DWI of colorectal liver metastases increases sensitivity of ADC to detect treatment-induced changes. Sci Rep 9:3828. https://doi.org/10.1038/s41598-019-40565-y
Funding
This study has received funding by National Natural Science Foundation of China (61771039, 61872030 and 61571036).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(MP4 121 kb)
Rights and permissions
About this article
Cite this article
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 58, 2095–2105 (2020). https://doi.org/10.1007/s11517-020-02224-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-020-02224-7