Open Access

Using of Laplacian Re-decomposition image fusion algorithm for glioma grading with SWI, ADC, and FLAIR images


Cite

1. Goodenberger ML, Jenkins RB. Genetics of adult glioma. Cancer Genet. 2012;205(12):613-621. https://doi.org/10.1016/j.cancergen.2012.10.00910.1016/j.cancergen.2012.10.009 Search in Google Scholar

2. Sasaki S, Tomomasa R, Nobusawa S, et al. Anaplastic pleomorphic xanthoastrocytoma associated with an H3G34 mutation: a case report with review of literature. Brain Tumor Pathol. 2019;36(4):169-173. https://doi.org/10.1007/s10014-019-00349-810.1007/s10014-019-00349-8 Search in Google Scholar

3. Hakyemez B, Erdogan C, Ercan I, Ergin N, Uysal S, Atahan S. High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol. 2005;60(4):493-502. https://doi.org/10.1016/j.crad.2004.09.00910.1016/j.crad.2004.09.009 Search in Google Scholar

4. Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131(6):803-820. https://doi.org/10.1007/s00401-016-1545-110.1007/s00401-016-1545-1 Search in Google Scholar

5. Law M, Oh S, Babb JS. Low-grade gliomas: Dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging-prediction of patient clinical rewsponse (Radiology (2006) 238,(658-667)). Radiology. 2008;246(3):989. https://doi.org/10.1148/radiol.238204218010.1148/radiol.2382042180 Search in Google Scholar

6. Arvinda HR, Kesavadas C, Sarma PS, et al. RETRACTED ARTICLE: Glioma grading: sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging. J Neurooncol. 2009;94(1):87-96. https://doi.org/10.1007/s11060-009-9807-610.1007/s11060-009-9807-6 Search in Google Scholar

7. Hsu CC, Watkins TW, Kwan GNC, Haacke EM. Susceptibility-weighted imaging of glioma: update on current imaging status and future directions. J Neuroimaging. 2016;26(4):383-390. https://doi.org/10.1111/jon.1236010.1111/jon.12360 Search in Google Scholar

8. Ryu YJ, Choi SH, Park SJ, Yun TJ, Kim J-H, Sohn C-H. Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity. PLoS One. 2014;9(9):e108335. https://doi.org/10.1371/journal.pone.010833510.1371/journal.pone.0108335 Search in Google Scholar

9. Santarosa C, Castellano A, Conte GM, et al. Dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging for glioma grading: preliminary comparison of vessel compartment and permeability parameters using hotspot and histogram analysis. Eur J Radiol. 2016;85(6):1147-1156. https://doi.org/10.1016/j.ejrad.2016.03.02010.1016/j.ejrad.2016.03.020 Search in Google Scholar

10. Jain KK, Sahoo P, Tyagi R, et al. Prospective glioma grading using single-dose dynamic contrast-enhanced perfusion MRI. Clin Radiol. 2015;70(10):1128-1135. https://doi.org/10.1016/j.crad.2015.06.07610.1016/j.crad.2015.06.076 Search in Google Scholar

11. Kim HS, Kim SY. A prospective study on the added value of pulsed arterial spin-labeling and apparent diffusion coefficients in the grading of gliomas. Am J Neuroradiol. 2007;28(9):1693-1699. https://doi.org/10.3174/ajnr.A067410.3174/ajnr.A0674 Search in Google Scholar

12. Wang Q, Zhang H, Zhang J, et al. The diagnostic performance of magnetic resonance spectroscopy in differentiating high-from low-grade gliomas: a systematic review and meta-analysis. Eur Radiol. 2016;26(8):2670-2684. https://doi.org/10.1007/s00330-015-4046-z10.1007/s00330-015-4046-z Search in Google Scholar

13. Schenck JF. The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. Med Phys. 1996;23(6):815-850. https://doi.org/10.1118/1.59785410.1118/1.597854 Search in Google Scholar

14. Mittal S, Wu Z, Neelavalli J, Haacke EM. Susceptibility-weighted imaging: technical aspects and clinical applications, part 2. Am J Neuroradiol. 2009;30(2):232-252. https://doi.org/10.3174/ajnr.A146110.3174/ajnr.A1461 Search in Google Scholar

15. Sehgal V, Delproposto Z, Haddar D, et al. Susceptibility-weighted imaging to visualize blood products and improve tumor contrast in the study of brain masses. J Magn Reson Imaging An Off J Int Soc Magn Reson Med. 2006;24(1):41-51. https://doi.org/10.1002/jmri.2059810.1002/jmri.20598 Search in Google Scholar

16. Li C, Ai B, Li Y, Qi H, Wu L. Susceptibility-weighted imaging in grading brain astrocytomas. Eur J Radiol. 2010;75(1):e81-e85. https://doi.org/10.1016/j.ejrad.2009.08.00310.1016/j.ejrad.2009.08.003 Search in Google Scholar

17. Ding Y, Xing Z, Liu B, Lin X, Cao D. Differentiation of primary central nervous system lymphoma from high-grade glioma and brain metastases using susceptibility-weighted imaging. Brain Behav. 2014;4(6):841-849. https://doi.org/10.1002/brb3.28810.1002/brb3.288 Search in Google Scholar

18. Minati L, Węglarz WP. Physical foundations, models, and methods of diffusion magnetic resonance imaging of the brain: A review. Concepts Magn Reson Part A An Educ J. 2007;30(5):278-307. https://doi.org/10.1002/cmr.a.2009410.1002/cmr.a.20094 Search in Google Scholar

19. Wang Q, Lei D, Yuan Y, Xiong N. Accuracy of ADC derived from DWI for differentiating high-grade from low-grade gliomas: Systematic review and meta-analysis. Medicine (Baltimore). 2020;99(8). https://doi.org/10.1097/MD.000000000001925410.1097/MD.0000000000019254 Search in Google Scholar

20. Soliman RK, Essa AA, Elhakeem AAS, Gamal SA, Zaitoun MMA. Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue. Diagn Interv Imaging. 2021;102(5):287-295. https://doi.org/10.1016/j.diii.2020.12.00110.1016/j.diii.2020.12.001 Search in Google Scholar

21. Phuttharak W, Thammaroj J, Wara-Asawapati S, Panpeng K. Grading Gliomas Capability: Comparison between Visual Assessment and Apparent Diffusion Coefficient (ADC) Value Measurement on Diffusion-Weighted Imaging (DWI). Asian Pacific J Cancer Prev APJCP. 2020;21(2):385. https://doi.org/10.31557/APJCP.2020.21.2.38510.31557/APJCP.2020.21.2.385 Search in Google Scholar

22. Sadeghi N, D’haene N, Decaestecker C, et al. Apparent diffusion coefficient and cerebral blood volume in brain gliomas: relation to tumor cell density and tumor microvessel density based on stereotactic biopsies. Am J Neuroradiol. 2008;29(3):476-482. https://doi.org/10.3174/ajnr.A085110.3174/ajnr.A0851 Search in Google Scholar

23. Ma X, Lv K, Sheng J, et al. Application evaluation of DCE-MRI combined with quantitative analysis of DWI for the diagnosis of prostate cancer. Oncol Lett. 2019;17(3):3077-3084. https://doi.org/10.3892/ol.2019.998810.3892/ol.2019.9988 Search in Google Scholar

24. Hilario A, Ramos A, Perez-Nunez A, et al. The added value of apparent diffusion coefficient to cerebral blood volume in the preoperative grading of diffuse gliomas. Am J Neuroradiol. 2012;33(4):701-707. https://doi.org/10.3174/ajnr.A284610.3174/ajnr.A2846 Search in Google Scholar

25. Saini J, Gupta PK, Sahoo P, et al. Differentiation of grade II/III and grade IV glioma by combining “T1 contrast-enhanced brain perfusion imaging” and susceptibility-weighted quantitative imaging. Neuroradiology. 2018;60(1):43-50. https://doi.org/10.1007/s00234-017-1942-810.1007/s00234-017-1942-8 Search in Google Scholar

26. Qi G, Wang J, Zhang Q, Zeng F, Zhu Z. An integrated dictionary-learning entropy-based medical image fusion framework. Futur Internet. 2017;9(4):61. https://doi.org/10.3390/fi904006110.3390/fi9040061 Search in Google Scholar

27. Wang K, Qi G, Zhu Z, Chai Y. A novel geometric dictionary construction approach for sparse representation based image fusion. Entropy. 2017;19(7):306. https://doi.org/10.3390/e1907030610.3390/e19070306 Search in Google Scholar

28. Zhu Z, Chai Y, Yin H, Li Y, Liu Z. A novel dictionary learning approach for multi-modality medical image fusion. Neurocomputing. 2016;214:471-482. https://doi.org/10.1016/j.neucom.2016.06.03610.1016/j.neucom.2016.06.036 Search in Google Scholar

29. Zhu Z, Yin H, Chai Y, Li Y, Qi G. A novel multi-modality image fusion method based on image decomposition and sparse representation. Inf Sci (Ny). 2018;432:516-529. https://doi.org/10.1016/j.ins.2017.09.01010.1016/j.ins.2017.09.010 Search in Google Scholar

30. Li X, Guo X, Han P, Wang X, Li H, Luo T. Laplacian redecomposition for multimodal medical image fusion. IEEE Trans Instrum Meas. 2020;69(9):6880-6890. https://doi.org/10.1109/TIM.2020.297540510.1109/TIM.2020.2975405 Search in Google Scholar

31. Das M, Gupta D, Radeva P, Bakde AM. NSST domain CT-MR neurological image fusion using optimised biologically inspired neural network. IET Image Process. 2020;14(16):4291-4305. https://doi.org/10.1049/iet-ipr.2020.021910.1049/iet-ipr.2020.0219 Search in Google Scholar

32. Wang G, Li W, Huang Y. Medical image fusion based on hybrid three-layer decomposition model and nuclear norm. Comput Biol Med. 2021;129:104179. https://doi.org/10.1016/j.compbiomed.2020.10417910.1016/j.compbiomed.2020.104179 Search in Google Scholar

33. Pouratian N, Asthagiri A, Jagannathan J, Shaffrey ME, Schiff D. Surgery Insight: the role of surgery in the management of low-grade gliomas. Nat Clin Pract Neurol. 2007;3(11):628-639. https://doi.org/10.1038/ncpneuro063410.1038/ncpneuro0634 Search in Google Scholar

34. Upadhyay N, Waldman A. Conventional MRI evaluation of gliomas. Br J Radiol. 2011;84(special_issue_2):S107-S111. https://doi.org/10.1259/bjr/6571181010.1259/bjr/65711810 Search in Google Scholar

35. Al-Agha M, Abushab K, Quffa K, Al-Agha S, Alajerami Y, Tabash M. Efficiency of High and Standard b Value Diffusion-Weighted Magnetic Resonance Imaging in Grading of Gliomas. J Oncol. 2020;2020. https://doi.org/10.1155/2020/694240610.1155/2020/6942406 Search in Google Scholar

36. Zhang L, Min Z, Tang M, Chen S, Lei X, Zhang X. The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas: evidence from a meta-analysis. J Neurol Sci. 2017;373:9-15. https://doi.org/10.1016/j.jns.2016.12.00810.1016/j.jns.2016.12.008 Search in Google Scholar

37. Thust SC, Hassanein S, Bisdas S, et al. Apparent diffusion coefficient for molecular subtyping of non-gadolinium-enhancing WHO grade II/III glioma: volumetric segmentation versus two-dimensional region of interest analysis. Eur Radiol. 2018;28(9):3779-3788. https://doi.org/10.1007/s00330-018-5351-010.1007/s00330-018-5351-0 Search in Google Scholar

38. Li X, Zhu Y, Kang H, et al. Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging. Cancer Imaging. 2015;15(1):1-9. https://doi.org/10.1186/s40644-015-0039-z10.1186/s40644-015-0039-z Search in Google Scholar

39. Gaudino S, Marziali G, Pezzullo G, et al. Role of susceptibility-weighted imaging and intratumoral susceptibility signals in grading and differentiating pediatric brain tumors at 1.5 T: a preliminary study. Neuroradiology. 2020;62(6):705-713. https://doi.org/10.1007/s00234-020-02386-z10.1007/s00234-020-02386-z Search in Google Scholar

40. Mohammed W, Xunning H, Haibin S, Jingzhi M. Clinical applications of susceptibility-weighted imaging in detecting and grading intracranial gliomas: a review. Cancer Imaging. 2013;13(2):186. https://doi.org/10.1102/1470-7330.2013.002010.1102/1470-7330.2013.0020 Search in Google Scholar

eISSN:
1898-0309
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Medicine, Biomedical Engineering, Physics, Technical and Applied Physics, Medical Physics