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Masjoodi, S, Hashemi, H, Oghabian, M A, Sharifi, G. (1397). Differentiation of Edematous, Tumoral and Normal Areas of Brain Using Diffusion Tensor and Neurite Orientation Dispersion and Density Imaging. سامانه مدیریت نشریات علمی, 8(3), 251-260. doi: 10.31661/jbpe.v0i0.874
S Masjoodi; H Hashemi; M A Oghabian; G Sharifi. "Differentiation of Edematous, Tumoral and Normal Areas of Brain Using Diffusion Tensor and Neurite Orientation Dispersion and Density Imaging". سامانه مدیریت نشریات علمی, 8, 3, 1397, 251-260. doi: 10.31661/jbpe.v0i0.874
Masjoodi, S, Hashemi, H, Oghabian, M A, Sharifi, G. (1397). 'Differentiation of Edematous, Tumoral and Normal Areas of Brain Using Diffusion Tensor and Neurite Orientation Dispersion and Density Imaging', سامانه مدیریت نشریات علمی, 8(3), pp. 251-260. doi: 10.31661/jbpe.v0i0.874
Masjoodi, S, Hashemi, H, Oghabian, M A, Sharifi, G. Differentiation of Edematous, Tumoral and Normal Areas of Brain Using Diffusion Tensor and Neurite Orientation Dispersion and Density Imaging. سامانه مدیریت نشریات علمی, 1397; 8(3): 251-260. doi: 10.31661/jbpe.v0i0.874

Differentiation of Edematous, Tumoral and Normal Areas of Brain Using Diffusion Tensor and Neurite Orientation Dispersion and Density Imaging

Journal of Biomedical Physics and Engineering
مقاله 4، دوره 8، شماره 3، آذر 2018، صفحه 251-260    اصل مقاله (1.09 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.874
نویسندگان
S Masjoodi1؛ H Hashemi2؛ M A Oghabian* 1، 3؛ G Sharifi4
1Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
2Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences (TUMS), Tehran, Iran
3Research Center for Science and Technology in Medicine, Imam Khomeini Hospital Complex, Tehran, Iran
4osurgery, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: Presurigical planning for glioma tumor resection and radiotherapy treatment require proper delineation of tumoral and peritumoral areas of brain. Diffusion tensor imaging (DTI) is the most common mathematical model applied for diffusion weighted MRI data. Neurite orientation dispersion and density imaging (NODDI) is another mathematical model for DWI data modeling.Objective: We studied whether extracted parameters of DTI, and NODDI models can be used to differentiate between edematous, tumoral, and normal areas in brain white matter (WM).Material and Methods: 12 patients with peritumoral edema underwent 3T multi-shell diffusion imaging with b-values of 1000 and 2000 smm-2 in 30 and 64 gradient directions, respectively. We fitted DTI and NODDI to data in manually drawn regions of interest and used their derived parameters to characterize edematous, tumoral and normal brain areas.Results: We found that DTI parameters fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) all significantly differentiated edematous from contralateral normal brain WM (p<0.005). However, only FA was found to distinguish between edematous WM fibers and tumor invaded fibers (p = 0.001). Among NODDI parameters, the intracellular volume fraction (ficvf) had the best distinguishing power with (p = 0.001) compared with the isotropic volume fraction (fiso), the orientation dispersion index (odi), and the concentration parameter of Watson distribution (κ), while comparing fibers inside normal, tumoral, and edematous areas.Conclusion: The combination of two diffusion based methods, i.e. DTI and NODDI parameters can distinguish and characterize WM fibers involved in edematus, tumoral, and normal brain areas with reasonable confidence. Further studies will be required to improve the detectability of WM fibers inside the solid tumor if they hypothetically exist in tumoral parenchyma.
کلیدواژه‌ها
MRI؛ NODDI؛ Diffusion tensor imaging؛ Edema؛ Brain Tumor
مراجع
  1. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986;161:401-7. doi: 10.1148/radiology.161.2.3763909. PubMed PMID: 3763909.
  2. Lenglet C, Deriche R, Faugeras O, editors. Inferring white matter geometry from diffusion tensor MRI: Application to connectivity mapping. European Conference on Computer Vision; 2004: Springer. P.127-140.
  3. Ramirez-Manzanares A, Rivera M, Vemuri BC, Carney P, Mareci T. Diffusion basis functions decomposition for estimating white matter intravoxel fiber geometry. IEEE Trans Med Imaging. 2007;26:1091-102. doi: 10.1109/TMI.2007.900461. PubMed PMID: 17695129.
  4. Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, et al. The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol. 2001;22:1081-8. PubMed PMID: 11415902.
  5. Sundgren PC, Dong Q, Gomez-Hassan D, Mukherji SK, Maly P, Welsh R. Diffusion tensor imaging of the brain: review of clinical applications. Neuroradiology. 2004;46:339-50. doi: 10.1007/s00234-003-1114-x. PubMed PMID: 15103435.
  6. Sundgren PC, Fan X, Weybright P, Welsh RC, Carlos RC, Petrou M, et al. Differentiation of recurrent brain tumor versus radiation injury using diffusion tensor imaging in patients with new contrast-enhancing lesions. Magn Reson Imaging. 2006;24:1131-42. doi: 10.1016/j.mri.2006.07.008. PubMed PMID: 17071335.
  7. Chenevert TL, Sundgren PC, Ross BD. Diffusion imaging: insight to cell status and cytoarchitecture. Neuroimaging Clin N Am. 2006;16:619-32. doi: 10.1016/j.nic.2006.06.005. PubMed PMID: 17148023.
  8. White NS, McDonald CR, Farid N, Kuperman JM, Kesari S, Dale AM. Improved conspicuity and delineation of high-grade primary and metastatic brain tumors using “restriction spectrum imaging”: quantitative comparison with high B-value DWI and ADC. AJNR Am J Neuroradiol. 2013;34:958-64, S1. doi: 10.3174/ajnr.A3327. PubMed PMID: 23139079; PubMed Central PMCID: PMC4146398.
  9. Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging. 1999;9:53-60. PubMed PMID: 10030650.
  10. Maier SE, Sun Y, Mulkern RV. Diffusion imaging of brain tumors. NMR Biomed. 2010;23:849-64. doi: 10.1002/nbm.1544. PubMed PMID: 20886568; PubMed Central PMCID: PMC3000221.
  11. Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A. In vivo fiber tractography using DT-MRI data. Magn Reson Med. 2000;44:625-32. PubMed PMID: 11025519.
  12. Pujol S, Wells W, Pierpaoli C, Brun C, Gee J, Cheng G, et al. The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery. J Neuroimaging. 2015;25:875-82. doi: 10.1111/jon.12283. PubMed PMID: 26259925; PubMed Central PMCID: PMC4641305.
  13. Stadlbauer A, Nimsky C, Buslei R, Salomonowitz E, Hammen T, Buchfelder M, et al. Diffusion tensor imaging and optimized fiber tracking in glioma patients: Histopathologic evaluation of tumor-invaded white matter structures. Neuroimage. 2007;34:949-56. doi: 10.1016/j.neuroimage.2006.08.051. PubMed PMID: 17166744.
  14. Dimou S, Battisti RA, Hermens DF, Lagopoulos J. A systematic review of functional magnetic resonance imaging and diffusion tensor imaging modalities used in presurgical planning of brain tumour resection. Neurosurg Rev. 2013;36:205-14. doi: 10.1007/s10143-012-0436-8. PubMed PMID: 23187966.
  15. Chen Z, Tie Y, Olubiyi O, Zhang F, Mehrtash A, Rigolo L, et al. Corticospinal tract modeling for neurosurgical planning by tracking through regions of peritumoral edema and crossing fibers using two-tensor unscented Kalman filter tractography. Int J Comput Assist Radiol Surg. 2016;11:1475-86. doi: 10.1007/s11548-015-1344-5. PubMed PMID: 26762104; PubMed Central PMCID: PMC4942409.
  16. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012;61:1000-16. doi: 10.1016/j.neuroimage.2012.03.072. PubMed PMID: 22484410.
  17. Colgan N, Siow B, O’Callaghan JM, Harrison IF, Wells JA, Holmes HE, et al. Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer’s disease. Neuroimage. 2016;125:739-44. doi: 10.1016/j.neuroimage.2015.10.043. PubMed PMID: 26505297; PubMed Central PMCID: PMC4692518.
  18. Kamagata K, Hatano T, Aoki S. What is NODDI and what is its role in Parkinson’s assessment? United Kingdom: Taylor & Francis; 2016.
  19. Jellison BJ, Field AS, Medow J, Lazar M, Salamat MS, Alexander AL. Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. AJNR Am J Neuroradiol. 2004;25:356-69. PubMed PMID: 15037456.
  20. Klatzo I. Presidental address. Neuropathological aspects of brain edema. J Neuropathol Exp Neurol. 1967;26:1-14. PubMed PMID: 5336776.
  21. Kuroiwa T, Ueki M, Chen Q, Suemasu H, Taniguchi I, Okeda R. Biomechanical characteristics of brain edema: the difference between vasogenic-type and cytotoxic-type edema. Acta Neurochir Suppl (Wien). 1994;60:158-61. PubMed PMID: 7976533.
  22. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. Fsl. Neuroimage. 2012;62:782-90. doi: 10.1016/j.neuroimage.2011.09.015. PubMed PMID: 21979382.
  23. Coupé P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging. 2008;27:425-41. doi: 10.1109/tmi.2007.906087.
  24. Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, et al. Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform. 2014;8:8. doi: 10.3389/fninf.2014.00008. PubMed PMID: 24600385; PubMed Central PMCID: PMC3931231.
  25. Pierpaoli C, Jones D, editors. Removing CSF contamination in brain DT-MRIs by using a two-compartment tensor model. Proc International Society for Magnetic Resonance in Medicine 12th Scientific meeting ISMRM04; 2004.
  26. Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage. 2005;27:48-58. doi: 10.1016/j.neuroimage.2005.03.042. PubMed PMID: 15979342.
  27. Assaf Y, Blumenfeld-Katzir T, Yovel Y, Basser PJ. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn Reson Med. 2008;59:1347-54. doi: 10.1002/mrm.21577. PubMed PMID: 18506799; PubMed Central PMCID: PMC4667732.
  28. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31:1116-28. doi: 10.1016/j.neuroimage.2006.01.015. PubMed PMID: 16545965.
  29. Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. Journal of the American statistical Association. 1952;47:583-621. doi: 10.1080/01621459.1952.10483441.
  30. Min Z-g, Niu C, Rana N, Ji H-m, Zhang M. Differentiation of pure vasogenic edema and tumor-infiltrated edema in patients with peritumoral edema by analyzing the relationship of axial and radial diffusivities on 3.0 T MRI. Clin Neurol Neurosurg. 2013;115:1366-70. doi: 10.1016/j.clineuro.2012.12.031.
  31. Kuroiwa T, Ueki M, Chen Q, Ichinose S, Okeda R. Is the swelling in brain edema isotropic or anisotropic? Acta Neurochir Suppl (Wien). 1994;60:155-7. PubMed PMID: 7976532.
  32. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53:1432-40. doi: 10.1002/mrm.20508. PubMed PMID: 15906300.
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