臺灣博碩士論文加值系統

擴散磁振影像 (diffusion Magnetic Resonance Imaging，dMRI) 對氫原子擴散運動非常敏感，因此此技術能夠以非侵入性技術探測大腦組織微結構，此影像相當有潛力能在活體中呈現其大腦白質神經纖維結構。在過去二十年裡，傳統的擴散磁振影像方法中，以擴散張量影像 (Diffusion Tensor Imaging，DTI) 使用最為廣泛，它提供一些參數去評估白質組織的生物變化。它以水分子擴散呈自由或受阻非等相性擴散 (free or hindered Gaussian anisotropic diffusion) 形式的高斯模型作為量化的基礎，然而DTI量化的擴散係數因掃描單一b值而不能反映非高斯擴散，導致其他DTI衍生的相關參數也因此都缺乏特異性。由於DTI其背後原理相較簡略，近代許多進階的擴散磁振影像方法已被提出，並且能夠探索在每單一體素內神經纖維的微結構形態學。

神經突起方向分散及密度成像 (Neurite Orientation Dispersion and Density Imaging，NODDI) 最早於2012年由Zhang等人提出，且具有臨床可行性的優點，近幾年在中樞神經系統影像學的應用相當廣泛。NODDI其技術原理是基於擴散磁振影的多物件模型，它結合了三種多物件模型分別為:透過神經突內部相當受限的非等相性非高斯擴散 (restricted non-Gaussian anisotropic diffusion) (即被神經突髓鞘內限制的空間)、外部部分受阻的非等相性高斯擴散擴散 (hindered Gaussian anisotropic diffusion) (亦指神經突起周圍空間，包括神經膠質細胞、灰質中的細胞體等)、等向性的高斯擴散 (isotropic Gaussian diffusion) (腦脊髓液中水的自由擴散)。三種組織結構的複合模型去評估每一體素中神經軸突 (白質) 和樹突 (灰質) 微結構，因此NODDI比DTI具有更高的特異性，更進一步量化神經密度及神經分散度等。

本文旨在找尋能描繪腦膠質瘤 (glioma) 週邊區域的可信擴散模型參數並且展現NODDI的臨床可行性及應用價值。在第一章中，先簡介了dMRI、DTI及NODDI之模型。在第二章到第五章中，我們利用胼胝體的特徵去驗證不同影像前處理方式對於模型參數的干擾 (第二章)，使用優化的前處理方式評估腦膠質瘤分類及描繪腫瘤週邊浸潤範圍 (第三章)，探討簡化的NODDI臨床掃描序列及建構腫瘤週邊感興趣區 (Regions Of Interest, ROI) 的自動化分割流程 (第四、五章)。最後，總結本論文結果 (第六章)。

Diffusion magnetic resonance imaging (dMRI) is a technique for the non-invasive characterization of the microstructure in biological tissues since it is sensitive for the diffusion processes of hydrogen molecules. dMRI is a promising candidate for in vivo quantification of neurite morphology in white matter. Over the past two decades, conventional dMRI method usually focused on Diffusion Tensor Imaging (DTI). DTI was widely used to assess the organization of tissue in white matter, providing some indices to describe changes in biology. The model of DTI describes the diffusive water molecules relevant to free diffusion or hindered anisotropic diffusion homogeneous within each voxel based on the assumption of Gaussian distribution. However, DTI was obtained at single b-value and lacked of specificity for describing tissues in this assumption. Additionally, several advanced dMRI techniques, especially multi-compartment models, have been proposed with complicated assumption for estimating neuron morphology.

Neurite Orientation Dispersion and Density Imaging (NODDI) is a clinically feasible technique for estimating the microstructural complexity in central neuron system imaging, post by Zhang et al. in 2012. NODDI is a multi-compartment tissue model based on dMRI, combining a three-compartment tissue model: restricted compartment for non-Gaussian anisotropic diffusion (referring to the space bounded by the membrane of neurites), hindered compartment for Gaussian anisotropic diffusion (referring to the space around the neurites) and isotropic compartment for Gaussian diffusion (referring to the CSF space) in each voxel. Using three compartments, NODDI map not only axons in the white matter but also dendrites in gray matter in each voxel. Compared to DTI indices, NODDI may provide greater specificity to morphology and pathology, e.g. neurite density and orientation dispersion.

The aims of this work are to explore the promising indices of diffusion models in characterizing the microstructural complexity in the peritumoral area of gliomas and to show the clinical feasibility and potential capability of NODDI studies. The first chapter gave an overview of dMRI and explained the models of NODDI as well as DTI (Chapter 1). In the chapters 2~5, we investigated the different preprocessing interference on NODDI and DTI as verified by topography of corpus callosum for optimization (Chapter 2), and then we differentiated different types of gliomas and characterized the infiltration in peritumoral area by NODDI and DTI using the optimized preprocessing method (Chapter 3); furthermore, we in-vivo evaluated the simplified NODDI imaging protocol and constructed the semi-automatic regions of interest (ROI) delineation for peritumoral areas (Chapter 4 and 5). Finally, the last chapter gave a conclusion of this thesis (Chapter 6).

Table of Contents
Acknowledgement ....................................................................................................... i
Chinese Abstract ...........................................................................................................ii
English Abstract ............................................................................................................iii Abbreviations ................................................................................................................. v
Table of Contents ........................................................................................................viii
List of Figures................................................................................................................xii
List of Tables ................................................................................................................ xvi
Chapter 1 Introduction .........................................................................................................................1
1.1 Diffusion Magnetic Resonance Imaging .................................................................................2
1.2 Diffusion protocol............................................................................................................................3
1.3 Diffusion Weighting Imaging and Diffusion Tensor Imaging ........................................5
1.4 The Partial Volume Effect in Diffusion Tensor Imaging................................................10
1.5 Ball and stick ...................................................................................................................................12
1.6 Combines Hindered And Restricted Models of water Diffusion ...............................13 1.7 Neurite orientation dispersion and density imaging (NODDI) .................................14
1.8 Pitfalls of echo planer imaging-based Diffusion Imaging..............................................19
1.9 Structure of this thesis ................................................................................................................21 References ...............................................................................................................................................22
Chapter 2 Preprocessing Interference for NODDI –Verified by Topography of the Human Corpus Callosum .................................................................................................................. 25
2.1 Introduction .................................................................................................................................... 26
2.1.1 Artifacts of echo-planar imaging (EPI) .............................................................................26
2.1.2 Different preprocessing steps of diffusion imaging ....................................................27
2.1.3 Role of corpus callosum ..........................................................................................................29
2.1.4 Brain lateralization ...................................................................................................................31
2.1.5 Structural alterations of corpus callosum in patients by diffusion models.......32
2.1.6 Specific aim ...................................................................................................................................33
2.2 Materials and methods................................................................................................................ 34
2.2.1 Subjects...........................................................................................................................................35
2.2.2 Edinburgh Handedness Inventory .....................................................................................35
2.2.3 MR imaging acquisition ...........................................................................................................35
2.2.4 Definition of ROI..........................................................................................................................38
2.2.5 Preprocessing...............................................................................................................................40
2.2.6 Fitting of NODDI parameter maps.......................................................................................43
2.2.7 Statistical analysis......................................................................................................................44
2.3 Results ................................................................................................................................................46
2.3.1 Preprocessing Interference for geometry of corpus callosum ...............................46 2.3.2 Preprocessing Interference for NODDI of corpus callosum ....................................49 2.3.3 Regional difference in corpus callosum ...........................................................................60
2.3.4 Correlations between parameters and scores of test.................................................64
2.4 Discussion......................................................................................................................................... 66
2.4.1 Image and parameter restoration ......................................................................................66
2.4.2 Callosal region..............................................................................................................................67
2.4.3 DTI model and further discussion about FA ..................................................................69
2.4.4 Lateralization ...............................................................................................................................69
2.4.5 Angular and orientation dispersion of fibers ................................................................70
2.4.6 Information of axonal space volume fraction.................................................................71
2.4.7 Axonal diameter: the limitation of NODDI.......................................................................72
2.4.8 Gradient strength .......................................................................................................................73
2.4.9 ROI definition...............................................................................................................................74
2.5 Conclusion ........................................................................................................................................74
References ................................................................................................................................................76
Chapter 3 Microstructural Features of Glioma by NODDI....................................................79
3.1 Introduction .....................................................................................................................................80
3.1.1 Morbidity and mortality of glioma .....................................................................................80
3.1.2 Fatal prognosis of high-grade tumor .................................................................................82
3.1.3 Characterization of diffuse astrocytic and oligodendroglial tumors ...................83
3.1.4 Presentation of glioma on conventional imaging ........................................................85
3.1.5 Symbols of cerebral tumor infiltration on imaging ....................................................87
3.1.6 Controversial DTI results in characterization of peritumoral regions ...............90
3.1.7 Components of models ............................................................................................................91
3.1.8 Specific aim ...................................................................................................................................91
3.2 Materials and methods................................................................................................................ 92
3.2.1 Patients............................................................................................................................................93
3.2.2 MR imaging acquisition ...........................................................................................................94
3.2.3 Segmentation and imaging registration ..........................................................................95
3.2.4 Definition of ROI........................................................................................................................96
3.2.5 Preprocessing and fitting of DTI parameter maps....................................................100
3.2.6 Fitting of NODDI parameter maps....................................................................................101
3.2.7 Statistical analysis...................................................................................................................101
3.3 Results ............................................................................................................................................ 102
3.3.1 Neuropathological diagnosis and ROIs defined by conventional MRI…..........102 3.3.2 Comparison of bilateral normal appearing areas of LGG / HGG..........................104
3.3.3 Comparison of bilateral normal appearing areas of IDHmut / IDHwt ............106
3.3.4 Distinguishability of LGG and HGG by NODDI and DTI parameters..................110 3.3.5 Distinguishability of IDHmut and IDHwt glioma by NODDI .................................113
3.3.6 Estimated DTI and NODDI indices of LGG and HGG..................................................116
3.3.7 ROC curve ...................................................................................................................................119
3.3.8 GB recurrence............................................................................................................................122
3.4 Discussion...................................................................................................................................... 124
3.4.1 Controversial DTI parameters derived from gliomas..............................................124
3.4.2 Differentiation of HGG and LGG ........................................................................................126
3.4.3 Comparison of NAWM in LGG / HGG groups ..............................................................127
3.4.4 Differentiation of IDHmut and IDHwt; comparison of NAWM in IDHmut / IDHwt groups .......................................................................................................................................128
3.4.5 Receiver operating characteristic (ROC) curve..........................................................129
3.4.6 Limitations of this study ....................................................................…...............................130
3.5 Conclusion .......................................................................................................….......................... 132
References ............................................................................................................................................ 133
Chapter 4 In Vivo Verification of the Simplified NODDI.................................................... 139 4.1 Introduction ................................................................................................................................. 139
4.2 Specific aim ................................................................................................................................... 140
4.3 Methods and Materials ............................................................................................................ 140
4.3.1 Subjects.........................................................................................................................................140
4.3.2 MR imaging acquisition ...............................................................….....................................140
4.3.3 Preprocessing...................................................................................……..................................141
4.3.4 Definition of ROI and imaging registration ..................................................................142
4.3.5 Statistical analysis....................................................................................................................142
4.4 Results .....................................................................................................……................................ 143
4.5 Discussion and conclusion...................................................................................................... 144 References ............................................................................................................................................ 146
Chapter 5 Semi-automatic ROI Delineation............................................................................ 147
5.1 Introduction ................................................................................................................................. 147
5.2 Specific aim ................................................................................................................................... 148
5.3 Methods and Materials .....................................................................…................................... 148
5.3.1 Patients.........................................................................................................................................148
5.3.2 MR Imaging acquisition ........................................................................................................149
5.3.3 Preprocessing............................................................................................................................149
5.3.4 Definition of ROI and imaging registration ..................................................................150
5.3.5 Statistical analysis....................................................................................................................152
5.4 Results ............................................................................................................................................ 152
5.4.1 Semi-automatic definition of ROIs....................................................................................152
5.4.2 Automatic definition of ROIs ..............................................................................................156
5.5 Discussion and conclusion...................................................................................................... 158 References ............................................................................................................................................ 160
Chapter 6 Conclusion........................................................................................................................ 161
Chapter 7 Appendix .......................................................................................................................... 165


List of Figures
Figure 1-1 Gaussian distribution ..................................................................................................... 3 Figure 1-2 The pulse sequence for double and single diffusion encoding...................... 4
Figure 1-3 DWI using different diffusion weighting .................................................................5
Figure 1-4 Simplified schematic shows derivation of ADC ...................................................7
Figure 1-5 The diffusion ellipsoid ................................................................................................... 8 Figure 1-6 Description of fractional anisotropy ........................................................................ 9 Figure 1-7 Two modes of diffusion in WM ............................................................................... 13 Figure 2-1 Coronal and sagittal view of fibers and the brain............................................ 29 Figure 2-2 Regional differences in fiber composition along the CC .............................. 30 Figure 2-3 Flow Chart of Processing of Imaging..................................................................... 34 Figure 2-4 Images of NODDI after TOPUP-EDDY ................................................................... 36 Figure 2-5 ROI definition were segmented in midline sagittal view of CC ............... 39
Figure 2-6 Image correction caused the necessary different ROIs of CC ..................... 39 Figure 2-7 FSL graphical user interfaces.................................................................................... 40 Figure 2-8 PE imaging, RPE imaging and TOPUP-EDDY correction .............................. 43 Figure 2-9 Morphological effects of preprocessing on different of ROIs ..................... 46 Figure 2-10 Hollow CC and ROI showing the correction after TOPUP ......................... 47 Figure 2-11 Geometric mismatch between diffusion and structural images............. 48 Figure 2-12 Preprocessing effects of TOPUP-EDDY and eddy_correct......................... 50 Figure 2-13 Preprocessing effects of TOPUP-EDDY_rotatedB and eddy_correct………
........................................................................................................................................................................52
Figure 2-14 Preprocessing effects of TOPUP-EDDY and TOPUP-EDDY_rotatedB……..
........................................................................................................................................................................54
Figure 2-15 Box plot of ODI results processed by three preprocessing....................... 56
Figure 2-16 Box plot of FISO results processed by three preprocessing .................... 57
Figure 2-17 Box plot of FICV results processed by three preprocessing..................... 58 Figure 2-18 Box plot of FA results processed by three preprocessing.......................... 59 Figure 2-19 NODODI map processed by TOPUP-EDDY_rotatedB…................................ 61 Figure 2-20 Trend of ODI along the midline CC....................................................................... 62 Figure 2-21 Trend of FICV along the midline CC .................................................................... 62 Figure 2-22 Trend of FISO along the midline CC..................................................................... 63 Figure 2-23 Trend of FA along the midline CC......................................................................... 63 Figure 2-24 3D topographic connection distribution of CC by connectivity.............. 68 Figure 2-25 Angular dispersion of the corpus callosum ..................................................... 71 Figure 3-1 The peritumoral region and corresponding imaging modalities for detecting imaging features .............................................................................................................. 89
Figure 3-2 Data workflow in detecting microstructural features of gliomas ............ 92 Figure 3-3 Axial morphological images and ROIs on T2WI for four cases ................. 97 Figure 3-4 Illustration of ROIs on axial T2WI of right insular astrocytoma................ 98 Figure 3-5 The location of the same ROIs on T2WI and DWI-B0 images .................... 99 Figure 3-6 Difference of mean FECV between the ipsilesional and contralateral
NAWM of LGG...................................................................................................................................... 105
Figure 3-7 Difference of mean indices between the ipsilesional and contralateral
NAWM and surNAWM of HGG ..................................................................................................... 106
Figure 3-8 Difference of mean FICV and FECV between ipsilesional and contralateral NAWM of IDHmut ................................................................................................ 108
Figure 3-9 Difference of mean FICV and FECV between ipsilesional and contralateral NAWM of IDHwt .................................................................................................... 108
Figure 3-10 Comparison of normalized FICV in WM between IDHmut and IDHwt.......................................................................................................................................................109
Figure 3-11 Comparison of normalized FECV in WM between IDHmut and IDHwt. .....................................................................................................................................................109
Figure 3-12 Statistical difference of FECV, FISO and MD between the peritumoral edema of LGG and HGG.................................................................................................................... 111
Figure 3-13 No statistic difference of LGG and HGG in NAWM...................................... 112
Figure 3-14 No statistic differenc of LGG and HGG in NAGM ......................................... 112
Figure 3-15 Satistical difference of FECV and FICV in NAWM between IDHmut and IDHwt...................................................................................................................................................... 114
Figure 3-16 No statistic differenc of indices in NAGM between IDHmut and IDHwt ..................................................................................................................................................... 115
Figure 3-17 No statistic differenc of indices in peritumoral edema between IDHmut and IDHwt ............................................................................................................................................ 115
Figure 3-18 Demonstrated cases for parameter maps of NODDI and DTI................ 116 Figure 3-19 All averged parameters estimated by in LGG group ................................. 117 Figure 3-20 All averged parameters estimated in HGG group ...................................... 118 Figure 3-21 ROC curve by FISO in the edema of LGG and HGG ..................................... 119 Figure 3-22 ROC curve by FICV in the edema of LGG and HGG...................................... 120 Figure 3-23 ROC curve by MD in the edema of LGG and HGG........................................ 120
Figure 3-24 ROC curve by FICV in the NAWM of IDHmut and IDHwt ........................ 121 Figure 3-25 ROC curve by FECV in the NAWM of IDHmut and IDHwt ...................... 121
Figure 4-1 Comparison of simplified NODDI protocol by two-sample t test .......... 143 Figure 4-2 Comparison of simplified NODDI protocol by Mann-Whitney U test....144
Figure 5-1 Illustration of ROIs of left parietal astrocytoma on axial T2WI ............. 151
Figure 5-2 Difference of LGG and HGG in the Zone_1_WM using ODI ........................ 153
Figure 5-3 Difference of LGG and HGG in the Zone_1_WM using MD ......................... 153
Figure 5-4 No statistic difference of LGG and HGG in GM ................................................ 154
Figure 5-5 Differentiation of LGG and HGG in Zone_1_GM or Zone_1_GM by normalized FECV ............................................................................................................................... 154
Figure 5-6 Significant difference of mean FECV between ipsilesional Zone_1_WM and contralateral Zone_1_WM of HGG...................................................................................... 155
Figure 5-7 Significant difference of mean MD between ipsilesional Zone_1_WM and contralateral Zone_1_WM of HGG............................................................................................... 155
Figure 5-8 Automatic and manual defined lesional area (/ROI) on T2WI................ 157








List of Tables
Table 2.1 Comparison table of scan parameters of T1WI and DWIs............................. 37 Table 2.2 Preprocessing effects of TOPUP-EDDY and eddy_correct.............................. 51 Table 2.3 Preprocessing effects of TOPUP-EDDY_rotatedB and eddy_correct”……. 53 Table 2.4 Preprocessing effects of TOPUP-EDDY and TOPUP-EDDY_rotatedB……...55 Table 2.5 Profiles of ODI (median values) ................................................................................ 56
Table 2.6 Profiles of FISO (median values)................................................................................ 57 Table 2.7 Profiles of FICV (median values) ............................................................................... 58 Table 2.8 Profiles of FA (median values) ................................................................................... 59 Table 2.9 Significant regional differences ................................................................................. 60 Table 2.10 The Edinburgh Handedness Inventory................................................................ 64
Table 2.11 Nonparametric correlations of parameters after “TOPUP-EDDY_rotatedB” ..................................................................................................................65
Table 2.12 Nonparametric correlations of parameters after TOPUP-EDDY............... 65
Table 2.13 Nonparametric correlations of parameters after eddy_correct................ 66
Table 2.14 Comparison of different models.............................................................................. 72 Table 3.1 Grading of selected gliomas from 2007 CNS WHO............................................. 83
Table 3.2 Grading of selected gliomas from 2016 CNS WHO............................................ 83
Table 3.3 Scan parameters of T1WI and DWIs....................................................................... 94
Table 3.4 Morphological presentation and pathology of patients ............................... 103
Table 3.5 Histology, immunohistochemistry and avalible ROIs ................................... 103
Table 3.6 Comparison of FICV / FECV in normal appearing tissue of ipsilesional and contralateral sites in LGG or HGG groups ............................................................................... 104
Table 3.7 Comparison of LGG and HGG with NODDI and DTI indices in flipped normal appearing tissues of contralateral hemisphere ................................................... 105
Table 3.8 Comparison of FICV and FECV in normal appearing tissues of ipsilesional and contralateral hemispheres of IDHmut / IDHwt groups........................................... 107
Table 3.9 Comparison of IDHmut and IDHwt with NODDI and DTI indices in flipped normal appearing tissues of contralateral hemisphere ................................................... 107
Table 3.10 FICV, FISO and MD indices showed significant differnce in the
peritumoral edema of LGG and HGG by Mann-Whitney U test ..................................... 110
Table 3.11 Significant differnce of FICV and FECV between IDHmut and IDHwt in NAWM.................................................................................................................................................... 113
Table 5.1 The overlapping ratio of automatic and manual ROIs................................... 156

Chapter 1- References

1. Pierpaoli, C. and P.J. Basser, Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med, 1996. 36(6): p. 893-906.
2. Le Bihan, D., et al., Artifacts and pitfalls in diffusion MRI. J Magn Reson Imaging, 2006. 24(3): p. 478-88.
3. Jones, D.K. and M. Cercignani, Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed, 2010. 23(7): p. 803-20.
4. Brown, R., A brief account of microscopical observations made in the months of June, July and August, 1827, on the particles contained in the pollen of plants; and on the general existence of active molecules in organic and inorganic bodies. . Philos Mag., 1828. 4: p. 161-173.
5. Einstein, A., Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen. Ann Phys., 1905. 17: p. 549-560.
6. Torrey, H., Bloch Equations with Diffusion Terms. Physical Review, 1956. 104(3): p. 563-565.
7. Stejskal, E.O. and J.E. Tanner, Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient. The Journal of Chemical Physics, 1965. 42: p. 288-292.
8. Daducci, A., et al., Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data. Neuroimage, 2015. 105: p. 32-44.
9. Le Bihan, D., Molecular diffusion, tissue microdynamics and microstructure. NMR Biomed, 1995. 8(7-8): p. 375-86.
10. Le Bihan, D., et al., MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology, 1986. 161(2): p. 401-7.
11. Dixon, W.T., Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging: a modest proposal with tremendous potential. Radiology, 1988. 168(2): p. 566-7.
12. Mori, S. and P.B. Barker, Diffusion magnetic resonance imaging: its principle and applications. Anat Rec, 1999. 257(3): p. 102-9.
13. Moseley, M.E., et al., Diffusion-weighted MR imaging of acute stroke: correlation with T2-weighted and magnetic susceptibility-enhanced MR imaging in cats. AJNR Am J Neuroradiol, 1990. 11(3): p. 423-9.
14. Warach, S., et al., Fast magnetic resonance diffusion-weighted imaging of acute human stroke. Neurology, 1992. 42(9): p. 1717-23.
15. Basser, P.J., J. Mattiello, and D. LeBihan, MR diffusion tensor spectroscopy and imaging. Biophys J, 1994. 66(1): p. 259-67.
16. Maier, S.E., et al., Normal brain and brain tumor: multicomponent apparent diffusion coefficient line scan imaging. Radiology, 2001. 219(3): p. 842-9.
17. Basser, P.J. and C. Pierpaoli, Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. 1996. J Magn Reson, 2011. 213(2): p. 560-70.
18. Nagy, Z., et al., Diffusion tensor imaging on teenagers, born at term with moderate hypoxic-ischemic encephalopathy. Pediatr Res, 2005. 58(5): p. 936-40.
19. Hoefnagels, F.W., et al., Differentiation of edema and glioma infiltration: proposal of a DTI-based probability map. J Neurooncol, 2014. 120(1): p. 187-98.
20. Sinha, S., et al., Diffusion tensor MR imaging of high-grade cerebral gliomas. AJNR Am J Neuroradiol, 2002. 23(4): p. 520-7.
21. Oouchi, H., et al., Diffusion anisotropy measurement of brain white matter is affected by voxel size: underestimation occurs in areas with crossing fibers. AJNR Am J Neuroradiol, 2007. 28(6): p. 1102-6.
22. Westin, C.F., et al., Processing and visualization for diffusion tensor MRI. Med Image Anal, 2002. 6(2): p. 93-108.
23. Szczepankiewicz, F., et al., Variability in diffusion kurtosis imaging: impact on study design, statistical power and interpretation. Neuroimage, 2013. 76: p. 145-54.
24. Vos, S.B., et al., Partial volume effect as a hidden covariate in DTI analyses. Neuroimage, 2011. 55(4): p. 1566-76.
25. Nilsson, M., et al., The importance of axonal undulation in diffusion MR measurements: a Monte Carlo simulation study. NMR Biomed, 2012. 25(5): p. 795-805.
26. De Santis, S., et al., Why diffusion tensor MRI does well only some of the time: variance and covariance of white matter tissue microstructure attributes in the living human brain. Neuroimage, 2014. 89: p. 35-44.
27. Vos, S.B., et al., The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain. Neuroimage, 2012. 59(3): p. 2208-16.
28. Hsu, J.L., et al., Microstructural white matter changes in normal aging: a diffusion tensor imaging study with higher-order polynomial regression models. Neuroimage, 2010. 49(1): p. 32-43.
29. Li, X.R., et al., Analysis of white matter characteristics with tract-based spatial statistics according to diffusion tensor imaging in early Parkinson's disease. Neurosci Lett, 2018. 675: p. 127-132.
30. Sternberg, E.J., M.L. Lipton, and J. Burns, Utility of diffusion tensor imaging in evaluation of the peritumoral region in patients with primary and metastatic brain tumors. AJNR Am J Neuroradiol, 2014. 35(3): p. 439-44.
31. Suh, C.H., et al., Diffusion-Weighted Imaging and Diffusion Tensor Imaging for Differentiating High-Grade Glioma from Solitary Brain Metastasis: A Systematic Review and Meta-Analysis. AJNR Am J Neuroradiol, 2018.
32. Lu, S., et al., Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol, 2003. 24(5): p. 937-41.
33. Genc, S., et al., Neurite density index is sensitive to age related differences in the developing brain. Neuroimage, 2017. 148: p. 373-380.
34. Kamagata, K., et al., Neurite orientation dispersion and density imaging in the substantia nigra in idiopathic Parkinson disease. Eur Radiol, 2016. 26(8): p. 2567-77.
35. Kamagata, K., et al., Gray Matter Abnormalities in Idiopathic Parkinson's Disease: Evaluation by Diffusional Kurtosis Imaging and Neurite Orientation Dispersion and Density Imaging. Hum Brain Mapp, 2017.
36. Maximov, II, A.S. Tonoyan, and I.N. Pronin, Differentiation of glioma malignancy grade using diffusion MRI. Phys Med, 2017. 40: p. 24-32.
37. Behrens, T.E., et al., Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med, 2003. 50(5): p. 1077-88.
38. Assaf, Y., et al., New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter. Magn Reson Med, 2004. 52(5): p. 965-78.
39. Zhang, H., et al., NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012. 61(4): p. 1000-16.


Chapter 2 - References
1. Zhang, H., et al., NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012. 61(4): p. 1000-16.
2. Kunz, N., et al., Assessing white matter microstructure of the newborn with multi-shell diffusion MRI and biophysical compartment models. Neuroimage, 2014. 96: p. 288-99.
3. Budde, M.D. and J. Annese, Quantification of anisotropy and fiber orientation in human brain histological sections. Front Integr Neurosci, 2013. 7: p. 3.
4. Le Bihan, D., et al., Artifacts and pitfalls in diffusion MRI. J Magn Reson Imaging, 2006. 24(3): p. 478-88.
5. Andersson, J.L., S. Skare, and J. Ashburner, How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage, 2003. 20(2): p. 870-88.
6. Jenkinson, M. and S. Smith, A global optimisation method for robust affine registration of brain images. Med Image Anal, 2001. 5(2): p. 143-56.
7. Oguz, I., et al., DTIPrep: quality control of diffusion-weighted images. Front Neuroinform, 2014. 8: p. 4.
8. Andersson, J.L. and S.N. Sotiropoulos, Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes. Neuroimage, 2015. 122: p. 166-76.
9. Andersson, J.L. and S.N. Sotiropoulos, An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage, 2016. 125: p. 1063-78.
10. Graham, M.S., I. Drobnjak, and H. Zhang, Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques. Neuroimage, 2016. 125: p. 1079-1094.
11. Aboitiz, F. and J. Montiel, One hundred million years of interhemispheric communication: the history of the corpus callosum. Braz J Med Biol Res, 2003. 36(4): p. 409-20.
12. Olivares, R., J. Montiel, and F. Aboitiz, Species differences and similarities in the fine structure of the mammalian corpus callosum. Brain Behav Evol, 2001. 57(2): p. 98-105.
13. Tantillo, G., et al., Corpus Callosum Diffusion and Language Lateralization in Patients with Brain Tumors: A DTI and fMRI Study. J Neuroimaging, 2016. 26(2): p. 224-31.
14. Brambilla, P., et al., Magnetic resonance imaging study of corpus callosum abnormalities in patients with bipolar disorder. Biol Psychiatry, 2003. 54(11): p. 1294-7.
15. Emsell, L., et al., Limbic and callosal white matter changes in euthymic bipolar I disorder: an advanced diffusion magnetic resonance imaging tractography study. Biol Psychiatry, 2013. 73(2): p. 194-201.
16. Luders, E., et al., Decreased callosal thickness in attention-deficit/hyperactivity disorder. Biol Psychiatry, 2009. 65(1): p. 84-8.
17. Dramsdahl, M., et al., Adults with attention-deficit/hyperactivity disorder - a diffusion-tensor imaging study of the corpus callosum. Psychiatry Res, 2012. 201(2): p. 168-73.
18. Rusch, N., et al., Corpus callosum abnormalities in women with borderline personality disorder and comorbid attention-deficit hyperactivity disorder. J Psychiatry Neurosci, 2007. 32(6): p. 417-22.
19. Assaf, Y., et al., New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter. Magn Reson Med, 2004. 52(5): p. 965-78.
20. Clark, C.A., M. Hedehus, and M.E. Moseley, In vivo mapping of the fast and slow diffusion tensors in human brain. Magn Reson Med, 2002. 47(4): p. 623-8.
21. Kochunov, P., J. Chiappelli, and L.E. Hong, Permeability-diffusivity modeling vs. fractional anisotropy on white matter integrity assessment and application in schizophrenia. Neuroimage Clin, 2013. 3: p. 18-26.
22. Assaf, Y. and P.J. Basser, Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage, 2005. 27(1): p. 48-58.
23. Assaf, Y., et al., AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn Reson Med, 2008. 59(6): p. 1347-54.
24. Alexander, D.C., et al., Orientationally invariant indices of axon diameter and density from diffusion MRI. Neuroimage, 2010. 52(4): p. 1374-89.
25. De Santis, S., D.K. Jones, and A. Roebroeck, Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter. Neuroimage, 2016. 130: p. 91-103.
26. Jelescu, I.O., et al., One diffusion acquisition and different white matter models: how does microstructure change in human early development based on WMTI and NODDI? Neuroimage, 2015. 107: p. 242-56.
27. De Santis, S., et al., T1 relaxometry of crossing fibres in the human brain. Neuroimage, 2016. 141: p. 133-42.
28. Tariq, M., et al., Bingham-NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI. Neuroimage, 2016. 133: p. 207-23.
29. Knecht, S., et al., Handedness and hemispheric language dominance in healthy humans. Brain, 2000. 123 Pt 12: p. 2512-8.
30. Oldfield, R.C., The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia, 1971. 9(1): p. 97-113.
31. Jenkinson, M., et al., FSL. Neuroimage, 2012. 62(2): p. 782-90.
32. Chao, Y.P., et al., Probabilistic topography of human corpus callosum using cytoarchitectural parcellation and high angular resolution diffusion imaging tractography. Hum Brain Mapp, 2009. 30(10): p. 3172-87.
33. Wu, Y.C., et al., Age- and gender-related changes in the normal human brain using hybrid diffusion imaging (HYDI). Neuroimage, 2011. 54(3): p. 1840-53.
34. McNab, J.A., et al., The Human Connectome Project and beyond: Initial applications of 300 mT/m gradients. NeuroImage, 2013. 80: p. 234-245.
35. Suzuki, Y., et al., Estimation of the Mean Axon Diameter and Intra-axonal Space Volume Fraction of the Human Corpus Callosum: Diffusion q-space Imaging with Low q-values. Magn Reson Med Sci, 2016. 15(1): p. 83-93.
36. McNab, J.A., et al., The Human Connectome Project and beyond: initial applications of 300 mT/m gradients. Neuroimage, 2013. 80: p. 234-45.
37. Barazany, D., P.J. Basser, and Y. Assaf, In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. Brain, 2009. 132(Pt 5): p. 1210-20.
38. Dyrby, T.B., et al., Contrast and stability of the axon diameter index from microstructure imaging with diffusion MRI. Magn Reson Med, 2013. 70(3): p. 711-21.
39. Fine, J.G., K.A. Musielak, and M. Semrud-Clikeman, Smaller splenium in children with nonverbal learning disability compared to controls, high-functioning autism and ADHD. Child Neuropsychol, 2014. 20(6): p. 641-61.
40. Collinson, S.L., et al., Corpus callosum morphology in first-episode and chronic schizophrenia: combined magnetic resonance and diffusion tensor imaging study of Chinese Singaporean patients. Br J Psychiatry, 2014. 204(1): p. 55-60.
41. Fischl, B., et al., Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 2002. 33(3): p. 341-55.
42. Yamada, H., et al., Efficacy of distortion correction on diffusion imaging: comparison of FSL eddy and eddy_correct using 30 and 60 directions diffusion encoding. PLoS One, 2014. 9(11): p. e112411.
43. Chepuri, N.B., et al., Diffusion anisotropy in the corpus callosum. AJNR Am J Neuroradiol, 2002. 23(5): p. 803-8.


Chapter 3- References
1. Zhang, H., et al., NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012. 61(4): p. 1000-16.
2. Schneider, T., et al., Sensitivity of multi-shell NODDI to multiple sclerosis white matter changes: a pilot study. Funct Neurol, 2017. 32(2): p. 97-101.
3. By, S., et al., Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients. Neuroimage Clin, 2017. 15: p. 333-342.
4. Winston, G.P., et al., Advanced diffusion imaging sequences could aid assessing patients with focal cortical dysplasia and epilepsy. Epilepsy Res, 2014. 108(2): p. 336-9.
5. Slattery, C.F., et al., ApoE influences regional white-matter axonal density loss in Alzheimer's disease. Neurobiol Aging, 2017. 57: p. 8-17.
6. Colgan, N., et al., Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer's disease. Neuroimage, 2016. 125: p. 739-44.
7. Kamagata, K., et al., Gray Matter Abnormalities in Idiopathic Parkinson's Disease: Evaluation by Diffusional Kurtosis Imaging and Neurite Orientation Dispersion and Density Imaging. Hum Brain Mapp, 2017.
8. Kamagata, K., T. Hatano, and S. Aoki, What is NODDI and what is its role in Parkinson's assessment? Expert Rev Neurother, 2016. 16(3): p. 241-3.
9. Kamagata, K., et al., Neurite orientation dispersion and density imaging in the substantia nigra in idiopathic Parkinson disease. Eur Radiol, 2016. 26(8): p. 2567-77.
10. Lemee, J.M., et al., Characterizing the peritumoral brain zone in glioblastoma: a multidisciplinary analysis. J Neurooncol, 2015. 122(1): p. 53-61.
11. Stummer, W., Mechanisms of tumor-related brain edema. Neurosurg Focus, 2007. 22(5): p. E8.
12. Kallenberg, K., et al., Abnormalities in the normal appearing white matter of the cerebral hemisphere contralateral to a malignant brain tumor detected by diffusion tensor imaging. Folia Neuropathol, 2014. 52(3): p. 226-33.
13. de Robles, P., et al., The worldwide incidence and prevalence of primary brain tumors: a systematic review and meta-analysis. Neuro Oncol, 2015. 17(6): p. 776-83.
14. Lin, Y.J., et al., Trends in the incidence of primary malignant brain tumors in Taiwan and correlation with comorbidities: A population-based study. Clin Neurol Neurosurg, 2017. 159: p. 72-82.
15. Mortazavi, S.M.J., S.A.R. Mortazavi, and M. Paknahad, Cancers of the Brain and CNS: Global Patterns and Trends in Incidence. J Biomed Phys Eng, 2018. 8(1): p. 151-152.
16. Walid, M.S., Prognostic factors for long-term survival after glioblastoma. Perm J, 2008. 12(4): p. 45-8.
17. Thakkar, J.P., et al., Epidemiologic and molecular prognostic review of glioblastoma. Cancer Epidemiol Biomarkers Prev, 2014. 23(10): p. 1985-96.
18. Petrecca, K., et al., Failure pattern following complete resection plus radiotherapy and temozolomide is at the resection margin in patients with glioblastoma. J Neurooncol, 2013. 111(1): p. 19-23.
19. Ostrom, Q.T., et al., CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2009-2013. Neuro Oncol, 2016. 18(suppl_5): p. v1-v75.
20. Emmanuel, C., et al., Long-term survival after glioblastoma resection: hope despite poor prognosis factors. J Neurosurg Sci, 2018.
21. Watkins, S., et al., Disruption of astrocyte-vascular coupling and the blood-brain barrier by invading glioma cells. Nat Commun, 2014. 5: p. 4196.
22. Jain, R.K., et al., Angiogenesis in brain tumours. Nat Rev Neurosci, 2007. 8(8): p. 610-22.
23. Oike, T., et al., Radiotherapy plus concomitant adjuvant temozolomide for glioblastoma: Japanese mono-institutional results. PLoS One, 2013. 8(11): p. e78943.
24. Tovi, M., MR imaging in cerebral gliomas analysis of tumour tissue components. Acta Radiol Suppl, 1993. 384: p. 1-24.
25. Louis, D.N., et al., The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol, 2007. 114(2): p. 97-109.
26. Louis, D.N., et al., The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol, 2016. 131(6): p. 803-20.
27. Ohgaki, H. and P. Kleihues, Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. J Neuropathol Exp Neurol, 2005. 64(6): p. 479-89.
28. Reitman, Z.J. and H. Yan, Isocitrate dehydrogenase 1 and 2 mutations in cancer: alterations at a crossroads of cellular metabolism. J Natl Cancer Inst, 2010. 102(13): p. 932-41.
29. Krell, D., et al., IDH mutations in tumorigenesis and their potential role as novel therapeutic targets. Future Oncol, 2013. 9(12): p. 1923-35.
30. Prensner, J.R. and A.M. Chinnaiyan, Metabolism unhinged: IDH mutations in cancer. Nat Med, 2011. 17(3): p. 291-3.
31. Yan, H., et al., IDH1 and IDH2 mutations in gliomas. N Engl J Med, 2009. 360(8): p. 765-73.
32. Parsons, D.W., et al., An integrated genomic analysis of human glioblastoma multiforme. Science, 2008. 321(5897): p. 1807-12.
33. Brat, D.J., et al., Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. N Engl J Med, 2015. 372(26): p. 2481-98.
34. Leung, D., et al., Role of MRI in primary brain tumor evaluation. J Natl Compr Canc Netw, 2014. 12(11): p. 1561-8.
35. Radbruch, A. and M. Bendszus, Advanced MR Imaging in Neuro-oncology. Clin Neuroradiol, 2015. 25 Suppl 2: p. 143-9.
36. Ellingson, B.M., et al., Pros and cons of current brain tumor imaging. Neuro Oncol, 2014. 16 Suppl 7: p. vii2-11.
37. Peet, A.C., et al., Functional imaging in adult and paediatric brain tumours. Nat Rev Clin Oncol, 2012. 9(12): p. 700-11.
38. Waldman, A.D., et al., Quantitative imaging biomarkers in neuro-oncology. Nat Rev Clin Oncol, 2009. 6(8): p. 445-54.
39. Kim, M.M., et al., Non-invasive metabolic imaging of brain tumours in the era of precision medicine. Nat Rev Clin Oncol, 2016. 13(12): p. 725-739.
40. Broen, M.P.G., et al., The T2-FLAIR Mismatch Sign as an Imaging Marker for Non-Enhancing IDH-mutant, 1p/19q-intact Lower Grade Glioma: A Validation Study. Neuro Oncol, 2018.
41. Sadeghi, N., 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. AJNR Am J Neuroradiol, 2008. 29(3): p. 476-82.
42. Kelly, P.J., et al., Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms. J Neurosurg, 1987. 66(6): p. 865-74.
43. DeAngelis, L.M., Brain tumors. N Engl J Med, 2001. 344(2): p. 114-23.
44. Chang, P.D., et al., Predicting Glioblastoma Recurrence by Early Changes in the Apparent Diffusion Coefficient Value and Signal Intensity on FLAIR Images. AJR Am J Roentgenol, 2017. 208(1): p. 57-65.
45. Schmainda, K.M., Diffusion-weighted MRI as a biomarker for treatment response in glioma. CNS Oncol, 2012. 1(2): p. 169-80.
46. Lope-Piedrafita, S., et al., Longitudinal diffusion tensor imaging in a rat brain glioma model. NMR Biomed, 2008. 21(8): p. 799-808.
47. Chenevert, T.L., et al., Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst, 2000. 92(24): p. 2029-36.
48. Gupta, P.K., et al., Value of Minimum Apparent Diffusion Coefficient on Magnetic Resonance Imaging as a Biomarker for Predicting Progression of Disease Following Surgery and Radiotherapy in Glial Tumors from a Tertiary Care Center in Northern India. J Neurosci Rural Pract, 2017. 8(2): p. 185-193.
49. Galla, N., et al., Apparent diffusion coefficient changes predict survival after intra-arterial bevacizumab treatment in recurrent glioblastoma. Neuroradiology, 2017. 59(5): p. 499-505.
50. Sternberg, E.J., M.L. Lipton, and J. Burns, Utility of diffusion tensor imaging in evaluation of the peritumoral region in patients with primary and metastatic brain tumors. AJNR Am J Neuroradiol, 2014. 35(3): p. 439-44.
51. Cruz Junior, L.C. and A.G. Sorensen, Diffusion tensor magnetic resonance imaging of brain tumors. Neurosurg Clin N Am, 2005. 16(1): p. 115-34.
52. van Westen, D., et al., Tumor extension in high-grade gliomas assessed with diffusion magnetic resonance imaging: values and lesion-to-brain ratios of apparent diffusion coefficient and fractional anisotropy. Acta Radiol, 2006. 47(3): p. 311-9.
53. Provenzale, J.M., et al., Peritumoral brain regions in gliomas and meningiomas: investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging. Radiology, 2004. 232(2): p. 451-60.
54. Bastin, M.E., et al., Measurements of water diffusion and T1 values in peritumoural oedematous brain. Neuroreport, 2002. 13(10): p. 1335-40.
55. Lu, S., et al., Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol, 2003. 24(5): p. 937-41.
56. Lin, C.-P., et al., Peri-tumoral Fractional Anisotropy Mapping as a Prognosticator and Treatment Guidance of Brain Tumors: A Feasibility Study. Journal of Medical and Biological Engineering, 2008. 28: p. 139-145.
57. Fornasa, F., Diffusion-weighted Magnetic Resonance Imaging: What Makes Water Run Fast or Slow? J Clin Imaging Sci, 2011. 1: p. 27.
58. Witwer, B.P., et al., Diffusion-tensor imaging of white matter tracts in patients with cerebral neoplasm. J Neurosurg, 2002. 97(3): p. 568-75.
59. Alexander, A.L., et al., Analysis of partial volume effects in diffusion-tensor MRI. Magn Reson Med, 2001. 45(5): p. 770-80.
60. Sener, R.N., Diffusion MRI: apparent diffusion coefficient (ADC) values in the normal brain and a classification of brain disorders based on ADC values. Comput Med Imaging Graph, 2001. 25(4): p. 299-326.
61. Aboitiz, F. and J. Montiel, One hundred million years of interhemispheric communication: the history of the corpus callosum. Braz J Med Biol Res, 2003. 36(4): p. 409-20.
62. Sundgren, P.C., 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(9): p. 1131-42.
63. Deng, Z., et al., Quantitative analysis of glioma cell invasion by diffusion tensor imaging. J Clin Neurosci, 2010. 17(12): p. 1530-6.
64. Hoefnagels, F.W., et al., Differentiation of edema and glioma infiltration: proposal of a DTI-based probability map. J Neurooncol, 2014. 120(1): p. 187-98.
65. Tropine, A., et al., Contribution of diffusion tensor imaging to delineation of gliomas and glioblastomas. J Magn Reson Imaging, 2004. 20(6): p. 905-12.
66. Lu, S., et al., Diffusion-tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction of the tumor infiltration index. Radiology, 2004. 232(1): p. 221-8.
67. Sinha, S., et al., Diffusion tensor MR imaging of high-grade cerebral gliomas. AJNR Am J Neuroradiol, 2002. 23(4): p. 520-7.
68. Pena, A., et al., Enhanced visualization and quantification of magnetic resonance diffusion tensor imaging using the p:q tensor decomposition. Br J Radiol, 2006. 79(938): p. 101-9.
69. Price, S.J., et al., Diffusion tensor imaging of brain tumours at 3T: a potential tool for assessing white matter tract invasion? Clin Radiol, 2003. 58(6): p. 455-62.
70. Price, S.J., et al., Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am J Neuroradiol, 2006. 27(9): p. 1969-74.
71. Wang, W., C.E. Steward, and P.M. Desmond, Diffusion tensor imaging in glioblastoma multiforme and brain metastases: the role of p, q, L, and fractional anisotropy. AJNR Am J Neuroradiol, 2009. 30(1): p. 203-8.
72. Papadopoulos, M.C., D.K. Binder, and A.S. Verkman, Enhanced macromolecular diffusion in brain extracellular space in mouse models of vasogenic edema measured by cortical surface photobleaching. Faseb j, 2005. 19(3): p. 425-7.
73. Zhang, H. and A.S. Verkman, Microfiberoptic measurement of extracellular space volume in brain and tumor slices based on fluorescent dye partitioning. Biophys J, 2010. 99(4): p. 1284-91.
74. Verkman, A.S., Diffusion in the extracellular space in brain and tumors. Phys Biol, 2013. 10(4): p. 045003.
75. Vargova, L., et al., Diffusion parameters of the extracellular space in human gliomas. Glia, 2003. 42(1): p. 77-88.
76. Caverzasi, E., et al., Neurite Orientation Dispersion and Density Imaging Color Maps to Characterize Brain Diffusion in Neurologic Disorders. J Neuroimaging, 2016. 26(5): p. 494-8.
77. Engelhorn, T., et al., Cellular characterization of the peritumoral edema zone in malignant brain tumors. Cancer Sci, 2009. 100(10): p. 1856-62.
78. Turkalp, Z., J. Karamchandani, and S. Das, IDH mutation in glioma: new insights and promises for the future. JAMA Neurol, 2014. 71(10): p. 1319-25.
79. Wen, Q., et al., Clinically feasible NODDI characterization of glioma using multiband EPI at 7 T. Neuroimage Clin, 2015. 9: p. 291-9.
80. Maximov, II, A.S. Tonoyan, and I.N. Pronin, Differentiation of glioma malignancy grade using diffusion MRI. Phys Med, 2017.


Chapter 4- References
1. Zhang, H., et al., NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012. 61(4): p. 1000-16.
2. Tariq, M., et al., Bingham-NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI. Neuroimage, 2016. 133: p. 207-223.
3. Feinberg, D.A., et al., Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One, 2010. 5(12): p. e15710.
4. Lampinen, B., et al., Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: A model comparison using spherical tensor encoding. Neuroimage, 2017. 147: p. 517-531.
5. Wen, Q., et al., Clinically feasible NODDI characterization of glioma using multiband EPI at 7 T. Neuroimage Clin, 2015. 9: p. 291-9.
6. Chung, A.W., K.K. Seunarine, and C.A. Clark, NODDI reproducibility and variability with magnetic field strength: A comparison between 1.5 T and 3 T. Hum Brain Mapp, 2016. 37(12): p. 4550-4565.


Chapter 5- References
1. Maximov, II, A.S. Tonoyan, and I.N. Pronin, Differentiation of glioma malignancy grade using diffusion MRI. Phys Med, 2017. 40: p. 24-32.
2. Sanjuan, A., et al., Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors. Front Neurosci, 2013. 7: p. 241.
3. Odland, A., et al., Volumetric glioma quantification: comparison of manual and semi-automatic tumor segmentation for the quantification of tumor growth. Acta Radiol, 2015. 56(11): p. 1396-403.
4. Kadkhodaei, M., et al., Automatic segmentation of multimodal brain tumor images based on classification of super-voxels. Conf Proc IEEE Eng Med Biol Soc, 2016. 2016: p. 5945-5948.
5. Cui, S., et al., Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network. J Healthc Eng, 2018. 2018: p. 4940593.
6. Chen, W., et al., Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics. Int J Biomed Imaging, 2018. 2018: p. 2512037.
7. Zhan, T., et al., Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge. CNS Neurol Disord Drug Targets, 2017. 16(2): p. 129-136.
8. Daducci, A., et al., Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data. Neuroimage, 2015. 105: p. 32-44.