Abstract
Current clinical studies involve multidimensional high-resolution images containing an overwhelming amount of structural and functional information. The analysis of such a wealth of information is becoming increasingly difficult yet necessary in order to improve diagnosis, treatment and healthcare. Voxel-wise analysis is a class of modern methods of image processing in the medical field with increased popularity. It has replaced manual region of interest (ROI) analysis and has provided tools to make statistical inferences at voxel level. The introduction of voxel-based analysis software in all modern commercial scanners allows clinical use of these techniques. This review will explain the main principles, advantages and disadvantages behind these methods of image analysis.
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Dammann F (2002) Image processing in radiology. Rofo 174:541–550
Tofts P (ed) (2003) Quantitative MRI of the brain: measuring changes caused by disease, 1st edn. Wiley, West Sussex
Iavindrasana J, Cohen G, Depeursinge A et al (2009) Clinical data mining: a review. Yearb Med Inform 2009:121–133
Reiner BI, Siegel EL (2009) The clinical imperative of medical imaging informatics. J Digit Imaging 22:345–347
Evidence-Based Radiology Working Group (2001) Evidence-based radiology: a new approach to the practice of radiology. Radiology 220:566–575
Thrall JH (2004) Personalized medicine. Radiology 231:613–616
Boone JM (2007) Radiological interpretation 2020: toward quantitative image assessment. Med Phys 34:4173–4179
Sullivan DC (2008) Imaging as a quantitative science. Radiology 248:328–332
Dhawan AP, Huang HK, Kim DS (eds) (2008) Principles and advanced methods in medical imaging and image analysis, 1st edn. World Scientific, Singapore
Seeram E (2004) Digital image processing. Radiol Technol 75:435–452, quiz 453–435
Xydis V, Astrakas L, Drougia A et al (2006) Myelination process in preterm subjects with periventricular leucomalacia assessed by magnetization transfer ratio. Pediatr Radiol 36:934–939
Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315–337
Jain R, Kasturi R, Schunck BG (1995) Introduction to machine vision, 2nd edn. McGraw Hill, New York
Gonzalez RC, Woods RE (2001) Digital Image Processing. Prentice Hall, New Jersey
Bankman IN (ed) (2000) Handbook of medical imaging: processing and analysis management. Academic, San Diego
Olabarriaga SD, Smeulders AW (2001) Interaction in the segmentation of medical images: a survey. Med Image Anal 5:127–142
Young R, Babb J, Law M et al (2007) Comparison of region-of-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas. J Magn Reson Imaging 26:1053–1063
Law M, Young R, Babb J et al (2007) Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR 28:761–766
Dehmeshki J, Ruto AC, Arridge S et al (2001) Analysis of MTR histograms in multiple sclerosis using principal components and multiple discriminant analysis. Magn Reson Med 46:600–609
Nusbaum AO, Tang CY, Buchsbaum MS et al (2001) Regional and global changes in cerebral diffusion with normal aging. AJNR 22:136–142
Yamamoto A, Miki Y, Adachi S et al (2006) Whole brain magnetization transfer histogram analysis of pediatric acute lymphoblastic leukemia patients receiving intrathecal methotrexate therapy. Eur J Radiol 57:423–427
Argyropoulou MI, Zikou AK, Tzovara I et al (2007) Non-arteritic anterior ischaemic optic neuropathy: evaluation of the brain and optic pathway by conventional MRI and magnetisation transfer imaging. Eur Radiol 17:1669–1674
Mori N, Miki Y, Fushimi Y et al (2008) Cerebral infarction associated with moyamoya disease: histogram-based quantitative analysis of diffusion tensor imaging—a preliminary study. Magn Reson Imaging 26:835–840
Iannucci G, Tortorella C, Rovaris M et al (2000) Prognostic value of MR and magnetization transfer imaging findings in patients with clinically isolated syndromes suggestive of multiple sclerosis at presentation. AJNR 21:1034–1038
Frackowiak RSJ, Friston KJ, Frith C et al (eds) (2003) Human brain function, 2nd edn. Academic, San Diego
Henson R, Büchel C, Josephs O et al (1999) The slice-timing problem in event-related fMRI. NeuroImage 9:S125
Van de Moortele PF, Cerf B, Lobel E et al (1997) Latencies in fMRI time-series: effect of slice acquisition order and perception. NMR Biomed 10:230–236
Van de Moortele PF, Poline J-B, Paradis A-L et al (1998) Slice-dependent time shift efficiently corrected by interpolation in multi-slice EPI fMRI series. NeuroImage 7:S607
Friston KJ, Fletcher P, Josephs O et al (1998) Event-related fMRI: characterizing differential responses. Neuroimage 7:30–40
Behrenbruch CP, Petroudi S, Bond S et al (2004) Image filtering techniques for medical image post-processing: an overview. Br J Radiol 77(Spec No 2):S126–S132
D’ Agostino RB (ed) (2004) Tutorials in biostatistics volume 2. Statistical modelling of complex medical data. Wiley, West Sussex
Friston KJ (2005) Models of brain function in neuroimaging. Annu Rev Psychol 56:57–87
Friston K, Ashburner J, Kiebel S et al (eds) (2006) Statistical parametric mapping. The analysis of functional brain images, 1st edn. Academic, San Diego
Friston KJ, Holmes AP, Poline JB et al (1995) Analysis of fMRI time-series revisited. Neuroimage 2:45–53
Carlin JB, Doyle LW (2001) Statistics for clinicians: 4: basic concepts of statistical reasoning: hypothesis tests and the t-test. J Paediatr Child Health 37:72–77
Matthews DE, Farewell VT (2007) Using and understanding medical statistics, 4th edn. Karger, Basel
Perneger TV (1998) What’s wrong with Bonferroni adjustments. Bmj 316:1236–1238
Genovese CR, Lazar NA, Nichols T (2002) Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15:870–878
Worsley KJ, Evans AC, Marrett S et al (1992) A three-dimensional statistical analysis for CBF activation studies in human brain. J Cereb Blood Flow Metab 12:900–918
Worsley KJ, Marrett S, Neelin P et al (1996) A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping 4:58–73
Deneux T, Faugeras O (2006) Using nonlinear models in fMRI data analysis: model selection and activation detection. Neuroimage 32:1669–1689
Friston KJ, Mechelli A, Turner R et al (2000) Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. Neuroimage 12:466–477
Friston KJ, Glaser DE, Henson RN et al (2002) Classical and Bayesian inference in neuroimaging: applications. Neuroimage 16:484–512
Friston KJ, Penny W, Phillips C et al (2002) Classical and Bayesian inference in neuroimaging: theory. Neuroimage 16:465–483
Groves AR, Chappell MA, Woolrich MW (2009) Combined spatial and non-spatial prior for inference on MRI time-series. Neuroimage 45:795–809
Woolrich MW, Jbabdi S, Patenaude B et al (2009) Bayesian analysis of neuroimaging data in FSL. Neuroimage 45:S173–S186
Lukic AS, Wernick MN, Tzikas DG et al (2007) Bayesian kernel methods for analysis of functional neuroimages. IEEE Trans Med Imaging 26:1613–1624
Holden M (2008) A review of geometric transformations for nonrigid body registration. IEEE Trans Med Imaging 27:111–128
McInerney T, Terzopoulos D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1:91–108
Davatzikos C (1996) Spatial normalization of 3D brain images using deformable models. J Comput Assist Tomogr 20:656–665
Thompson P, Toga AW (1996) A surface-based technique for warping three-dimensional images of the brain. IEEE Trans Med Imaging 15:402–417
Sandor S, Leahy R (1997) Surface-based labeling of cortical anatomy using a deformable atlas. IEEE Trans Med Imaging 16:41–54
Pluim JP, Maintz JB, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22:986–1004
Studholme C, Constable RT, Duncan JS (2000) Accurate alignment of functional EPI data to anatomical MRI using a physics-based distortion model. IEEE Trans Med Imaging 19:1115–1127
Thevenaz P, Unser M (2000) Optimization of mutual information for multiresolution image registration. IEEE Trans Image Process 9:2083–2099
Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system: an approach to medical cerebral imaging. Thieme Medical Publishers Inc, New York
Mazziotta J, Toga A, Evans A et al (2001) A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 356:1293–1322
Good CD, Johnsrude I, Ashburner J et al (2001) Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains. Neuroimage 14:685–700
Burgund ED, Kang HC, Kelly JE et al (2002) The feasibility of a common stereotactic space for children and adults in fMRI studies of development. Neuroimage 17:184–200
Muzik O, Chugani DC, Juhasz C et al (2000) Statistical parametric mapping: assessment of application in children. Neuroimage 12:538–549
Hoeksma MR, Kenemans JL, Kemner C et al (2005) Variability in spatial normalization of pediatric and adult brain images. Clin Neurophysiol 116:1188–1194
Wilke M, Schmithorst VJ, Holland SK (2002) Assessment of spatial normalization of whole-brain magnetic resonance images in children. Hum Brain Mapp 17:48–60
Yoon U, Fonov VS, Perusse D et al (2009) The effect of template choice on morphometric analysis of pediatric brain data. Neuroimage 45:769–777
Wilke M, Schmithorst VJ, Holland SK (2003) Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data. Magn Reson Med 50:749–757
Wilke M, Holland SK, Altaye M et al (2008) Template-O-Matic: a toolbox for creating customized pediatric templates. Neuroimage 41:903–913
Altaye M, Holland SK, Wilke M et al (2008) Infant brain probability templates for MRI segmentation and normalization. Neuroimage 43:721–730
Prastawa M, Gilmore JH, Lin W et al (2005) Automatic segmentation of MR images of the developing newborn brain. Med Image Anal 9:457–466
Weisenfeld NI, Mewes AUJ, Warfield SK (2006) Segmentation of newborn brain MRI. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro 1:776–769
Song Z, Awate SP, Licht DJ et al (2007) Clinical neonatal brain MRI segmentation using adaptive nonparametric data models and intensity-based Markov priors. Med Image Comput Comput Assist Interv 10:883–890
Kazemi K, Moghaddam HA, Grebe R et al (2007) A neonatal atlas template for spatial normalization of whole-brain magnetic resonance images of newborns: preliminary results. Neuroimage 37:463–473
Kazemi K, Ghadimi S, Abrishami-Moghaddam H et al (2008) Neonatal probabilistic models for brain, CSF and skull using T1-MRI data: preliminary results. Conf Proc IEEE Eng Med Biol Soc 2008:3892–3895
Mazziotta JC, Toga AW, Evans A et al (1995) A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping (ICBM). Neuroimage 2:89–101
Diedrichsen J, Balsters JH, Flavell J et al (2009) A probabilistic MR atlas of the human cerebellum. Neuroimage 46:39–46
Shattuck DW, Mirza M, Adisetiyo V et al (2008) Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39:1064–1080
Toga AW, Mazziotta JC (1995) Brain mapping: the methods, 2nd edn. Academic, San Diego
Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11:805–821
Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15:1–25
Holmes AP, Blair RC, Watson JD et al (1996) Nonparametric analysis of statistic images from functional mapping experiments. J Cereb Blood Flow Metab 16:7–22
Friston KJ, Frith CD, Liddle PF et al (1990) The relationship between global and local changes in PET scans. J Cereb Blood Flow Metab 10:458–466
O’Shaughnessy ES, Berl MM, Moore EN et al (2008) Pediatric functional magnetic resonance imaging (fMRI): issues and applications. J Child Neurol 23:791–801
Kocak M (2009) Advanced imaging in paediatric neuroradiology. Pediatr Radiol 39:S456–S463
Mannerkoski MK, Heiskala HJ, Van Leemput K et al (2009) Subjects with intellectual disability and familial need for full-time special education show regional brain alterations: a voxel-based morphometry study. Pediatr Res 66:306–311
de Jonge RC, Swart JF, Koomen I et al (2008) No structural cerebral differences between children with a history of bacterial meningitis and healthy siblings. Acta Paediatr 97:1390–1396
Guimaraes CA, Bonilha L, Franzon RC et al (2007) Distribution of regional gray matter abnormalities in a pediatric population with temporal lobe epilepsy and correlation with neuropsychological performance. Epilepsy Behav 11:558–566
Carmona S, Bassas N, Rovira M et al (2007) Pediatric OCD structural brain deficits in conflict monitoring circuits: a voxel-based morphometry study. Neurosci Lett 421:218–223
Ment LR, Hirtz D, Huppi PS (2009) Imaging biomarkers of outcome in the developing preterm brain. Lancet Neurol 8:1042–1055
Counsell SJ, Boardman JP (2005) Differential brain growth in the infant born preterm: current knowledge and future developments from brain imaging. Semin Fetal Neonatal Med 10:403–410
Tzarouchi LC, Astrakas LG, Xydis V et al (2009) Age-related grey matter changes in preterm infants: an MRI study. Neuroimage 47:1148–1153
Pell GS, Briellmann RS, Waites AB et al (2004) Voxel-based relaxometry: a new approach for analysis of T2 relaxometry changes in epilepsy. Neuroimage 21:707–713
Snook L, Plewes C, Beaulieu C (2007) Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment. Neuroimage 34:243–252
Lee JE, Chung MK, Lazar M et al (2009) A study of diffusion tensor imaging by tissue-specific, smoothing-compensated voxel-based analysis. Neuroimage 44:870–883
Komatsu H, Nagamitsu S, Ozono S et al (2009) Regional cerebral blood flow changes in early-onset anorexia nervosa before and after weight gain. Brain Dev Oct 27 [Epub ahead of print]
Casanova R, Srikanth R, Baer A et al (2007) Biological parametric mapping: a statistical toolbox for multimodality brain image analysis. Neuroimage 34:137–143
Chen K, Reiman EM, Huan Z et al (2009) Linking functional and structural brain images with multivariate network analyses: a novel application of the partial least square method. Neuroimage 47:602–610
Tzarouchi LC, Astrakas LG, Kontsiotis S et al (2009) Voxel-based morphometry and voxel-based relaxometry in Parkinsonian variant of multiple system atrophy. J Neuroimaging Jan 29 [Epub ahead of print]
Hugenschmidt CE, Peiffer AM, Kraft RA et al (2008) Relating imaging indices of white matter integrity and volume in healthy older adults. Cereb Cortex 18:433–442
Bartres-Faz D, Sole-Padulles C, Junque C et al (2009) Interactions of cognitive reserve with regional brain anatomy and brain function during a working memory task in healthy elders. Biol Psychol 80:256–259
Somorjai RL (2002) Exploratory data analysis in functional neuroimaging. Artif Intell Med 25:1–3
Petersson KM, Nichols TE, Poline JB et al (1999) Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models. Philos Trans R Soc Lond B Biol Sci 354:1239–1260
Calhoun VD, Liu J, Adali T (2009) A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage 45:S163–S172
Sommer FT, Wichert A (eds) (2003) Exploratory analysis and data modeling in functional neuroimaging. MIT Press, Cambridge
Ngan SC, Yacoub ES, Auffermann WF et al (2002) Node merging in Kohonen’s self-organizing mapping of fMRI data. Artif Intell Med 25:19–33
Ardila A, Bernal B (2007) What can be localized in the brain? Toward a “factor” theory on brain organization of cognition. Int J Neurosci 117:935–969
Papadakis NG, Zheng Y, Wilkinson ID (2003) Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis. Phys Med Biol 48:N343–N350
Guo WY, Wu YT, Wu HM et al (2004) Toward normal perfusion after radiosurgery: perfusion MR Imaging with independent component analysis of brain arteriovenous malformations. AJNR 25:1636–1644
Bookstein FL (2001) “Voxel-based morphometry” should not be used with imperfectly registered images. Neuroimage 14:1454–1462
Ashburner J, Friston KJ (2001) Why voxel-based morphometry should be used. Neuroimage 14:1238–1243
Davatzikos C (2004) Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. Neuroimage 23:17–20
Costafreda SG, David AS, Brammer MJ (2009) A parametric approach to voxel-based meta-analysis. Neuroimage 46:115–122
Turkeltaub PE, Eden GF, Jones KM et al (2002) Meta-analysis of the functional neuroanatomy of single-word reading: method and validation. Neuroimage 16:765–780
Laird AR, Eickhoff SB, Kurth F et al (2009) ALE meta-analysis workflows via the brainmap database: progress towards a probabilistic functional brain atlas. Front Neuroinformatics 3:23
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Astrakas, L.G., Argyropoulou, M.I. Shifting from region of interest (ROI) to voxel-based analysis in human brain mapping. Pediatr Radiol 40, 1857–1867 (2010). https://doi.org/10.1007/s00247-010-1677-8
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DOI: https://doi.org/10.1007/s00247-010-1677-8