SCI Publications
2010
C. Marc, C. Vachet, J.E. Blocher, G. Gerig, J.H. Gilmore, M.A. Styner.
“Changes of MR and DTI appearance in early human brain development,” In Proceedings of SPIE Medical Imaging 7623, 762324, 2010.
DOI: 10.1117/12.844912
M.W. Prastawa, N. Sadeghi, J.H. Gilmore, W. Lin, G. Gerig.
“A new framework for analyzing white matter maturation in early brain development,” In Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 97--100. April, 2010.
ISBN: 978-1-4244-4125-9
DOI: 10.1109/ISBI.2010.5490404
N. Sadeghi, M.W. Prastawa, J.H. Gilmore, W. Lin, G. Gerig.
“Spatio-Temporal Analysis of Early Brain Development,” In Proceedings IEEE Asilomar Conference on Signals, Systems and Computers, pp. 777--781. 2010.
DOI: 10.1109/ACSSC.2010.5757670
Analysis of human brain development is a crucial step for improved understanding of neurodevelopmental disorders. We focus on normal brain development as is observed in the multimodal longitudinal MRI/DTI data of neonates to two years of age. We present a spatio-temporal analysis framework using Gompertz function as a population growth model with three different spatial localization strategies: voxel-based, data driven clustering and atlas driven regional analysis. Growth models from multimodal imaging channels collected at each voxel form feature vectors which are clustered using the Dirichlet Process Mixture Models (DPMM). Clustering thus combines growth information from different modalities to subdivide the image into voxel groups with similar properties. The processing generates spatial maps that highlight the dynamic progression of white matter development. These maps show progression of white matter maturation where primarily, central regions mature earlier compared to the periphery, but where more subtle regional differences in growth can be observed. Atlas based analysis allows a quantitative analysis of a specific anatomical region, whereas data driven clustering identifies regions of similar growth patterns. The combination of these two allows us to investigate growth patterns within an anatomical region. Specifically, analysis of anterior and posterior limb of internal capsule show that there are different growth trajectories within these anatomies, and that it may be useful to divide certain anatomies into subregions with distinctive growth patterns.
N. Sadeghi, M.W. Prastawa, J.H. Gilmore, W. Lin, G. Gerig.
“Towards Analysis of Growth Trajectory through Multi-modal Longitudinal MR Imaging,” In SPIE Medical Imaging 2010: Image Processing, Vol. 7623, 76232U, Edited by Benoit M. Dawant and David R. Haynor, pp. (published online). March, 2010.
DOI: 10.1117/12.844526
2009
M. Datar, J. Cates, P.T. Fletcher, S. Gouttard, G. Gerig, R.T. Whitaker.
“Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science LNCS, Vol. 5762, pp. 167--174. 2009.
DOI: 10.1007/978-3-642-04271-3_21
PubMed ID: 20426109
S. Durrleman, X. Pennec, A. Trouvé, G. Gerig, N. Ayache.
“Spatiotemporal atlas estimation for developmental delay detection in longitudinal datasets,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science LNCS, Vol. 5761, pp. 297--304. 2009.
DOI: 10.1007/978-3-642-04268-3_37
PubMed ID: 20426000
W. Gao, W. Lin, Y. Chen, G. Gerig, J.K. Smith, V. Jewells, J.H. Gilmore.
“Temporal and Spatial Development of Axonal Maturation and Myelination of White Matter in the Developing Brain,” In American Journal of Neuroradiology (AJNR), Vol. 30, pp. 290--296. 2009.
PubMed ID: 19001533
C. Goodlett, P.T. Fletcher, J.H. Gilmore, G. Gerig.
“Group analysis of DTI fiber tract statistics with application to neurodevelopment,” In Neuroimage, Vol. 45, No. 1 (suppl 1), pp. S133--S142. 2009.
PubMed ID: 19059345
C. Goodlett, P.T. Fletcher, J.H. Gilmore, G. Gerig.
“Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment,” In NeuroImage, Vol. 45, pp. S133--S142. December, 2009.
DOI: 10.1016/j.neuroimage.2008.10.060
PubMed ID: 19059345
PubMed Central ID: PMC2727755
Diffusion tensor imaging (DTI) provides a unique source of information about the underlying tissue structure of brain white matter in vivo including both the geometry of major fiber bundles as well as quantitative information about tissue properties represented by derived tensor measures. This paper presents a method for statistical comparison of fiber bundle diffusion properties between populations of diffusion tensor images. Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics. Diffusion properties, such as fractional anisotropy (FA) and tensor norm, along fiber tracts are modeled as multivariate functions of arc length. Hypothesis testing is performed non-parametrically using permutation testing based on the Hotelling T(2) statistic. The linear discriminant embedded in the T(2) metric provides an intuitive, localized interpretation of detected differences. The proposed methodology was tested on two clinical studies of neurodevelopment. In a study of 1 and 2 year old subjects, a significant increase in FA and a correlated decrease in Frobenius norm was found in several tracts. Significant differences in neonates were found in the splenium tract between controls and subjects with isolated mild ventriculomegaly (MVM) demonstrating the potential of this method for clinical studies.
S. Gouttard, M.W. Prastawa, E. Bullitt, W. Lin, C. Goodlett, G. Gerig.
“Constrained Data Decomposition and Regression for Analyzing Healthy Aging from Fiber Tract Diffusion Properties,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science LNCS, Vol. 5761, pp. 321--328. 2009.
PubMed ID: 20426003
H.C. Hazlett, M.D. Poe, A.A. Lightbody, G. Gerig, J.R. MacFall, A.K. Ross, J. Provenzale, A. Martin, A.L. Reiss, J. Piven.
“Teasing apart the heterogeneity of autism: Same behavior, different brains in toddlers with fragile X syndrome and autism,” In Journal of Neurodevelopmental Disorders, Vol. 1, No. 1, pp. 81--90. 2009.
PubMed ID: 20700390
Z. Liu, H. Zhu, B.L. Marks, L.M. Katz, C.B. Goodlett, G. Gerig, M. Styner.
“Voxel-wise group analysis of DTI,” In Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, pp. 807--810. 2009.
DOI: 10.1109/ISBI.2009.5193172
M.W. Mosconi, H. Cody-Hazlett, M.D. Poe, G. Gerig, R. Gimpel-Smith, J. Piven.
“Longitudinal study of amygdala volume and joint attention in 2- to 4-year-old children with autism,” In Arch Gen Psychiatry, Vol. 66, No. 5, pp. 509--516. 2009.
PubMed ID: 19414710
N. Mukherjee, C. Kang, H.M. Wolfe, B.S. Hertzberg, J.K. Smith, W. Lin, G. Gerig, R.M. Hamer, J.H. Gilmore.
“Discordance of Prenatal and Neonatal Brain Development in Twins,” In Early Human Development, Vol. 85, No. 3, pp. 171--175. 2009.
PubMed ID: 18804925
M.W. Prastawa, E. Bullitt, G. Gerig.
“Simulation of brain tumors in MR images for evaluation of segmentation efficacy,” In Med Image Anal, Vol. 13, No. 2, pp. 297--311. 2009.
PubMed ID: 19119055
F. Shi, P.T. Yap, Y. Fan, J.Z. Cheng, L.L. Wald, G. Gerig, W. Lin, D. Shen.
“Cortical Enhanced Tissue Segmentation of Neonatal Brain MR Images Acquired by a Dedicated Phased Array Coil,” In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 39--45. 2009.
PubMed ID: 20862268
F. Zhang, E.R. Hancock, C. Goodlett, G. Gerig.
“Probabilistic White Matter Fiber Tracking using, Particle Filtering and von Mises-Fisher Sampling,” In Medical Image Analysis, Vol. 13, No. 1, pp. 5--18. 2009.
PubMed ID: 18602332
2008
A. Fedorov, E. Billet, M.W. Prastawa, G. Gerig, A. Radmanesh, S.K. Warfield, R. Kikinis, N. Chrisochoides.
“Evaluation of Brain MRI Alignment with the Robust Hausdorff Distance Measures,” In Lecture Notes in Computer Science, Vol. 5358, Springer, pp. 594--603. 2008.
DOI: 10.1007/978-3-540-89639-5_57
W. Gao, Y. Chen, G. Gerig, J.K. Smith, V. Jewells, J.H. Gilmore, W. Lin.
“Temporal and Spatial Development of Axonal Maturation and Myelination of White Matter in the Developing Brain,” In American Journal of Neuroradiology, pp. (published online). Nov 11, 2008.
DOI: 10.3174/ajnr.A1363
J.H. Gilmore, L. Smith, H. Wolfe, B. Hertzberg, J.K. Smith, N. Chescheir, D. Evans, C. Kang, R.M. Hamer, W. Lin, G. Gerig.
“Prenatal Mild Ventriculomegaly Predicts Abnormal Development of the Neonatal Brain,” In Biological Psychiatry, Vol. 64, No. 12, pp. 1069-1076. Dec, 2008.
PubMed ID: 18835482