Brain morphometry on congenital hand deformities based on Teichmüller space theory

Congenital Hand Deformities (CHD) are usually occurred between fourth and eighth week after the embryo is formed. Failure of the transformation from arm bud cells to upper limb can lead to an abnormal appearing/functioning upper extremity which is presented at birth. Some causes are linked to genetics while others are affected by the environment, and the rest have remained unknown. CHD patients develop prehension through the use of their hands, which affect the brain as time passes. In recent years, CHD have gain increasing attention and researches have been conducted on CHD, both surgically and psychologically. However, the impacts of CHD on brain structure are not well-understood so far. Here, we propose a novel approach to apply Teichmüller space theory and conformal welding method to study brain morphometry in CHD patients. Conformal welding signature reflects the geometric relations among different functional areas on the cortex surface, which is intrinsic to the Riemannian metric, invariant under conformal deformation, and encodes complete information of the functional area boundaries. The computational algorithm is based on discrete surface Ricci flow, which has theoretic guarantees for the existence and uniqueness of the solutions. In practice, discrete Ricci flow is equivalent to a convex optimization problem, therefore has high numerically stability. In this paper, we compute the signatures of contours on general 3D surfaces with surface Ricci flow method, which encodes both global and local surface contour information. Then we evaluated the signatures of pre-central and post-central gyrus on healthy control and CHD subjects for analyzing brain cortical morphometry. Preliminary experimental results from 3D MRI data of CHD/control data demonstrate the effectiveness of our method. The statistical comparison between left and right brain gives us a better understanding on brain morphometry of subjects with Congenital Hand Deformities, in particular, missing the distal part of the upper limb.

[1]  Timothy F. Cootes,et al.  Shape Discrimination in the Hippocampus Using an MDL Model , 2003, IPMI.

[2]  T. Elbert,et al.  Cortical reorganization and phantom phenomena in congenital and traumatic upper-extremity amputees , 1998, Experimental Brain Research.

[3]  T. Chan,et al.  Shape analysis with conformal invariants for multiply connected domains and its application to analyzing brain morphology , 2009, CVPR 2009.

[4]  Ron Kikinis,et al.  Nondistorting flattening maps and the 3-D visualization of colon CT images , 2000, IEEE Transactions on Medical Imaging.

[5]  Marianne Arner,et al.  Epidemiology of congenital upper limb anomalies in 562 children born in 1997 to 2007: a total population study from stockholm, sweden. , 2010, The Journal of hand surgery.

[6]  P. Manske,et al.  Developmental biology and classification of congenital anomalies of the hand and upper extremity. , 2010, The Journal of hand surgery.

[7]  Kuncheng Li,et al.  Voxel-based assessment of gray and white matter volumes in Alzheimer's disease , 2010, Neuroscience Letters.

[8]  F. P. Gardiner,et al.  Quasiconformal Teichmuller Theory , 1999 .

[9]  H. R. Mccarroll Congenital anomalies: a 25-year overview. , 2000, The Journal of hand surgery.

[10]  B. Chow,et al.  The Ricci flow on surfaces , 2004 .

[11]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[12]  Alain Trouvé,et al.  Metamorphoses Through Lie Group Action , 2005, Found. Comput. Math..

[13]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[14]  Henry C. Thacher,et al.  Applied and Computational Complex Analysis. , 1988 .

[15]  A. Flatt The care of congenital hand anomalies , 1994 .

[16]  E. Sharon,et al.  2D-Shape Analysis Using Conformal Mapping , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[17]  Paul M. Thompson,et al.  Brain Surface Conformal Parameterization with Algebraic Functions , 2006, MICCAI.

[18]  Lok Ming Lui,et al.  Shape Analysis of Planar Objects with Arbitrary Topologies Using Conformal Geometry , 2010, ECCV.

[19]  Douglas W. Jones,et al.  Shape analysis of brain ventricles using SPHARM , 2001, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001).

[20]  T. Chan,et al.  Genus zero surface conformal mapping and its application to brain surface mapping. , 2004, IEEE transactions on medical imaging.

[21]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[22]  Moo K. Chung,et al.  Tensor-Based Cortical Surface Morphometry via Weighted Spherical Harmonic Representation , 2008, IEEE Transactions on Medical Imaging.

[23]  P C Bucy,et al.  The contribution of the precentral gyrus to the pyramidal tract of man. , 1967, Journal of neurosurgery.

[24]  Isaac Harvey,et al.  Classification of congenital anomalies of the hand and upper limb: development and assessment of a new system. , 2013, The Journal of hand surgery.

[25]  S. Kozin Upper-extremity congenital anomalies. , 2003, The Journal of bone and joint surgery. American volume.

[26]  Michael I. Miller,et al.  Multi-structure network shape analysis via normal surface momentum maps , 2008, NeuroImage.

[27]  Karl J. Friston,et al.  Identifying global anatomical differences: Deformation‐based morphometry , 1998 .

[28]  Lok Ming Lui,et al.  Brain Surface Conformal Parameterization Using Riemann Surface Structure , 2007, IEEE Transactions on Medical Imaging.

[29]  Paul M. Thompson,et al.  A surface-based technique for warping three-dimensional images of the brain , 1996, IEEE Trans. Medical Imaging.

[30]  Qing He,et al.  Semi-automatic 3D segmentation of brain structures from MRI , 2011, Int. J. Data Min. Bioinform..

[31]  Paul A. Yushkevich,et al.  Segmentation, registration, and measurement of shape variation via image object shape , 1999, IEEE Transactions on Medical Imaging.

[32]  Xianfeng Gu,et al.  Discrete Surface Ricci Flow , 2008, IEEE Transactions on Visualization and Computer Graphics.

[33]  Kenneth Stephenson,et al.  Cortical cartography using the discrete conformal approach of circle packings , 2004, NeuroImage.

[34]  Ivo D. Dinov,et al.  A Computational Model of Multidimensional Shape , 2010, International Journal of Computer Vision.

[35]  Paul M. Thompson,et al.  Use of 3-D cortical morphometry for mapping increased cortical gyrification and complexity in Williams syndrome , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..