T1 magnetic resonance imaging head segmentation for diffuse optical tomography and electroencephalography

Abstract. Accurate segmentation of structural magnetic resonance images is critical for creating subject-specific forward models for functional neuroimaging source localization. In this work, we present an innovative segmentation algorithm that generates accurate head tissue layer thicknesses that are needed for diffuse optical tomography (DOT) data analysis. The presented algorithm is compared against other publicly available head segmentation methods. The proposed algorithm has a root mean square scalp thickness error of 1.60 mm, skull thickness error of 1.96 mm, and summed scalp and skull error of 1.49 mm. We also introduce a segmentation evaluation metric that evaluates the accuracy of tissue layer thicknesses in regions of the head where optodes are typically placed. The presented segmentation algorithm and evaluation metric are tools for improving the localization accuracy of neuroimaging with DOT, and also multimodal neuroimaging such as combined electroencephalography and DOT.

[1]  W. Eric L. Grimson,et al.  Anatomical atlas-guided diffuse optical tomography of brain activation , 2009, NeuroImage.

[2]  Eiji Okada,et al.  Effects of spatial variation of skull and cerebrospinal fluid layers on optical mapping of brain activities , 2010 .

[3]  D. Boas,et al.  Volumetric diffuse optical tomography of brain activity. , 2003, Optics letters.

[4]  David A. Boas,et al.  Tetrahedral mesh generation from volumetric binary and grayscale images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[5]  Thomas R. Knösche,et al.  Influences of skull segmentation inaccuracies on EEG source analysis , 2012, NeuroImage.

[6]  J. Mugler,et al.  Three‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE) , 1990, Magnetic resonance in medicine.

[7]  P. Celsis,et al.  Effects of skull thickness, anisotropy, and inhomogeneity on forward EEG/ERP computations using a spherical three‐dimensional resistor mesh model , 2004, Human brain mapping.

[8]  D. Boas,et al.  Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head. , 2002, Optics express.

[9]  Robert Oostenveld,et al.  The five percent electrode system for high-resolution EEG and ERP measurements , 2001, Clinical Neurophysiology.

[10]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[11]  David A. Boas,et al.  Validating atlas-guided DOT: A comparison of diffuse optical tomography informed by atlas and subject-specific anatomies , 2012, NeuroImage.

[12]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[13]  Qianqian Fang,et al.  Quantitative assessment of diffuse optical tomography sensitivity to the cerebral cortex using a whole-head probe. , 2012, Physics in medicine and biology.

[14]  Xavier Tricoche,et al.  Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling , 2006, NeuroImage.

[15]  Masako Okamoto,et al.  Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping , 2004, NeuroImage.

[16]  Quan Zhang,et al.  Scalp and skull influence on near infrared photon propagation in the Colin27 brain template , 2014, NeuroImage.

[17]  B.N. Cuffin,et al.  Effects of local variations in skull and scalp thickness on EEG's and MEG's , 1993, IEEE Transactions on Biomedical Engineering.

[18]  R. Leahy,et al.  EEG and MEG: forward solutions for inverse methods , 1999, IEEE Transactions on Biomedical Engineering.

[19]  Belma Dogdas,et al.  Segmentation of skull and scalp in 3‐D human MRI using mathematical morphology , 2005, Human brain mapping.

[20]  Alexander Opitz,et al.  Electric field calculations in brain stimulation based on finite elements: An optimized processing pipeline for the generation and usage of accurate individual head models , 2013, Human brain mapping.

[21]  Anders M. Dale,et al.  A hybrid approach to the Skull Stripping problem in MRI , 2001, NeuroImage.

[22]  Valer Jurcak,et al.  10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems , 2007, NeuroImage.

[23]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[24]  Arthur W. Toga,et al.  Online resource for validation of brain segmentation methods , 2009, NeuroImage.

[25]  Audrey R. Nath,et al.  The Developmental Trajectory of Brain-Scalp Distance from Birth through Childhood: Implications for Functional Neuroimaging , 2011, PloS one.

[26]  David A. Boas,et al.  Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters , 2003, NeuroImage.

[27]  Arvid Lundervold,et al.  Evaluation of automated brain MR image segmentation and volumetry methods , 2009, Human brain mapping.

[28]  D. Louis Collins,et al.  Twenty New Digital Brain Phantoms for Creation of Validation Image Data Bases , 2006, IEEE Transactions on Medical Imaging.