Modelling and validation of diffuse reflectance of the adult human head for fNIRS: scalp sub-layers definition

Accurate estimation of brain haemodynamics parameters such as cerebral blood flow and volume as well as oxygen consumption i.e. metabolic rate of oxygen, with funcional near infrared spectroscopy (fNIRS) requires precise characterization of light propagation through head tissues. An anatomically realistic forward model of the human adult head with unprecedented detailed specification of the 5 scalp sublayers to account for blood irrigation in the connective tissue layer is introduced. The full model consists of 9 layers, accounts for optical properties ranging from 750nm to 950nm and has a voxel size of 0.5mm. The whole model is validated comparing the predicted remitted spectra, using Monte Carlo simulations of radiation propagation with 108 photons, against continuous wave (CW) broadband fNIRS experimental data. As the true oxy- and deoxy-hemoglobin concentrations during acquisition are unknown, a genetic algorithm searched for the vector of parameters that generates a modelled spectrum that optimally fits the experimental spectrum. Differences between experimental and model predicted spectra was quantified using the Root mean square error (RMSE). RMSE was 0.071 ± 0.004, 0.108 ± 0.018 and 0.235±0.015 at 1, 2 and 3cm interoptode distance respectively. The parameter vector of absolute concentrations of haemoglobin species in scalp and cortex retrieved with the genetic algorithm was within histologically plausible ranges. The new model capability to estimate the contribution of the scalp blood flow shall permit incorporating this information to the regularization of the inverse problem for a cleaner reconstruction of brain hemodynamics.

[1]  D. Boas,et al.  Double-layer estimation of intra- and extracerebral hemoglobin concentration with a time-resolved system. , 2008, Journal of biomedical optics.

[2]  M. Kohl,et al.  Near-infrared optical properties of ex vivo human skin and subcutaneous tissues measured using the Monte Carlo inversion technique. , 1998, Physics in medicine and biology.

[3]  D. Boas,et al.  Effective scattering coefficient of the cerebral spinal fluid in adult head models for diffuse optical imaging. , 2006, Applied optics.

[4]  I. Yaroslavsky,et al.  Optical properties of selected native and coagulated human brain tissues in vitro in the visible and near infrared spectral range. , 2002, Physics in medicine and biology.

[5]  D. Delpy,et al.  Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer. , 2003, Applied optics.

[6]  David A Boas,et al.  Noninvasive measurement of neuronal activity with near-infrared optical imaging , 2004, NeuroImage.

[7]  Rebecca Richards-Kortum,et al.  Light scattering from collagen fiber networks: micro-optical properties of normal and neoplastic stroma. , 2007, Biophysical journal.

[8]  Dinh Tuan Vo Biomedical photonics handbook , 2003 .

[9]  Teresa Correia,et al.  Identification of the optimal wavelengths for optical topography: a photon measurement density function analysis. , 2010, Journal of biomedical optics.

[10]  Valery V. Tuchin,et al.  OPTICAL PROPERTIES OF SKIN, SUBCUTANEOUS, AND MUSCLE TISSUES: A REVIEW , 2011 .

[11]  A Taddeucci,et al.  Optical properties of brain tissue. , 1996, Journal of biomedical optics.

[12]  S. Arridge,et al.  Estimation of optical pathlength through tissue from direct time of flight measurement , 1988 .

[13]  Qianqian Fang,et al.  Mesh-based Monte Carlo method using fast ray-tracing in Plücker coordinates , 2010, Biomedical optics express.

[14]  Gemma Bale,et al.  Cytochrome-C-Oxidase Exhibits Higher Brain-Specificity than Haemoglobin in Functional Activation , 2016 .

[15]  D. Delpy,et al.  Near-infrared light propagation in an adult head model. II. Effect of superficial tissue thickness on the sensitivity of the near-infrared spectroscopy signal. , 2003, Applied optics.

[16]  A Rebora,et al.  The thickness of human scalp: normal and bald. , 1972, The Journal of investigative dermatology.

[17]  I. Tachtsidis,et al.  Optimal wavelength combinations for near-infrared spectroscopic monitoring of changes in brain tissue hemoglobin and cytochrome c oxidase concentrations. , 2015, Biomedical optics express.

[18]  David A Boas,et al.  Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. , 2009, Optics express.

[19]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[20]  Gemma Bale,et al.  A new broadband near-infrared spectroscopy system for in-vivo measurements of cerebral cytochrome-c-oxidase changes in neonatal brain injury. , 2014, Biomedical optics express.

[21]  S. Jacques Corrigendum: Optical properties of biological tissues: a review , 2013 .

[22]  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.

[23]  Valery V. Tuchin,et al.  Optical properties of human cranial bone in the spectral range from 800 to 2000 nm , 2006, Saratov Fall Meeting.

[24]  David W Hahn,et al.  Measurement of small-signal absorption coefficient and absorption cross section of collagen for 193-nm excimer laser light and the role of collagen in tissue ablation. , 2004, Applied optics.