Patient-oriented simulation based on Monte Carlo algorithm by using MRI data
© Chuang et al; licensee BioMed Central Ltd. 2012
Received: 9 August 2011
Accepted: 17 April 2012
Published: 17 April 2012
Although Monte Carlo simulations of light propagation in full segmented three-dimensional MRI based anatomical models of the human head have been reported in many articles. To our knowledge, there is no patient-oriented simulation for individualized calibration with NIRS measurement. Thus, we offer an approach for brain modeling based on image segmentation process with in vivo MRI T1 three-dimensional image to investigate the individualized calibration for NIRS measurement with Monte Carlo simulation.
In this study, an individualized brain is modeled based on in vivo MRI 3D image as five layers structure. The behavior of photon migration was studied for this individualized brain detections based on three-dimensional time-resolved Monte Carlo algorithm. During the Monte Carlo iteration, all photon paths were traced with various source-detector separations for characterization of brain structure to provide helpful information for individualized design of NIRS system.
Our results indicate that the patient-oriented simulation can provide significant characteristics on the optimal choice of source-detector separation within 3.3 cm of individualized design in this case. Significant distortions were observed around the cerebral cortex folding. The spatial sensitivity profile penetrated deeper to the brain in the case of expanded CSF. This finding suggests that the optical method may provide not only functional signal from brain activation but also structural information of brain atrophy with the expanded CSF layer. The proposed modeling method also provides multi-wavelength for NIRS simulation to approach the practical NIRS measurement.
In this study, the three-dimensional time-resolved brain modeling method approaches the realistic human brain that provides useful information for NIRS systematic design and calibration for individualized case with prior MRI data.
Near-infrared spectroscopy (NIRS) is a promising non-invasive brain imaging technique with a higher sampling rate than positron emission tomography (PET)/functional magnetic resonance imaging (fMRI) and a more precise and localized spatial resolution than Electroencephalography (EEG)/Magnetoencephalography (MEG). The NIRS technique provides information about the slow signal (i.e., hemoglobin response) and fast signal (i.e., neuronal activation) [1–5]. This optical method permitted several benefits as non-invasive, less expensive, non-ionizing radiation imaging, real-time measurement, compact implementation, long time monitoring and easy operation with high time resolution and adequate spatial resolution for continuously recording oxy- and deoxy-hemoglobin changes of brain. Also, NIRS offers a more comprehensive measurement of brain activity than blood-oxygenation-level-dependent (BOLD) fMRI.
Functional near-infrared brain imaging is achieved with the backscattering light detection by using source-detector pairs on the surface of human head [5–7]. For NIRS implementation, there are several issues that including signal-to-noise ratio evaluation, optimal choice of source-detector separation, the brain structural effects on light propagation and the brain volume sampled remain to be fully understood well. Therefore, the simulation approach is important for characterization of photon migration in human brain with various source-detector separations to provide helpful information for individualized design of NIRS system [8–10].
In the most previous studies, the simulation results were generally obtained by semi-infinity five-layer structure [11–22] or two-dimensional head model with a MRI slice [13, 18]. Naturally, the three-dimensional brain structure modeling by utilizing in vivo MRI data provides a realistic phenomenon of photon migration dynamics. However, there is no detail description for efficient and systematic modeling method of Monte Carlo algorithm with three-dimensional anatomical MRI data [14, 15, 17, 19]. Additionally, the three-dimensional model which faithfully represents the realistic human head from MRI data depends on image processing.
Therefore, we offer a systematic approach for 3D brain modeling based on image segmentation process with in vivo MRI T1 three-dimensional image. For investigation of individualized difference in brain structure with NIRS, an adult volunteer was modeling to implement Monte Carlo simulated with various source-detector separations. According to previously studies, the light guiding effect occurred in the CSF layer of human brain. The presence of a relatively clear layer such as CSF that has both low scattering and absorption coefficients has been shown especially to alter the light propagation in the head [12, 16, 19, 23, 24]. This phenomenon cannot be portrayed by diffusion approximation method because the CSF reveals low scattering property  but it can be observed in the Monte Carlo simulation. Accordingly, the result indicates the advantage of the Monte Carlo method for NIRS modeling. Besides, the NIRS system typically applies multi-wavelength sources to detect the concentration changes of oxy- and deoxy-hemoglobin such as 690 nm, 780 nm and 830 nm. Therefore, this study offers a NIRS simulation method for understanding photon migration dynamics in human brain by using three-dimensional MRI data with multi-wavelength illumination.
Three dimensional brain MRI T1 data processing
where G α is the Gaussian kernel with standard deviation α and I is an image.
Figure 2(b) shows the results obtained using level sets to segment scalp and skull from the background. To obtain the outer boundary of the scalp, we started with an initial level set at the boundary of the image. To obtain the structures of skull on the inside of the brain, we started with an initial level set that was a closed curve around a point on the inside. This curve evolved to identify the boundaries of skull inside the brain. The contours generated by the level sets are closed contours.
After image segmentation by utilizing level set method, the contours of scalp and skull were segmented as shown in Figure 2(c) and 2(d). The region growing approach was then adopted to segment the two connected layers in binary image. The basic idea of region growing was starting with seeds. The grow regions from corresponding seeds revealed similar properties with their neighboring pixels [30, 31]. According to the result of region growing segmentation, the scalp and skull layers were distinguished and marked as type 1 and 2 in simulation (Figure 2(e) and 2(f)). Figure 2(g) shows the two layers modeling of the scalp and skull.
Monte Carlo algorithm
where w is the weight of the photon packet before the scattering event. In addition, the formal solution, Mie theory, describes absorption and/or scattering event with a sphere that has been available in previously study . The photon-passed voxels were all recorded with temporal evolution for photon footprint tracing. Therefore, the dynamic behavior of photon migration in human brain can be manifested. Additionally, we recorded all the paths of the received photons in the simulations, the visited layers of each photon were marked. Accordingly, spatial sensitivity profiles (SSP) of adult head models were calculated from the accumulated trajectories of photons. The spatial sensitivity has been described theoretically by photon measurement density functions or sensitivity maps [39, 42, 43].
Optical properties of brain tissues in Monte Carlo simulation
μ a/μ s' at 690 nm (cm-1 )
μ a/μ s' at 780 nm (cm-1 )
μ a/μ s' at 830 nm (cm-1 )
Anisotropy factor (g)
Additional file 1: The movies of the dynamics of photon migration in brain models. The movie shows the dynamic photon migration with 800 nm light pulse illumination through the adult brain models. Two cross-sectional views are demonstrated as transverse and sagittal. (MOV 8 MB)
In this paper, the patient-oriented and individualized simulation for brain monitoring by using in vivo MRI data was proposed. An adult brain was modeled in three-dimensional time-resolved Monte Carlo simulation to investigate the structural characteristic of individualized brain. The result indicates that the three-dimensional model which faithfully represents the realistic and individualized human head from MRI data depends on image processing. In order to detect the characteristic of individualized structural with NIRS measurement, the various source-detector separations on human head were simulated dynamically with transverse and sagittal views in an adult brain model. Although the previously studies indicated the light guiding effect occurred in the CSF layer of human head. We described the Monte Carlo method that is capable of performing the penetration analysis with dynamic photon migration movies to show more clearly effects of light guiding by CSF. The penetration of 800 nm light via the CSF is remarkably clear in the Figure 7.
The CSF is a region that is filled with clear low scattering fluid in the head. In other words, the effect of both low scattering and absorption coefficients in the CSF layer reveals a strong effect on light propagation in the head. Thus, the photons propagate longer distance along the CSF layer can be observed clearly in the movie file of Figure 7, especially in sagittal view. In the Figure 8, we observed that the penetration depth in sagittal view is longer than transverse with source-detector separation from 3 cm to 10 cm, especially from 6 cm to 10 cm. In other words, the transverse cross-section contains bigger volume of gray and white matter and smaller volume of CSF than the sagittal cross-section that can observe in Figure 8. Besides, the gray and white matters generate absorption and multi-scattering that cause shallow penetration in transverse cross-section. On the contrary, the CSF layer provides low extinction of light that can help light propagation longer in sagittal cross-section. The results showed that the spatial sensitivity profile in the head formed unlike the well-known "banana" shape when the source-detector separations are less than 3 cm, which covered uniformly between the source and the detector, and covered the gray matter and even the surface of the white matter. Significant distortions were observed around the cerebral cortex folding. The spatial sensitivity profile penetrated deeper to the brain in the case of expanded CSF. Accordingly, the cerebral cortex folding geometry was suggested to significantly affect the spatial sensitivity profile in human head because it filled with CSF. This was discrepant with the previous finding by loose brain models [39, 40]. However, accurate modeling of the brain structure based on image segmentation process, the effect of the sulcus (filled of CSF) on the spatial sensitivity profile was obvious. Therefore, in most studies using the brain models based on MRI data of the adult brain [13–15, 17, 38, 39], the cerebral cortex folding in the models were suggested to be not exact and large enough to affect the spatial sensitivity profile. In the sagittal section, the photons propagate longer distance along the CSF layer can be observed clearly because the expanded interhemispheric fissure. This finding suggests that the optical method may provide not only functional signal from brain activation but also structural information of brain atrophy with the expanded CSF layer.
The multi-wavelength simulations at 690, 780, and 830 nm demonstrate nicely effects of increasing absorption with wavelength and light guiding effect through the cerebral cortex folding.
In previous studies, although the results of Monte Carlo simulation of light propagation in full segmented 3D MRI model of the human head was presented and the code was released for use by other researchers, it was an only one regular brain model. In our study, we provided an efficient and systematic modeling method of individual brain model for patient-oriented measurement and analysis. The signal-to-noise ratio evaluation and optimal choice of source-detector separation for individualized brain may provide more helpful information for NIRS systems design.
Li et. al. indicated that the significant characterization on the visible Chinese human model was significantly stronger than that on the MRI model . Additionally, we clarified and proved that the three-dimensional model which faithfully represents the realistic human head from MRI data depends on image processing.
Currently, most Monte Carlo simulations have been suited to a single wavelength. However, the NIRS system usually applies multi-wavelength. In our study, we reformed the Monte Carlo simulation for multi-wavelength sources to approach the practical NIRS measurement.
In conclusion, the three-dimensional time-resolved brain modeling method approaches the realistic human brain that provides useful information for NIRS systematic design and calibration for individualized case with prior MRI data. Besides, NIRS, with its advantages, could be a useful research tool for the diagnosis of patient-oriented in the near future.
This research was supported by National Taiwan University YongLin Biomedical Engineering Center and under grants FB002-2, National Taiwan University and under grants 10R80921-4, the National Science Council of Taiwan and under grants NSC 100-2221-E-010-004, NSC 100-2622-E-010-003-CC3, NSC 100-2627-E-010-001, a grant from Ministry of Education, Aim for the Top University Plan in National Yang-Ming University and Yen Tjing Ling Medical Foundation.
- Strangman G, Boas DA, Sutton JP: Non-invasive neuroimaging using near-infrared light. Biol Psychiatry 2002, 52: 679–693. 10.1016/S0006-3223(02)01550-0View ArticleGoogle Scholar
- Wolf M, Wolf U, Choi JH, Gupta R, Safonova LP, Paunescu LA, Michalos A, Gratton E: Functional Frequency-Domain Near-Infrared Spectroscopy Detects Fast Neuronal Signal in the Motor Cortex. NeuroImage 2002, 17: 1868–1875. 10.1006/nimg.2002.1261View ArticleGoogle Scholar
- Gratton G, Brumback CR, Gordon BA, Pearson MA, Low KA, Fabiani M: Effects of measurement method, wavelength, and source-detector distance on the fast optical signal. NeuroImage 2006, 32: 1576–1590. 10.1016/j.neuroimage.2006.05.030View ArticleGoogle Scholar
- Medvedeva AV, Kainerstorferb J, Borisova SV, Barbourc RL, VanMetera J: Event-related fast optical signal in a rapid object recognition task: Improving detection by the independent component analysis. Brain Res 2008, 1236: 145–158.View ArticleGoogle Scholar
- Hillman EMC: Optical brain imaging in vivo: techniques and applications from animal to man. J Biomed Opt 2007, 12: 051402. 10.1117/1.2789693View ArticleGoogle Scholar
- Obrig H, Wenzel R, Kohl M, Horst S, Wobst P, Steinbrink J, Thomas F, Villringer A: Near-infrared spectroscopy: does it function in functional activation studies of the adult brain? Int J Psychophysiol 2000, 35: 125–142. 10.1016/S0167-8760(99)00048-3View ArticleGoogle Scholar
- Boas DA, Gaudette T, Strangman G, Cheng X, Marota JJA, Mandeville JB: The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics. NeuroImage 2001, 13: 76–90.View ArticleGoogle Scholar
- Gebhart SC, Lin WC, Jansen AM: In vitro determination of normal and neoplastic human brain tissue optical properties using inverse adding-doubling. Phys Med Biol 2006, 51: 2011–2027. 10.1088/0031-9155/51/8/004View ArticleGoogle Scholar
- Zhao H, Tanikawa Y, Gao F, Onodera Y, Sassaroli A, Tanaka K, Yamada Y: Maps of optical differential pathlength factor of human adult forehead, somatosensory motor and occipital regions at multi-wavelengths in NIR. Phys Med Biol 2002, 47: 2075–2093. 10.1088/0031-9155/47/12/306View ArticleGoogle Scholar
- Bevilacqua F, Piguet D, Marquet P, Gross JD, Tromberg BJ, Depeursinge C: In vivo local determination of tissue optical properties: applications to human brain. Appl Optics 1999, 38: 4939–4950. 10.1364/AO.38.004939View ArticleGoogle Scholar
- Li T, Gong H, Luo Q: Visualization of light propagation in visible Chinese human head for functional near-infrared spectroscopy. JBO 2011, 16(045001):1–6.Google Scholar
- Okada E, Delpy DT: Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer. Appl Optics 2003, 42: 2906–2914. 10.1364/AO.42.002906View ArticleGoogle Scholar
- Fukui Y, Ajichi Y, Okada E: Monte Carlo prediction of near-infrared light propagation in realistic adult and neonatal head models. Appl Optics 2003, 42: 2881–2887. 10.1364/AO.42.002881View ArticleGoogle Scholar
- Mudra R, Nadler A, Keller E, Niederer P: Analysis of near-infrared spectroscopy and indocyanine green dye dilution with Monte Carlo simulation of light propagation in the adult brain. J Biomed Opt 2006, 11: 044009. 10.1117/1.2341652View ArticleGoogle Scholar
- Xu Y, Graber HL, Barbour RL: Image correction algorithm for functional three-dimensional diffuse optical tomography brain imaging. Appl Optics 2007, 46: 1693–1704. 10.1364/AO.46.001693View ArticleGoogle Scholar
- Okada E, Delpy DT: Near-infrared light propagation in an adult head model. II. Effect of superficial tissue thickness on the sensitivity of the near-infrared spectroscopy signal. Appl Optics 2003, 42: 2915–2922. 10.1364/AO.42.002915View ArticleGoogle Scholar
- Boas DA, Dale AM: Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function. Appl Optics 2005, 44: 1957–1968. 10.1364/AO.44.001957View ArticleGoogle Scholar
- Ogoshi Y, Okada E: Analysis of light propagation in a realistic head model by a hybrid method for optical brain function measurement. Opt Rev 2005, 12: 264–269. 10.1007/s10043-005-0264-yView ArticleGoogle Scholar
- Heiskala J, Nissilä I, Neuvonen T, Järvenpää S, Somersalo E: Modeling anisotropic light propagation in a realistic model of the human head. Appl Optics 2005, 44: 2049–2057. 10.1364/AO.44.002049View ArticleGoogle Scholar
- Hayashi T, Kashio YO, Okada E: Hybrid Monte Carlo-diffusion method for light propagation in tissue with a low-scattering region. Appl Optics 2003, 42: 2888–2896. 10.1364/AO.42.002888View ArticleGoogle Scholar
- Hoshi Y, Shimada M, Sato C, Iguchi Y: Reevaluation of near-infrared light propagation in the adult human head: implications for functional near-infrared spectroscopy. J Biomed Opt 2005, 10: 064032. 10.1117/1.2142325View ArticleGoogle Scholar
- Diamond SG, Huppert TJ, Kolehmainen V, Franceschini MA, Kaipio JP, Arridge SR, Boas DA: Dynamic physiological modeling for functional diffuse optical tomography. NeuroImage 2006, 30: 88–101. 10.1016/j.neuroimage.2005.09.016View ArticleGoogle Scholar
- Firbanky M, Arridgez SR, Schweigery M, Delpy DT: An investigation of light transport through scattering bodies with non-scattering regions. Phys Med Biol 1996, 41: 767–783. 10.1088/0031-9155/41/4/012View ArticleGoogle Scholar
- Wolf M, Keel M, Dietz V, von Siebenthal K, Bucher HU, Baenziger O: The influence of a clear layer on near-infrared spectrophotometry measurements using a liquid neonatal head phantom. Phys Med Biol 1999, 44: 1743–1753. 10.1088/0031-9155/44/7/313View ArticleGoogle Scholar
- Dehghani H, Delpy DT, Arridge SR: Photon migration in non-scattering tissue and the effects on image reconstruction. Phys Med Biol 1999, 44: 2897–2906. 10.1088/0031-9155/44/12/303View ArticleGoogle Scholar
- Oshe S: Fronts Propagating with Curvature- Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations. J Comput Phys 1998, 79: 12–49.View ArticleMathSciNetGoogle Scholar
- Raviv TR, Kiryati N, Sochen N: Segmentation by Level Sets and Symmetry. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Volume 1 CVPR06 2006, 00: 1015–1022.Google Scholar
- Leventon ME, Faugeras O, Grimson WEL, Wells WM: Level Set Based Segmentation with Intensity and Curvature Priors. IEEE Workshop on Math Met Biomed Imag Anal 2000, 00: 4–11.Google Scholar
- Li C, Xu C, Gui C, Fox MD: Level set evolution without re-initialization: a new variational formulation. IEEE Comp Soc Conf Comp Vis Pattern Recogn 2005, 1: 430–436.Google Scholar
- Hojjatoleslami SA, Kittler J: Region growing: a new approach. IEEE T Image Process 1998, 7: 1079–1084. 10.1109/83.701170View ArticleGoogle Scholar
- Tremeau A, Borel N: A region growing and merging algorithm to color segmentation. Pattern Recognition 1997, 30: 1191–1203. 10.1016/S0031-3203(96)00147-1View ArticleGoogle Scholar
- Ashburner J, Friston KJ: Unified segmentation. NeuroImage 2005, 26: 839–851. 10.1016/j.neuroimage.2005.02.018View ArticleGoogle Scholar
- Strangman G, Franceschini MA, Boas DA: Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters. NeuroImage 2003, 18: 865–879. 10.1016/S1053-8119(03)00021-1View ArticleGoogle Scholar
- Lee CK, Sun CW, Lee PL, Lee HC, Yang CC, Jiang CP, Tong YP, Yeh TC, Hsieh JC: Study of photon migration with various sourcedetector separations in near-infrared spectroscopic brain imaging based on threedimensional Monte Carlo modelling. Opt Express 2005, 13: 8339–8348. 10.1364/OPEX.13.008339View ArticleGoogle Scholar
- Wang LH, Jacques SL, Zheng L-Q: MCML - Monte Carlo modeling of photon transport in multilayered tissues. Comput Meth Prog Bio 1995, 47: 131–146. 10.1016/0169-2607(95)01640-FView ArticleGoogle Scholar
- Kirillin M, Meglinski I, Kuzmin V, Sergeeva E, Myllylä R: Simulation of optical coherence tomography images by Monte Carlo modeling based on polarization vector approach. Opt Express 2010, 18: 21714–21724. 10.1364/OE.18.021714View ArticleGoogle Scholar
- Churmakov DY, Meglinski IV, Greenhalgh DA: Influence of refractive index matching on the photon diffuse reflectance. Phys Med Biol 2002, 47: 4271–4285. 10.1088/0031-9155/47/23/312View ArticleGoogle Scholar
- Meglinsky IV, Matcher SJ: Modelling the sampling volume for skin blood oxygenation measurements. Med Biol Eng Comput 2001, 39: 44–50. 10.1007/BF02345265View ArticleMATHGoogle Scholar
- Mansouri C, Huillier JPL, Kashou NH, Humeau A: Depth sensitivity analysis of functional near-infrared spectroscopy measurement using three-dimensional Monte Carlo modelling-based magnetic resonance imaging. Lasers Med Sci 2010, 25: 431–438. 10.1007/s10103-010-0754-4View ArticleGoogle Scholar
- Boas DA, Culver JP, Stott JJ, Dunn AK: Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head. Opt Express 2001, 10: 159–170.View ArticleGoogle Scholar
- Bohren CF, Huffman DR: Absorption and Scattering of Light by Small Particles. John Wiley & Sons; 1983.Google Scholar
- Okada E, Firbank M, Schweiger M, Arridge SR, Cope M, Delpy DT: Theoretical and experimental investigation of near-infrared light propagation in a model of the adult head. Appl Opt 1997, 36: 21–31.View ArticleGoogle Scholar
- Meglinsky IV, Matcher SJ: Analysis of the spatial distribution of the detector sensitivity in a multilayer randomly inhomogeneous medium with strong light scattering and absorption by the Monte Carlo method. Opt Spectrosc 2001, 91: 692–697.Google Scholar
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