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Quantitative evaluation of functional Near Infrared Spectroscopy measurements with different source-detector separations using Monte Carlo simulation

Lei Wang1, 2*, Meltem Izzetoglu2, 3, 4 and Hasan Ayaz1, 2, 5, 6
  • 1 Drexel University, School of Biomedical Engineering, Science & Health Systems, United States
  • 2 Drexel University, Cognitive Neuroengineering and Quantitative Experimental Research (CONQUER) Collaborative, United States
  • 3 Villanova University, College of Engineering, United States
  • 4 Albert Einstein College of Medicine, United States
  • 5 University of Pennsylvania, Department of Family and Community Health, United States
  • 6 Children's Hospital of Philadelphia, The Division of General Pediatrics, United States

INTRODUCTION: Functional Near Infrared Spectroscopy (fNIRS) is an emerging neuroimaging technique that utilizes near infrared light to detect cortical concentration changes of oxy-hemoglobin and deoxy-hemoglobin non-invasively (Strangman, Boas, & Sutton, 2002; Villringer & Chance, 1997). Infrared light propagates through the tissue and eventually part of it is back-scattered to the surface and collected by detector. The detected attenuated light encodes information about brain activity as a consequence of absorption and scattering dominated light tissue interaction. A number of light migration models have been developed to study the transmission process, the majority of the proposed modelling approximation fall into two categories of being either analytical or numerical methods (Arridge, 1999; Arridge & Schweiger, 1995; Boas, Culver, Stott, & Dunn, 2002; Dehghani et al., 2009; Fang, 2010; Flock, Patterson, Wilson, & Wyman, 1989; Ripoll et al., 2001; Schotland, 1997; Lihong Wang, Jacques, & Zheng, 1995). Analytical models have the advantage of fast computation, but limits to simple geometries(Arridge, Cope, & Delpy, 1992; Martelli, Contini, Taddeucci, & Zaccanti, 1997; Patterson, Chance, & Wilson, 1989). On the other hand, numerical models are capable of modelling complex geometries, but require relatively higher computational cost. One of the major computational methods in numerical algorithm is Monte Carlo (MC) simulation. OBJECTIVE: Various studies have used MC simulations to explore different aspects of system parameters, such as wavelength selection, effect of layers’ thickness and superficial layers’ influence in light detection (Guo, Cai, & He, 2013; Okada & Delpy, 2003; Takahashi et al., 2011; Uludağ, Steinbrink, Villringer, & Obrig, 2004). Our initial investigation on healthy human digital head model indicated, detector surface area could be a potential source for systematic error in the light intensity measurement (Lei Wang, Ayaz, Izzetoglu, & Onaral, 2017). Some other studies investigated NIRS sensitivity in different tissue compartments which can vary according to source detector separation. However, selection among different source detector separations still remains controversial, which indicates a quantitative evaluation of not only the penetration depth but also overall signal to noise ratio (SNR) for different source detector separations. Hence, understanding of depth sensitivity and signal quality of fNIRS measurements to brain-tissue is essential for designing experiments and devices as well as interpreting research findings which we aim to study in this work. METHODS: In MC simulation, several multi-layer slab geometries are designed to monitor adult head model as digital phantoms. The digital model utilizes a tetrahedral mesh to model a complex anatomical structure which we had performed in MATLAB (Mathworks, Natick, MA). In order to increase computational efficiency, the simulations are computed on hardware supported by Drexel’s University Research Computing Facility, Proteus, which is Drexel’s main high-performance computer cluster. It houses both AMD and Intel CPUs with QDR infiniband interconnects and runs the Red Hat Enterprise Linux operating system, 64 bit. The Proteus contains 2496 compute cores, 9.8TB RAM in total. We attempt to evaluate penetration depth of fNIRS measurement with different source-detector separations quantitatively. A digital phantom with four-layer (scalp, skull, CSF, brain) slab geometry was designed to monitor healthy adult head model where source-detector separations were set as: 10, 15, 20, 25, 30, 35 and 40 mm. Depth sensitivity and signal levels were evaluated via different aspects within MC simulation: detected photon number within each layer, sensitivity profile of photons in the model, detected light intensity of each detector location as well as the partial pathlength in each layer with mean and standard deviations calculated using total number of MC simulation runs. The same measures obtained for healthy adult model would also be evaluated in digital phantoms mimicking different ages. RESULT: Preliminary results indicated that with 100 million photons launched in the MC simulation, about 0.1644%, 0.0427%, 0.0159%, 0.0079%, 0.0047%, 0.0031% and 0.0021% of 100 million photons were detected in each source-detector separation of 10, 15, 20, 25, 30, 35 and 40 mm, respectively. However, of the total photons detected by each detector, percentage of photons reached brain layer can range from 11.19% to 99.05% with increasing orders as the source detector separation becomes larger. These results are summarized in fig 1 where the number of detected photons in each head layer is presented. The spatial sensitivity profile of the photons in the model, the so-called banana shape could illustrate the result more directly, as shown in fig 2. These results indicated that i) as the source-detector separation increases, larger proportion or percentage of photons reaches the brain layer out of all photons detected – increasing the sensitivity to brain layer; ii) however, the actual number of photons that reach the brain layer in particular and the overall number of photons detected by the detector in general drops significantly as the source detector separation is increased – reducing the signal strength and hence making it more prone to noise. In order to obtain high NIRS sensitivity to brain tissue with reliable signal quality (high SNR) appropriate source detector separations should be implemented suggesting 2.5 to 3cm separations. CONLCUSION: The detailed and quantitative evaluation of light propagation in head tissues could provide guidance for choosing appropriate source-detector separations in fNIRS measurements. In this study, our aim was to provide such clear quantitative comparisons for varying source detector separations using MC simulations. Further evaluations would include partial pathlength of each layer and detected light intensity at surface of the medium as well as at certain depth of the medium for adult healthy digital phantoms and healthy digital phantoms mimicking individuals at different ages such as infant, child and elderly.

Figure 1
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Figure 2
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Keywords: functional near-infrared spectroscopy (fNIRS), Monte Carlo simulation, Spatial sensitivity, Source-detector separation, Photon migration

Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018.

Presentation Type: Poster Presentation

Topic: Neuroergonomics

Citation: Wang L, Izzetoglu M and Ayaz H (2019). Quantitative evaluation of functional Near Infrared Spectroscopy measurements with different source-detector separations using Monte Carlo simulation. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00139

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Received: 06 Apr 2018; Published Online: 27 Sep 2019.

* Correspondence: Ms. Lei Wang, Drexel University, School of Biomedical Engineering, Science & Health Systems, Philadelphia, United States, lw474@drexel.edu