Traumatic Brain Injuries Detection By NIR spectroscopy

author: Susan

2022-01-06

Traumatic Brain Injuries Detection By NIR spectroscopy

It has high death risk of traumatic brain injuries, and cerebral hematoma is the most severe mortality. It requires fast and accurate diagnosis and treatment instrument. The NIR spectroscopy technology used to portable cerebral hematoma detector to detect the damage scene.
In the emergency scene of cerebral hematoma detection, NIR spectroscopy technology has obvious advantages and widely applied to handheld NIR spectroscopy device, test results can pass clinical diagnosis evidences.
Challenge
However, in practical applications, the existing detection technology also has obvious shortcomings, which are mainly reflected in the following five aspects:

There is no systematic study on the influence of hematoma on the transmission of near-infrared light in the brain tissue.
It is basically invalid for bilateral symmetric hematoma or subarachnoid hemorrhage.
The measurement is easily affected by hair, hair follicle and scalp hematoma
The measurement signal is weak, which is easy to cause detection failure
The measurement is susceptible to interference from ambient light, measurement techniques and other factors

Principle

1. NIR spectrometer range from 780-2526 nm, 780-1100 nm SWIR and 1100-2526 nm FWIR, in the range of 800 nm NIR range can detect through a depth of tissue, and hemoglobin and oxyhemoglobin has strong absorbance.

Figure 1 Absorption spectrum of hemoglobin

2. The main forms of light propagation in biological tissues include absorption, reflection, refraction, and scattering. When light enters the brain tissue, it will be reflected and refracted on the surface of the contact, and part of the light enters the interior of the tissue. The light entering the tissue will continue to scatter and absorb with the tissue. Part of the photons will be completely absorbed in the tissue, while another part of the photons will eventually be emitted from the tissue. The path of light transmission within the organization is shown in Figure 2.

Figure 2 Transmission of light within the organization

Solution
1. Optical model of the brain

According to the anatomical structure of the human brain, the brain model is divided into 5 layers, which are the scalp layer, the skull layer, the cerebrospinal fluid layer, the gray matter layer, and the white matter layer.

Fig.3 The structure of brain model

The brain structure and optical parameters at 840nm wavelength are shown in Figure 4. The brain parameters are defined as follows: refractive index n, absorption coefficient μa (cm-1), scattering coefficient μ s (cm-1), anisotropy factor g, tissue thickness d (cm), where d1 is the thickness of the scalp, d2 It is the thickness of the skull; the thickness of the two varies according to the growth environment, age, race, gender and other factors of the subject, and the total size (d1+d2) is usually in the range of 1.0~1.7 cm.

Figure 4 Brain model optical parameters

When the number of simulated photons is 108, the simulated wavelength is 840 nm, the thickness of the scalp and skull layer is set to 1.0~1.7 cm, the interval is 0.1 cm, and the dura mater position μ a is set to 0.5~1.5 cm-1, and the interval is 0.1 cm-1. , A total of 88 light source radial intensity distribution data are obtained by MC, as shown in Figure 2. Figure 2(a) shows the scalp and skull thickness ranging from 1.0 cm to 1.7 cm, the dura mater position μ a is 0.05 cm-1, and the light intensity distribution obtained by each detector with a different distance from the light source. Figure 2(b) shows the light intensity distribution obtained by each detector with different distances from the light source when the scalp and skull thickness is 1.3 cm, the absorption coefficient of the dura is 0.5~1.5 cm-1. For different individual differences, the light intensity
distribution obtained by the fixed distance sensor is obviously different, and the detector with a single fixed position will cause a larger detection error.

Fig.5 (a) Intensity distribution of light source radial with stationary μ a; (b) Intensity distribution of light source radial with stationary d

2. Multi-channel differential absorbance method
Figure 3 shows a schematic diagram of the multi-channel differential absorbance method for brain hematoma detection. Among them, S and S'are incident light sources, and D₁—D₅ and D'₁—D'₅ are photodetectors with equal intervals, respectively, distributed in symmetrical positions on the head. When detecting, first perform a detection on the right side of the head (R side), and the light intensities obtained by the 5 sensors are respectively I₁-I₅; then, perform detection on the left side of the head (L side), and the light obtained by the 5 sensors respectively. Strong is I'₁— I'₅. I0 is the incident light intensity on the left and right sides. According to formula (1) and formula (2), the absorbance of each position can be obtained as OD₁-OD₅, OD'₁-OD'₅.

Divide the absorbance at the left and right symmetric positions to obtain the differential absorbance:

According to the difference in absorbance at the symmetrical positions of the head, information about the degree of intracerebral hematoma can be obtained, and the differential absorbance of multiple positions can be combined to establish a model, which can effectively improve the detection accuracy of cerebral hematoma.

Fig.6 Multi-channel differential optical density detection

Test Result

1. Predictive model building process

Fig.7 (a)Optimal component selection of d; (b)Optimal component selection of epidural μ a

2. PLS model prediction results

Fig.8 (a)Prediction results of model set; (b) Prediction results of prediction set

Fig.9 (a)Prediction results of model set; (b)Prediction results of prediction set

Conclusion
The thickness of the skull can reflect the depth of the hematoma. The difference in the μ a value of the dural position reflects the difference in the degree of traumatic dural hematoma. The multi-channel near-infrared light differential absorbance and light intensity distribution are used to determine the thickness of the scalp and the skull and the position of the dura. a. Carrying out modeling and analysis, the prediction results have high accuracy, which basically meets the needs of rapid and accurate patients with traumatic cerebral hematoma. This model can detect traumatic dural hematoma patients with different scalp and skull thickness. It has good applicability and provides a new important reference for the rapid detection and degree prediction of traumatic dural hematoma.
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