KR-102961490-B1 - DIFFUSE CORRELATION SPECTROSCOPY BASED ON NUMERIAL INTEGRATION

Abstract

The present invention relates to a diffusion correlation spectroscopy method capable of measuring changes in blood flow in real time without data fitting to a physical model by applying numerical integration directly to an optical signal. A numerical integration-based diffusion correlation spectroscopy method according to one embodiment of the present invention is characterized by comprising the steps of acquiring an optical signal of a laser scattered from a tissue, calculating a field autocorrelation function based on the optical signal, and determining a blood flow index for a blood vessel within the tissue by applying numerical integration to the field autocorrelation function.

Inventors

• 김재관
• 오윤호
• 이기준

Assignees

• 광주과학기술원
• 재단법인대구경북과학기술원

Dates

Publication Date

20260507

Application Date

20230316

Claims (9)

1. A processor acquires the optical signal of a laser scattered from the tissue; The above processor calculates a field autocorrelation function in real time based on the optical signal; and The processor comprises the step of determining a blood flow index for a blood vessel within the tissue by applying the field autocorrelation function calculated in real time to the following [Equation 1] or [Equation 2]. [Mathematical Formula 1] [Mathematical Formula 2] (Here, BFI is the blood flow index, n and T are the minimum and maximum delay times between the time of irradiation of the laser and the time of detection of the optical signal, τ is the delay time between the time of irradiation of the laser and the time of detection of the optical signal, and g 1 (τ) is the field autocorrelation function) Numerical integration-based diffusion correlation spectroscopy.
2. In paragraph 1, The step of acquiring the optical signal includes the step of detecting the optical signal at a location of tissue different from the location where the laser was irradiated. Numerical integration-based diffusion correlation spectroscopy.
3. In paragraph 1, The step of detecting the optical signal includes the step of detecting the optical signal through a single photon counting module (SPCM). Numerical integration-based diffusion correlation spectroscopy.
4. In paragraph 1, The step of calculating the field autocorrelation function comprises calculating an intensity autocorrelation function based on the intensity of the optical signal and converting the intensity autocorrelation function into the field autocorrelation function. Numerical integration-based diffusion correlation spectroscopy.
5. In paragraph 4, The above intensity autocorrelation function is calculated according to [Equation 3] below, and the above field autocorrelation function is transformed according to [Equation 4] below [Mathematical Formula 3] (Here, g 2 (τ) is the intensity autocorrelation function, I(t) is the intensity of the optical signal over time, and τ is the delay time between the time of laser irradiation and the time of detection of the optical signal) [Mathematical Formula 4] (Here, g 1 (τ) is the field autocorrelation function, and β is a parameter determined by the detected optical signal) Numerical integration-based diffusion correlation spectroscopy.
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8. In paragraph 1, The step of determining the blood flow index includes the step of applying numerical integration to a field autocorrelation function greater than or equal to a preset threshold for each tissue. Numerical integration-based diffusion correlation spectroscopy.
9. In paragraph 1, The step of determining the blood flow index includes the step of applying numerical integration to a preset maximum delay time for each tissue. Numerical integration-based diffusion correlation spectroscopy.

Description

Diffusion Correlation Spectroscopy Based on Numerical Integration The present invention relates to a diffusion correlation spectroscopy method that can measure changes in blood flow in real time without data fitting to a physical model by applying numerical integration directly to an optical signal. According to the World Health Organization (WHO), approximately 17.9 million people worldwide died from cardiovascular and cerebrovascular diseases in 2019, accounting for 32% of global deaths. Additionally, statistics on causes of death for 2019 released by Statistics Korea showed that four of the top ten causes of death in the country were cardiovascular and cerebrovascular diseases. These cardiovascular and cerebrovascular diseases include ischemic heart diseases such as angina pectoris and myocardial infarction, cerebrovascular diseases such as stroke, and hypertension, diabetes, hyperlipidemia, and arteriosclerosis that cause these diseases. Since cardiovascular and cerebrovascular diseases are primarily caused by the blockage of blood flow due to vascular stenosis, monitoring blood flow is very important for disease prevention, and for this purpose, monitoring systems utilizing imaging diagnostic devices and biometric devices are currently being used. Among them, monitoring methods using image information have the advantage of being able to obtain three-dimensional blood flow information through PET, MRI, and CT images, but they have the limitation that continuous blood flow measurement is impossible due to the time required for imaging. In addition, among monitoring methods using signal measurement, transcranial Doppler ultrasonography has the advantage of being able to confirm information regarding the occlusion of cerebral blood vessels and blood flow velocity, but it has the limitation of not being able to measure blood flow in microvessels. Accordingly, recently, diffuse correlation spectroscopy, which enables continuous blood flow measurement regardless of the size of the blood vessels, is gaining attention. Referring to Figure 1, diffusion correlation spectroscopy is a method for confirming changes in blood flow by irradiating a tissue with a laser and measuring the degree of dynamic light scattering caused by changes in blood flow. However, since this method converts the measured signal into an autocorrelation function and fits it to a physical model to obtain blood flow information (Blood Flow Index; BFI), real-time analysis and observation are difficult, and since the analysis requires a significant amount of resources, it is difficult to implement on processors with small memory capacity. FIG. 1 is a diagram illustrating a method for determining a blood flow index using a conventional diffusion correlation spectroscopy. FIG. 2 is a flowchart illustrating a numerical integration-based diffusion correlation spectroscopy according to an embodiment of the present invention. FIG. 3 is a diagram illustrating a method for determining a blood flow index based on the present invention. Figure 4 is a diagram illustrating the process of applying numerical integration to an autocorrelation function greater than a threshold value. FIG. 5 is a diagram illustrating the process of applying numerical integration for a delay time smaller than a threshold. FIG. 6 is a diagram illustrating the phantom experimental conditions for the present invention. Figure 7 is a diagram comparing the experimental results of Figure 6 with a conventional nonlinear fitting method. FIG. 8 is a diagram illustrating the experimental conditions for arterial wrist closure according to the present invention. Figure 9 is a diagram comparing the experimental results of Figure 8 with a conventional nonlinear fitting method. FIG. 10 is a diagram illustrating the operation speed when the present invention is implemented through a PC, compared with a conventional non-linear fitting method. The aforementioned objectives, features, and advantages are described in detail below with reference to the attached drawings, thereby enabling those skilled in the art to easily implement the technical concept of the present invention. In describing the present invention, detailed descriptions of known technologies related to the present invention are omitted if it is determined that such descriptions would unnecessarily obscure the essence of the invention. Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings. In the drawings, the same reference numerals are used to indicate the same or similar components. In this specification, terms such as "first," "second," etc. are used to describe various components, but these components are not limited by these terms. These terms are used merely to distinguish one component from another, and unless specifically stated otherwise, the first component may be the second component. Additionally, in this specification, the statement that