An imaging method for diffuse light brain tissue oxygen concentration change quantity fused with photon weight and related products

Abstract

The application discloses an imaging method for fusing photon weight and diffuse light brain tissue blood oxygen concentration change quantity and related products, and relates to the technical field of biological tissue blood oxygen imaging. The method comprises the following steps: acquiring experimental data obtained by irradiating a measured tissue with light sources of two wavelengths; performing the following calculation on the experimental data corresponding to each wavelength of the light source: calculating the average photon transmission path of the kth light probe combination in the ith voxel unit by using a formula; calculating the photon weight of the kth light probe combination in the ith voxel unit by using a formula; calculating the change quantity of the tissue absorption coefficient of the ith voxel unit by using a formula; and finally calculating the change quantity of the concentrations of oxyhemoglobin and deoxyhemoglobin by using the calculation data obtained by the two wavelengths. The application proposes a method for fusing photon weight, and by means of weight calculation, the accurate brain tissue blood oxygen concentration change quantity can be obtained by using a small amount of S-D combination photon signals.

Description

Technical Field

[0001] This application relates to the field of biological tissue blood oxygenation imaging technology, and in particular to an imaging method and related products for measuring changes in blood oxygen concentration in brain tissue by incorporating photon weights. Background Technology

[0002] Blood oxygenation in biological tissues is an important physiological parameter, especially in the brain, where oxygenation levels are closely related to neuronal activity and blood supply. For example, under external stimuli or when the brain performs language or cognitive tasks, central neurons are activated, causing dilation of the capillary network in the cerebral cortex and accelerated blood circulation, resulting in a rich oxygen supply and increasing the concentration of hemoglobin and oxygen saturation in the brain. In pathological conditions such as stroke and Alzheimer's disease, the central neurons or capillary network in the brain cannot be effectively activated, leading to decreased blood oxygenation levels or even hypoxia. Therefore, changes in brain oxygenation concentration under external stimuli are closely related to physiological state and represent a functional imaging technique for detecting various neurological diseases.

[0003] Near-infrared diffuse optical imaging (NIR DEM) is a technique for detecting blood oxygenation in biological tissues. The principle involves placing multiple light sources and detectors on the surface of the tissue being measured, forming multiple light source-detector (SD) combinations. Different light sources sequentially emit a large number of photons into the tissue. After several scattering or absorption events within the tissue, some photons return to the tissue surface and are collected by the detector, thus acquiring the photon signal. In signal processing, the change in the absorption coefficient (Δμa) within the tissue is first estimated by measuring the change in light intensity collected by the detector. Since the main absorbers in biological tissues are oxy-hemoglobin, deoxy-hemoglobin, and water, the concentrations of oxy-hemoglobin ([ΔHbO2]) and deoxy-hemoglobin ([ΔHb]) can be calculated using the changes in the absorption coefficients at two or more wavelengths.

[0004] The process of reconstructing two-dimensional or three-dimensional images of tissue [ΔHbO2] and [ΔHb] from light intensity data acquired by the above light source-detector (SD) combination is called blood oxygenation imaging. Traditional blood oxygenation imaging is divided into two methods: Modified Beer-Lambert (MBL) and Finite Element Method (FEM). The MBL method maps the blood oxygenation changes obtained at each SD location to the local area covered by that location, and then stitches together the blood oxygenation changes from all local areas to obtain a global blood oxygenation image (or blood oxygenation map). This method is computationally simple and produces stable results, and requires only a small number of SD combinations (less than 50) to achieve imaging, resulting in low equipment costs. However, MBL can only obtain a two-dimensional blood oxygenation topographic map and cannot reflect blood oxygenation changes at different depths within the tissue.

[0005] FEM discretizes the tissue under test into a large number of volumetric units and obtains the discrete form of the differential equations for photon transmission in these volumetric units, forming a solution matrix. Further, the changes in blood oxygen concentration ([ΔHbO2] and [ΔHb]) for each volumetric unit are obtained through matrix solving, thus producing a three-dimensional image of the changes in blood oxygen concentration. While FEM can obtain blood oxygenation information at different tissue depths, the discrete form of the differential equations requires a large number of SD combinations (greater than 300) to obtain stable three-dimensional images, significantly increasing the hardware cost of the light source and detector. If only a small number of SD combinations (<50) are used to acquire photon information, the blood oxygenation imaging obtained by FEM is extremely unstable and has large errors. Summary of the Invention

[0006] The purpose of this application is to provide an imaging method and related products for measuring changes in brain tissue blood oxygen concentration using diffuse light with fused photon weights, which can obtain accurate changes in brain tissue blood oxygen concentration using photon information acquired with a small amount of SD combination.

[0007] To achieve the above objectives, this application provides the following solution:

[0008] In a first aspect, this application provides an imaging method for measuring changes in blood oxygen concentration in diffuse light brain tissue by incorporating photon weights, comprising:

[0009] Experimental data were obtained by irradiating the tested tissue with light sources of two wavelengths. The experimental data consisted of all photon packets and their corresponding information after Monte Carlo simulation of the tested tissue. The photon packet information included the photon energy and transmission path of the photon packet. Each photon packet was a combination of photons with the same path. The Monte Carlo simulation was performed using several detector assemblies. Each detector assembly could acquire the photon energy and transmission path of several photon packets. Each detector assembly included a light source and several detectors. The detector assemblies were placed on the tested tissue, which was divided into several voxel units.

[0010] The experimental data for each wavelength of the light source were calculated as follows:

[0011] Using formula Calculate the first i In the individual element unit, the first k The average photon propagation path of each optical detector combination was obtained; among them... w (q,k) Indicates the first q The total energy of each photon packet s (i,q,k) For the first q The photon packet is at the... i Transport paths within individual units, H k Indicates the first k The total number of photon packets acquired by each optical detector array;

[0012] Using formula Calculate the first i In the individual element unit, the first k The photon weights acquired by each optical detector combination; among them... ss (i,k) Indicates the first i The first individual element unit k The average photon propagation path of each optical probe combination is obtained;

[0013] Using formula Calculate the first i The change in the tissue absorption coefficient of individual units; whereby M Indicates the total number of light sources. J This indicates the number of detectors in each optical detector assembly. ws (i,k) Indicates the first i The first individual element unit k The photon weights obtained by each optical detector combination Indicates the first k The average change in tissue absorption coefficient within the coverage area of ​​a single optical probe combination;

[0014] Calculate the first tissue absorption coefficient change and the second tissue absorption coefficient change using the second tissue absorption coefficient change. i The change in the concentration of oxyhemoglobin and deoxyhemoglobin in the individual unit; the change in the first tissue absorption coefficient is calculated using experimental data corresponding to the first wavelength of the light source. i The change in tissue absorption coefficient of the individual unit; the second change in tissue absorption coefficient is calculated using experimental data corresponding to the second wavelength of the light source. i The change in the tissue absorption coefficient of an individual unit.

[0015] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the imaging method for the change in blood oxygen concentration in diffuse light brain tissue by fusion photon weighting as described above.

[0016] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the imaging method for the change in blood oxygen concentration in diffuse light brain tissue with fused photon weights as described above.

[0017] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the imaging method for the change in blood oxygen concentration in diffuse light brain tissue with fused photon weights as described above.

[0018] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0019] This application provides an imaging method and related products for the change of blood oxygen concentration in diffuse light brain tissue by incorporating photon weighting. The method includes: acquiring experimental data obtained by irradiating the tissue under test with light sources of two wavelengths; the experimental data consists of all photon packets and corresponding photon packet information after Monte Carlo simulation of the tissue under test; the photon packet information includes the photon energy and transmission path of the photon packet; the photon packet is a combination of photons with the same path; the Monte Carlo simulation is performed using several photodetector combinations; each photodetector combination can acquire the photon energy and transmission path of several photon packets; each photodetector combination includes a light source and several detectors; the photodetector combination is disposed on the tissue under test; the tissue under test is evenly divided into several voxel units; the experimental data corresponding to each wavelength of light source are calculated as follows: using the formula... Calculate the first i In the individual element unit, the first k The average photon propagation path of each optical detector combination was obtained; among them... w (q,k)Indicates the first q The total energy of each photon packet s (i,q,k) For the first q The photon packet is at the... i Transport paths within individual units, H k Indicates the first k The total number of photon packets acquired by each optical detector array; using the formula Calculate the first i In the individual element unit, the first k The photon weights acquired by each optical detector combination; among them... ss (i,k) Indicates the first i The first individual element unit k The average photon propagation path of each optical probe combination. ss (i) Indicates the first i Total photon transport path in a single unit; using the formula Calculate the first i The change in the tissue absorption coefficient of individual units; whereby M Indicates the total number of light sources. J This indicates the number of detectors in each optical detector assembly. ws (i,k) Indicates the first i The first individual element unit k The photon weights obtained by each optical detector combination Indicates the first k The average change in tissue absorption coefficient over the coverage area of ​​each optical detector combination; using the changes in the first and second tissue absorption coefficients, the first... i The change in the concentration of oxyhemoglobin and deoxyhemoglobin in the individual unit; the change in the first tissue absorption coefficient is calculated using experimental data corresponding to the first wavelength of the light source. i The change in tissue absorption coefficient of the individual unit; the second change in tissue absorption coefficient is calculated using experimental data corresponding to the second wavelength of the light source. i The change in tissue absorption coefficient of a voxel unit. This application proposes a method for fusing photon weights, which can reflect the contribution weight of different photons to each voxel unit. By calculating the weights, the accurate change in brain tissue blood oxygen concentration can be obtained using a small number of SD combined photon signals. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is an application environment diagram of an imaging method for measuring changes in blood oxygen concentration in brain tissue by incorporating photon weights, according to one embodiment of this application.

[0022] Figure 2 This is a flowchart illustrating an imaging method for measuring changes in blood oxygen concentration in brain tissue using diffuse light with fused photon weights, provided as an embodiment of this application.

[0023] Figure 3 This is a schematic diagram of a three-dimensional organizational model of voxel units provided in an embodiment of this application.

[0024] Figure 4 This is a schematic diagram showing the distribution of the light source and detector on the surface of the tissue being tested, according to an embodiment of this application.

[0025] Figures 5(a)-5(f) are schematic diagrams showing the changes in the absorption coefficient of brain tissue at different depths during 830nm light measurement according to an embodiment of this application.

[0026] Figures 6(a)-6(f) are schematic diagrams showing the changes in the absorption coefficient of brain tissue at different depths in a 690nm light measurement according to an embodiment of this application.

[0027] Figures 7(a)-7(f) are schematic diagrams showing the changes in oxyhemoglobin concentration in brain tissue at different depths according to an embodiment of this application.

[0028] Figures 8(a)-8(f) are schematic diagrams showing the changes in deoxyhemoglobin concentration in brain tissue at different depths according to an embodiment of this application.

[0029] Figure 9 This is a schematic diagram of the overall process provided for an embodiment of this application.

[0030] Figure 10 This is a schematic diagram illustrating the implementation process of step B4 in one embodiment of this application.

[0031] Figure 11 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] To address the limitations of existing technologies in achieving low cost, stable imaging, and the ability to represent different depths within tissues, this application proposes an imaging method for measuring changes in brain tissue oxygenation concentration using diffused light with photon weights. This method can stably obtain three-dimensional images of changes in brain tissue oxygenation concentration using a small number of photon signals (less than 50).

[0034] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] The imaging method for measuring changes in blood oxygen concentration in brain tissue using diffuse light with fused photon weights provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server, a server cluster consisting of multiple servers, or a cloud server.

[0036] In one exemplary embodiment, such as Figure 2 As shown, an imaging method for measuring changes in blood oxygen concentration in brain tissue by fusing photon weights is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S1 to S5. Wherein:

[0037] S1. Acquire experimental data obtained by irradiating the tissue under test with light sources of two wavelengths; the experimental data consists of all photon packets and corresponding photon packet information after Monte Carlo simulation of the tissue under test; the photon packet information includes the photon energy and transmission path of the photon packet; the photon packet is a combination of photons with the same path; the Monte Carlo simulation is performed using several photodetector combinations (SD combinations); each photodetector combination can acquire the photon energy and transmission path of several photon packets; each photodetector combination includes a light source and several detectors; the photodetector combination is placed on the tissue under test; the tissue under test is evenly divided into several voxel units.

[0038] In this embodiment, the tissue to be tested is the forehead of the subject, and the tissue to be tested is divided into 1536 voxel units; the Monte Carlo simulation is performed using 8 light detector combinations; each light detector combination includes 1 light source and 6 detectors.

[0039] Specifically, a total of [number] arrangements were made on the surface of the tissue being tested. M J Each SD assembly (for ease of description, the SD assembly is also referred to as the optical detector assembly in this application), is divided into... M Groups, each group includes one light source and J A detector surrounds the light source. During data acquisition, M The light sources emit light in sequence. J Each detector simultaneously collects photons emitted from the tissue surface, thus completing... M J Complete data acquisition for each SD location. When M =8, J When = 6, a total of 48 SD combinations are used to acquire photon signals. This embodiment can acquire photon signals from a smaller number of measurement data (e.g., 48 = 8). 6) Stably reconstruct three-dimensional voxel units (e.g., 16 voxel units). 16 6) Images showing changes in blood oxygen concentration in brain tissue.

[0040] The energy and path of the photon packets in this application can be obtained through photonic Monte Carlo simulation. The process involves placing a light source and a detector on the tissue surface, setting the tissue optical properties, and using a computer to simulate the absorption, scattering, and transmission of photon packets in the tissue model, thereby obtaining the energy and transmission path of the detected photon packets.

[0041] Monte Carlo simulation is a computer simulation. This is because physical experiments can only obtain the intensity of the detected light. I ( t 0, k )and I (t 1, k To obtain the tissue absorption change Δμ, the mean photon propagation path (MPL) needs to be known. a However, the MPL (Mean Transmission Level) cannot be directly measured. Previously, it was obtained analytically from the diffusion equation of diffuse light. This embodiment uses a weighted summation of the propagation paths of photon packets to obtain the MPL. The propagation paths of photon packets can only be obtained through Monte Carlo simulation. Monte Carlo can describe the random process of photon propagation within tissue. By setting optical properties (absorption, scattering, refraction, etc.), any Monte Carlo software (MCX, MCVM, etc.) can be used to obtain the energy of the photon packets and their paths within voxel units. Note that the energy of the photon packets here is obtained during Monte Carlo simulation and differs from the experimentally detected light intensity. I ( t 0, k )and I ( t 1, k This is completely unrelated. Computer simulation of photon transmission is a complex process that can only be achieved using readily available Monte Carlo software (MCX, MCVM, etc.). This embodiment only utilizes the results of Monte Carlo simulation (energy of photon packets, path of photon packets in voxel units) to calculate average path and photon weights.

[0042] In this embodiment, the tested tissue (or phantom) is divided into n=1536 (16 16 6) Units (i.e., voxel units). For example... Figure 3 As shown, it is a schematic diagram of a three-dimensional tissue model divided into voxel units in this embodiment.

[0043] The light source (S) and detector (D) are arranged on the tissue surface, with a total of M For J combinations of SDs, please refer to [link / reference]. Figure 4 This is a schematic diagram showing the distribution of the light source and detector on the surface of the tissue being tested when M=8 and J=6 (in this embodiment, on the forehead surface of the human brain); where S1 and S8 represent the positions of the light source, and D11~D16, D21~D26, ..., D81~D86 represent the positions of the detector. Here, S1 and D11~16 form a set of SD combinations, and so on, for a total of 8 sets of SD combinations.

[0044] The experimental data for each wavelength of light source were calculated as follows (the average propagation path (MPL) of photons can be calculated using Monte Carlo simulation of photons, and is derived from the tissue absorption coefficient of the area covered by all SD combinations). Dma The average change was used to calculate the change in the tissue absorption coefficient of each voxel unit. Dm a ( i ):

[0045] S2, Using the formula Calculate the first i In the individual element unit, the first k The average photon propagation path of each optical detector combination was obtained; among them... w (q,k) Indicates the first q The total energy of each photon packet s (i,q,k) For the first q The photon packet is at the... i Transport paths within individual units, H k Indicates the first k The total number of photon packets acquired by each optical detector array.

[0046] The average photon propagation path is explained below: w ( q , k Multiply by s ( i , q , k And for all H k Summing the photon packets (that is) ), get all H k The photon packet is at the... i The sum of photon paths within an individual unit, divided by H k The total energy of a photon packet (that is) ), that is, to obtain the first i Average photon transport path of an individual unit.

[0047] S3. Using the formula Calculate the first i In the individual element unit, the first k The photon weights acquired by each optical detector combination; among them... ss (i,k) Indicates the first i The first individual element unit k The average photon propagation path of each optical probe combination. Indicates the first of all optical probe combinations i Total photon transport path in an individual unit.

[0048] The above photon weighting calculation process is explained as follows: The summation is performed over all average photon propagation paths (that is...) ), thus obtaining the average path sum of photons for all optical detector combinations. Using the first k The photon paths of each optical detector combination divided by this sum (i.e.) ), that is, the photon weights of the optical detector combination are obtained.

[0049] According to this formula, any combination of optical detectors (SDs) can be determined at the [number]th [time]. i Photon weights of individual units.

[0050] S4. Use formula Calculate the first i The change in the tissue absorption coefficient of individual units; whereby M Indicates the total number of light sources. J This indicates the number of detectors in each optical detector assembly. ws (i,k) Indicates the first i The first individual element unit k The photon weights obtained by each optical detector combination Indicates the first k The average change in tissue absorption coefficient within the coverage area of ​​each optical probe combination.

[0051] The calculation process for the change in tissue absorption coefficient of the i-th voxel unit is explained below: ws ( i , k Multiply by D m a ( k And sum over all SD combinations (that is...) ), to obtain the photon-weighted sum of the changes in absorption coefficients of all SD combinations within the i-th voxel unit, and divide it by the sum of photon weights (that is, ), that is, to obtain the change in the tissue absorption coefficient of the i-th voxel unit.

[0052] Therefore, this embodiment realizes the calculation of the tissue absorption coefficient change of each voxel unit with fused photon weights.

[0053] S5. Using the change in the first tissue absorption coefficient and the change in the second tissue absorption coefficient, calculate the... i The change in the concentration of oxyhemoglobin and deoxyhemoglobin in the individual unit; the change in the first tissue absorption coefficient is calculated using experimental data corresponding to the first wavelength of the light source. i The change in tissue absorption coefficient of the individual unit; the second change in tissue absorption coefficient is calculated using experimental data corresponding to the second wavelength of the light source. i The change in the tissue absorption coefficient of an individual unit.

[0054] This embodiment uses two wavelengths ( l 1 and l2) Near-infrared light is used to irradiate the tissue being tested, and the change in the tissue absorption coefficient is obtained according to the formula above. Dma ( i , l 1) and Dma ( i , l 2); Based on the relationship between the absorption coefficient and hemoglobin concentration, the changes in the concentrations of oxyhemoglobin and deoxyhemoglobin in the i-th voxel unit can be obtained:

[0055] ;

[0056] ;

[0057] in, e HbO2 and e Hb They are respectively Extinction coefficients of oxyhemoglobin and deoxyhemoglobin at different wavelengths; e HbO2 and e Hb They are respectively Extinction coefficients of oxyhemoglobin and deoxyhemoglobin at different wavelengths; For the first i The change in the concentration of oxyhemoglobin in an individual unit; for The change in tissue absorption coefficient at a given wavelength, i.e., the change in the second tissue absorption coefficient; for The change in tissue absorption coefficient at a given wavelength, i.e., the change in the first tissue absorption coefficient. For the first i The change in deoxyhemoglobin concentration in individual units.

[0058] The formulas for calculating the changes in oxyhemoglobin and deoxyhemoglobin concentrations are explained below: Based on the principle of photon absorption in biological tissues, the incident light wavelength is... l At that time, tissue absorption coefficient m a ( i,l This is equal to the sum of the coefficients of all light absorbers. In human tissues, the main light absorbers are oxyhemoglobin, deoxyhemoglobin, and water, i.e.:

[0059] m a ( i,l )= C[HbO2] ( i,l )+ C [Hb] ( i,l )+ C water ( i,l );

[0060] in, C [HbO2] ( i,l ), C [Hb] ( i,l ), C water ( i,l The absorption coefficients of oxyhemoglobin, deoxyhemoglobin, and water are respectively; on the other hand, the absorption coefficients of oxyhemoglobin and deoxyhemoglobin are their concentrations multiplied by their extinction coefficients, i.e.:

[0061] C [HbO2] ( i,l )= e HbO2 ( l [HbO2]( i ); C [Hb] ( i,l )= e Hb ( l )[Hb]( i );

[0062] here e HbO2 and e Hb For a given wavelength l The extinction coefficients of oxyhemoglobin and deoxyhemoglobin are obtained from the literature. [HbO2] and [Hb] represent the concentrations of oxyhemoglobin and deoxyhemoglobin, respectively. For the first i Individual unit oxyhemoglobin concentration; For the first i Individual unit deoxyhemoglobin concentration.

[0063] Since the absorption coefficient of water remains constant, therefore... m a ( i,l ) Calculation formula and C [HbO2] ( i,l ) Calculation formula, from t 0 o'clock tThe change in tissue absorption coefficient at time 1 is as follows:

[0064] Dm a ( i,l )= e HbO2 ( l )Δ[HbO2]( i )+ e Hb ( l )Δ[Hb]( i );

[0065] Here, the symbol Δ represents the amount of change. That is... For the first i The change in the concentration of oxyhemoglobin in an individual unit; For the first i The change in deoxyhemoglobin concentration in individual units.

[0066] Using two wavelengths ( l 1 and l 2) Near-infrared light is used to irradiate the tissue being tested, as obtained from the previous formula:

[0067] Dm a ( i,l 1 )= e HbO2 ( l 1 )Δ[HbO2]( i )+ e Hb ( l 1 )Δ[Hb]( i );

[0068] Dm a ( i,l 2 )= e HbO2 ( l 2 )Δ[HbO2]( i )+ e Hb ( l 2 )Δ[Hb]( i );

[0069] For this system of two linear equations, the unknown variable Δ[HbO2]( i ) and Δ[Hb]( iSolve the system of equations simultaneously to obtain the formulas for calculating the changes in the concentrations of oxyhemoglobin and deoxyhemoglobin mentioned above.

[0070] In addition, the k The average change in tissue absorption coefficient in the area covered by each optical detection combination The calculation methods include:

[0071] A1, using the first q The propagation path of each photon packet within each voxel unit is calculated. q The total transmission path of a photon packet is calculated using the following formula:

[0072] ;

[0073] in, n This indicates the total number of voxel units.

[0074] A2, using the first k The total propagation path and total energy of each photon packet acquired by the optical detector combination are used to calculate the th photon packet. k The average photon propagation path of a single optical detector combination is calculated using the following formula:

[0075] ;

[0076] in, L (q,k) Indicates the first q The total transmission path of each photon packet.

[0077] A3. Using formulas ΔOD ( k )= ln ( I ( t 0, k ) / I ( t 1, k ))calculate t 0 o'clock to t The change in photon density within time 1; where, from the 1st time... k In each SD combination, t 0 time and t The light intensities obtained at time 1 are respectively I(t 0, k ) and I( t 1, k ). ln (·) is the natural logarithm.

[0078] A4. Using formulas Dm a ( k )= ΔOD (k ) / MPL ( k ) Calculate the first k The average change in tissue absorption coefficient within the coverage area of ​​each optical detection array; among which... ΔOD ( k )express t 0 o'clock to t The change in photon density within time 1. MPL ( k ) indicates the first k Average photon transmission path of a single optical probe combination.

[0079] The specific experimental process and information acquisition process include the following steps:

[0080] The imaging method in this embodiment was quantitatively validated in a verbal fluent task experiment in the human brain. The experiment included the following procedures:

[0081] (i) The subject sits in a chair; the researchers use medical alcohol swabs to wipe the test area of ​​the subject.

[0082] (ii) The optical probe is fixed to the subject's forehead using a medical bandage. The probe contains 8 A sensor with 6 SD combinations (i.e., 8 light source positions and 48 detector positions).

[0083] (iii) Pre-task measurements were performed while the subject remained calm. Photons with wavelengths of 830 nm and 690 nm were emitted sequentially into the cerebral cortex at each light source location, and eight detectors around the light source collected the photons simultaneously. The eight light sources emitted light in sequence, with each light source emitting light and collecting photons for 0.2 seconds. After eight light source switchings, the total time for the pre-task optical measurements was 1.6 seconds.

[0084] (iv) Subjects completed a language fluency test under guidance, which included forming words using simple words provided (e.g., sky, earth, cloud, wind, black, white, soil, etc.) within 60 seconds.

[0085] (v) Optical measurements were performed immediately after the subject finished the language fluency test, in the same manner as in step (iii); the optical measurements were performed after the task was completed in 1.6 seconds.

[0086] The light intensity signals collected before and after the mission (i.e. I ( t 0, k )and I ( t 1, k(The experiment only obtained light intensity signals; the photon path and photon energy in this embodiment were obtained through computer simulation of photon transmission (i.e., photon Monte Carlo simulation); the photon Monte Carlo simulation was implemented using specialized software (e.g., MCX, MCVM), and the optical parameters used in the simulation can be obtained from the literature.) Using the imaging method described above in this embodiment, a three-dimensional image of the change in the absorption coefficient of brain tissue was obtained (as shown in Figures 5(a)-5(f) and 6(a)-6(f), where Figure 5(a) shows the change in the absorption coefficient of brain tissue at a depth of 5 mm in 830 nm light measurement; Figure 5(b) shows the change in the absorption coefficient of brain tissue at a depth of 5 mm in 830 nm light measurement). Figure 5(c) shows the change in absorption coefficient of brain tissue at a depth of 10 mm under 30 nm light measurement; Figure 5(d) shows the change in absorption coefficient of brain tissue at a depth of 15 mm under 830 nm light measurement; Figure 5(e) shows the change in absorption coefficient of brain tissue at a depth of 20 mm under 830 nm light measurement; Figure 5(f) shows the change in absorption coefficient of brain tissue at a depth of 30 mm under 830 nm light measurement. Figure 6(a) shows the change in absorption coefficient of brain tissue at a depth of 5 mm under 690 nm light measurement; Figure 6(b) shows the change in absorption coefficient of brain tissue at a depth of 5 mm under 690 nm light measurement. The changes in absorption coefficient of brain tissue at a depth of 10 mm are shown in Figure 6(c); the changes in absorption coefficient of brain tissue at a depth of 15 mm are shown in Figure 6(d); the changes in absorption coefficient of brain tissue at a depth of 20 mm are shown in Figure 6(e); the changes in absorption coefficient of brain tissue at a depth of 25 mm are shown in Figure 6(f); the changes in absorption coefficient of brain tissue at a depth of 30 mm are shown in Figure 6(f); and the changes in blood oxygen concentration are shown in Figures 7(a)-7(f) and 8(a)-8(f), where Figure 7(a) shows the changes in absorption coefficient of brain tissue at a depth of 5 mm. Figure 7(b) shows the change in oxyhemoglobin concentration in brain tissue at a depth of 10 mm (ΔHbO2); Figure 7(c) shows the change in oxyhemoglobin concentration in brain tissue at a depth of 15 mm (ΔHbO2); Figure 7(d) shows the change in oxyhemoglobin concentration in brain tissue at a depth of 20 mm (ΔHbO2); Figure 7(e) shows the change in oxyhemoglobin concentration in brain tissue at a depth of 25 mm (ΔHbO2); Figure 7(f) shows the change in oxyhemoglobin concentration in brain tissue at a depth of 30 mm (ΔHbO2).Figure 8(a) shows the change in deoxyhemoglobin concentration (ΔHb) in brain tissue at a depth of 5 mm; Figure 8(b) shows the change in deoxyhemoglobin concentration (ΔHb) in brain tissue at a depth of 10 mm; Figure 8(c) shows the change in deoxyhemoglobin concentration (ΔHb) in brain tissue at a depth of 15 mm; Figure 8(d) shows the change in deoxyhemoglobin concentration (ΔHb) in brain tissue at a depth of 20 mm; Figure 8(e) shows the change in deoxyhemoglobin concentration (ΔHb) in brain tissue at a depth of 25 mm; Figure 8(f) shows the change in deoxyhemoglobin concentration (ΔHb) in brain tissue at a depth of 30 mm. The integers on the horizontal and vertical axes represent the grid numbers (unitless). (The horizontal and vertical axes can also represent pixel numbers (dimensionless). For example, if the horizontal axis ranges from 1 to 16 and the vertical axis ranges from 1 to 16, then this image has 16 pixels.) 16 = 256 pixels.

[0087] The imaging method of this embodiment can be extended to more general situations (i.e., acquiring optical signals at multiple times) according to actual needs, thereby obtaining continuous images of changes in brain tissue blood oxygen concentration.

[0088] In general, such as Figure 9 As shown, this embodiment provides an imaging method for measuring changes in blood oxygen concentration in brain tissue using diffuse photon weighting, comprising the following steps: B1: Dividing the tissue (or phantom) to be tested into n voxel units. B2: Arranging M voxel units on the tissue surface. J SD combinations are used, and the light intensity signals of all SD combinations are acquired at two different times. B3: Calculate the average change in tissue absorption coefficient for any SD combination. This step requires obtaining the average photon propagation path. Please refer to... Figure 10 B4: The average photon propagation path is calculated using Monte Carlo simulation, and the change in tissue absorption coefficient for each voxel unit is calculated from the average change in tissue absorption coefficient across all SD combinations. B5: The tested tissue is irradiated with near-infrared light of two wavelengths to obtain the respective changes in tissue absorption coefficient, and further, the changes in oxyhemoglobin and deoxyhemoglobin concentrations for all voxel units are obtained. This embodiment can obtain cerebral blood oxygenation changes with depth information using only a small amount of photon information from SD combinations, exhibiting stronger anti-interference capabilities and more stable imaging results.

[0089] The imaging method for measuring changes in blood oxygen concentration in brain tissue by incorporating photon weights ("imaging" refers to the process of calculating the grayscale value of each two-dimensional pixel unit or three-dimensional voxel unit. Projecting these grayscale values ​​onto any display device (computer or camera, etc.) presents a two-dimensional or three-dimensional image. That is, calculating the change in blood oxygen concentration of each voxel unit and representing its value using the grayscale of the image) described in this embodiment has the following beneficial effects:

[0090] (1) The method described in this embodiment, compared with the existing MBL method, uses the same SD combination to reflect information of different tissue depths and realize three-dimensional imaging of changes in blood oxygen concentration in brain tissue.

[0091] (2) The method described in this embodiment reflects the contribution weight of different photons to each tissue voxel element. The image of blood oxygen concentration change is obtained by linear iteration. Compared with the existing FEM method, a small number of SD combined photon signals can be used to obtain a stable three-dimensional image of blood oxygen concentration change, which greatly reduces equipment cost.

[0092] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 11 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an imaging method for measuring changes in blood oxygen concentration in brain tissue using diffuse photon weighting.

[0093] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0094] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0095] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0096] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0097] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0098] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0099] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0100] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0101] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An imaging method for measuring changes in blood oxygen concentration in brain tissue using diffuse photon weighting, characterized in that, include: Experimental data were obtained by irradiating the tested tissue with light sources of two wavelengths. The experimental data consisted of all photon packets and their corresponding information after Monte Carlo simulation of the tested tissue. The photon packet information included the photon energy and transmission path of the photon packet. Each photon packet was a combination of photons with the same path. The Monte Carlo simulation was performed using several detector assemblies. Each detector assembly could acquire the photon energy and transmission path of several photon packets. Each detector assembly included a light source and several detectors. The detector assemblies were placed on the tested tissue, which was divided into several voxel units. The experimental data for each wavelength of the light source were calculated as follows: Using formula Calculate the average photon transmission path acquired by the k-th photodetector combination in the i-th voxel unit; where w (q,k) Let s represent the total energy of the q-th photon packet. (i,q,k) H represents the transmission path of the q-th photon packet within the i-th voxel unit. k This represents the total number of photon packets acquired by the k-th optical detector combination; Using formula Calculate the photon weights acquired by the k-th photodetector combination in the i-th voxel unit; where ss (i,k) This represents the average photon transmission path acquired by the k-th photodetector combination in the i-th voxel unit; Using formula Calculate the change in tissue absorption coefficient of the i-th voxel unit; where M represents the total number of light sources, J represents the number of detectors in each photodetector assembly, and ws (i,k) Δμ represents the photon weight acquired by the k-th photodetector combination in the i-th voxel unit. a (k) represents the average change in the tissue absorption coefficient of the area covered by the kth optical detection combination; The changes in the concentrations of oxyhemoglobin and deoxyhemoglobin in the i-th voxel unit are calculated using the changes in the first and second tissue absorption coefficients. The first tissue absorption coefficient change is the tissue absorption coefficient change of the i-th voxel unit calculated using experimental data corresponding to the first wavelength light source. The second tissue absorption coefficient change is the tissue absorption coefficient change of the i-th voxel unit calculated using experimental data corresponding to the second wavelength light source.

2. The imaging method for measuring changes in blood oxygen concentration in brain tissue using diffuse light with fused photon weights according to claim 1, characterized in that, The formula for calculating the changes in the concentrations of oxyhemoglobin and deoxyhemoglobin in the i-th voxel unit, using the changes in the first and second tissue absorption coefficients, is as follows: Where, ε HbO2 (λ1) and ε Hb (λ1) are the extinction coefficients of oxyhemoglobin and deoxyhemoglobin at wavelength λ1, respectively; ε HbO2 (λ2) and ε Hb (λ2) represents the extinction coefficients of oxyhemoglobin and deoxyhemoglobin at wavelength λ2, respectively; Δ[HbO2](i) represents the change in the concentration of oxyhemoglobin in the i-th voxel unit; Δμ a (i,λ2) represents the change in tissue absorption coefficient at wavelength λ2, i.e., the change in the second tissue absorption coefficient; Δμ a (i,λ1) represents the change in tissue absorption coefficient at wavelength λ1, i.e., the change in the first tissue absorption coefficient, and Δ[Hb](i) represents the change in the concentration of deoxyhemoglobin in the i-th voxel unit.

3. The imaging method for measuring changes in blood oxygen concentration in brain tissue using diffuse light with fused photon weights according to claim 1, characterized in that, The method for calculating the average change in the tissue absorption coefficient of the coverage area of ​​the kth optical detection array includes: Calculate the total transmission path of the q-th photon packet using the transmission path of the q-th photon packet within each voxel unit; Using the total transmission path and total energy of each photon packet acquired by the k-th optical detector combination, calculate the average photon transmission path of the k-th optical detector combination; The change in photon density from time t0 to time t1 is calculated using the formula ΔOD(k)=ln(I(t0,k) / I(t1,k)). Where I(t0,k) represents the light intensity acquired by the k-th photodetector combination at time t0, and I(t1,k)) represents the light intensity acquired by the k-th photodetector combination at time t1. Using the formula Δμ a (k)=ΔOD(k) / MPL(k) calculates the average change in tissue absorption coefficient in the area covered by the k-th optical detector combination; where ΔOD(k) represents the change in photon density from time t0 to time t1, and MPL(k) represents the average photon transmission path of the k-th optical detector combination.

4. The imaging method for measuring changes in blood oxygen concentration in brain tissue by fusing photon weights according to claim 3, characterized in that, The formula for calculating the total transmission path of the q-th photon packet using the transmission path of the q-th photon packet within each voxel unit is as follows: Where n represents the total number of voxel units.

5. The imaging method for measuring changes in blood oxygen concentration in brain tissue by fusing photon weights according to claim 3, characterized in that, The formula for calculating the average photon transmission path of the k-th optical detector combination, using the total transmission path and total energy of each photon packet acquired by the k-th optical detector combination, is as follows: Among them, L (q,k) This represents the total transmission path of the q-th photon packet.

6. The imaging method for measuring changes in diffuse brain tissue blood oxygen concentration by fusing photon weights according to claim 1, characterized in that, The tested tissue was divided into 1536 voxel units; The Monte Carlo simulation was performed using eight optical detector assemblies; each optical detector assembly included one light source and six detectors.

7. The imaging method for measuring changes in blood oxygen concentration in brain tissue by fusing photon weights according to claim 1, characterized in that, The tissue being tested was the subject's forehead.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the imaging method for the amount of change in diffuse light brain tissue oxygen concentration with fused photon weights as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the imaging method for the change in blood oxygen concentration in diffuse light brain tissue by fusing photon weights as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the imaging method for the change in blood oxygen concentration in diffuse light brain tissue by fusing photon weights as described in any one of claims 1-7.

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