Tissue erythrocyte motion tomography method and device and readable storage medium

Abstract

The invention provides a near-infrared scattering light time autocorrelation red blood cell motion tomography (SCT) method which comprises the following steps: S1, establishing a mathematical model, obtaining a calculation equation of a red blood cell motion index, and obtaining a complete SCT signal through multiple times of optical switch switching; and S2, according to the complete SCT signal and the calculation equation of the red blood cell motion index, calculating and obtaining a red blood cell motion tomographic image through a linear iteration mode. The method has the advantages that complete SCT photon signals are collected through multiple times of optical switch switching, red blood cell motion tomography is achieved in a linear iteration mode, the anti-interference capacity is higher, and the obtained image result is more stable.

Description

Technical Field

[0001] The present invention belongs to the field of tissue red blood cell motion imaging, and in particular relates to a near-infrared scattered light time autocorrelation red blood cell motion tomographic imaging method, equipment and a readable storage medium. Background Art

[0002] Breast cancer is the most threatening malignant tumor for women. Currently, non-invasive breast examinations (mammography, color Doppler ultrasound, magnetic resonance imaging, etc.) are mainly morphological imaging methods in clinical practice, which lag behind in the early detection of malignant tumors. How to successfully detect tumor cells before they aggregate into masses is a key link in achieving early screening for breast cancer. It is well known that the uncontrolled proliferation of tumor cells causes disordered capillary proliferation and local hypoxia in local tissues, and has the characteristics of "high red blood cell movement, low blood oxygen, and vigorous metabolism"; but on the other hand, functional abnormalities such as tissue hemodynamics caused by breast tumors cannot be measured by conventional ultrasound Doppler technology, so it is impossible to predict early breast cancer. Near-infrared scattered light is a traditional technology for achieving blood oxygen imaging. The principle of this technology is to calculate the absorption coefficient inside the tissue by changes in light intensity. In recent years, a new near-infrared scattered light imaging technology has been developed, called near-infrared scattered light temporal autocorrelation tomography (SCT); the measurement principle of SCT is that the random movement of red blood cells will cause disturbances in the light field. The calculation of the temporal autocorrelation function of the light field can quantify the movement of red blood cells and estimate the blood flow information of tissue microcirculation. The specific process is: the digital temporal autocorrelator calculates the temporal autocorrelation of the number of photons (i.e., light intensity) collected by the detector to obtain the normalized light intensity temporal autocorrelation function g2(τ). Further, according to the Siegert relationship, the normalized light field temporal autocorrelation function g1(τ) can be obtained, which is the original data used for SCT red blood cell movement reconstruction calculation.

[0003] The reconstruction algorithm of SCT has gone through two stages: analytical method and finite element method. Among them, the analytical method needs to assume that the measured tissue has a regular geometric shape (for example: a cuboid), which is not applicable to irregular-shaped tissues such as breast. The finite element method requires the emission and reception of photons (called the light source-detector (SD) array) at a large number of locations on the surface of the measured tissue. In order to obtain a large amount of photon information, the SCT system uses the SD array to emit and collect photons through the same lens system, but the large amount of photon hardware used increases the equipment cost of SCT. Using a mechanical motor to scan the linear probe in space can collect SCT data in time and step by step, but this method requires a long mechanical scanning time (tens of seconds per step), which seriously prolongs the data acquisition time. Summary of the invention

[0004] In order to solve the technical problems in the prior art that data acquisition in tissue red blood cell motion imaging algorithms is time-consuming and requires high hardware equipment costs, the present invention proposes a near-infrared scattered light time autocorrelation red blood cell motion tomography imaging method to greatly shorten the data acquisition time and improve the acquisition accuracy of red blood cell motion tomography images.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a near-infrared scattered light time autocorrelation red blood cell motion tomography method, comprising the following steps:

[0006] Step S1: Establish a mathematical model, obtain the equation for the red blood cell motility index, and obtain the complete SCT signal by multiple optical switch switching;

[0007] Step S2: According to the equation for obtaining the complete SCT signal and the red blood cell motion index, the red blood cell motion tomographic image is calculated by linear iteration;

[0008] The step S1 specifically includes the following steps:

[0009] Step S101: dividing the tissue to be tested into multiple units;

[0010] Step S102: reading a complete normalized light intensity-time autocorrelation curve from the SCT device by switching the optical switch multiple times;

[0011] Step S103: converting into a normalized light field time autocorrelation curve as photon data according to the Siegert relationship;

[0012] Step S104: The equation for determining the red blood cell motility index according to the photon data of step S103 is:

[0013] Db=[Db(1),Db(2),...Db(n)] T ;

[0014] Where Db is the red blood cell motility index, Db(1), Db(2), ...Db(n) represent the red blood cell motility index of the 1st, 2nd ... nth units respectively; the symbol T represents the transpose of the matrix, and n is the unit number.

[0015] The step S2 specifically includes the following steps:

[0016] Step S201: for any light source-detector combination, select a number of delay time points, obtain a first-order slope by least squares fitting, and then obtain a first-order linear equation system, which includes a plurality of linear equations and a plurality of red blood cell movement index variables;

[0017] Step S202: In the first-order linear equation system, perform the following algebraic calculations:

[0018]

[0019] Among them, x current Represents the current value; x update Represents the updated value; A (k,·) represents the kth row of matrix A. The above calculation traverses the first-order linear equation system and obtains x update As x current Perform recursive calculations until all column vectors x j (j=1,...,n) The current value corresponding to and update value satisfy Stop the recursion and obtain the first-order red cell motility index, x j is a column vector with n components;

[0020] Step S203: The first-order red blood cell movement index obtained in step S202 is fitted by the least square method to obtain the second-order slope, and then the second-order linear equation system is obtained, and the second-order red blood cell movement index is obtained by the objective function in step S202;

[0021] Step S204: Repeat steps S202 to S203 multiple times to obtain the third to fifth order red blood cell movement indexes Db(3) ... Db(5) in sequence, and use the fifth order red blood cell movement index Db(5) as the red blood cell movement tomographic image.

[0022] In addition, the present invention also provides an electronic device, including a processor and a memory that is communicatively connected to the processor and is used to store executable instructions of the processor, wherein the processor is used to execute the near-infrared scattered light time autocorrelation red blood cell motion tomography imaging method.

[0023] In addition, the present invention also provides a server, comprising at least one processor, and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor so that the at least one processor performs a near-infrared scattered light time autocorrelation red blood cell motion tomography method as described.

[0024] In addition, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the near-infrared scattered light time autocorrelation red blood cell motion tomography imaging method.

[0025] Compared with the prior art, the near-infrared scattered light time autocorrelation red blood cell motion tomography method described in the present invention has the following beneficial effects:

[0026] (1) The near-infrared scattered light time autocorrelation red blood cell motion tomography method described in the present invention can obtain complete SCT data by using the measurement method of optical switch switching;

[0027] (2) The near-infrared scattered light time autocorrelation red blood cell movement tomographic imaging method described in the present invention can utilize complete SCT data and use a linear iteration method to obtain tomographic images of red blood cell movement. Compared with the existing analytical method and finite element solution method, it has a stronger anti-interference ability to the pathological nature of the equation group and the obtained imaging results are more stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0029] Figure 1 A schematic diagram of the process of the near-infrared scattered light time autocorrelation red blood cell motion imaging method provided in the first embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of the implementation process of step S1 in the first embodiment of the present invention;

[0031] Figure 3 This is a schematic diagram of the implementation process of step S2 in the first embodiment of the present invention;

[0032] Figure 4 A schematic diagram of a three-dimensional tissue model for dividing cells according to the first embodiment of the present invention;

[0033] Figure 5 This is a schematic diagram of the distribution of the light source optical fiber and the detector optical fiber on the probe surface according to the first embodiment of the present invention;

[0034] Figure 6 This is a physical schematic diagram of an optical fiber probe according to Embodiment 1 of the present invention;

[0035] Figure 7 This is a schematic diagram of the tissue red blood cell movement tomography of a breast tumor according to Example 1 of the present invention (depth 15 mm). DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0037] Embodiment 1

[0038] Embodiment 1 of the present invention proposes a near-infrared scattered light time autocorrelation red blood cell motion imaging method, which uses an optical switch to time-share the hardware light source and detector; the optical switch only takes 0.1 seconds to switch between different SD positions on the tissue surface, greatly shortening the data acquisition time. The specific implementation method is: when a fiber array with M light sources and J detectors distributed around each light source is used, the optical switch is switched M times to complete the complete data acquisition of the M*J array. In order to adapt to the data acquisition method of the optical switch, Embodiment 1 of the present invention proposes an iterative linear method, which can stably reconstruct a larger number of units (for example: 1536=16*16*6) of red blood cell motion tomographic images from less measurement data (for example, 48=8*6).

[0039] Specifically, Figures 1 to 3 As shown, the near-infrared scattered light time autocorrelation red blood cell motion imaging method of this embodiment includes the following steps:

[0040] Step S1: Establish a mathematical model, obtain the equation for the red blood cell motility index, and obtain the complete SCT signal by switching the optical switch 8 times.

[0041] Specifically, in this embodiment, step S1 includes the following steps:

[0042] Step S101: Divide the tissue under test (or phantom) into n=1536 (16*16*6) units (i.e., image voxels). Figure 4 As shown in FIG. 1 , it is a schematic diagram of a three-dimensional organizational model for dividing cells in this embodiment. Figure 5 , which is a schematic diagram of the distribution of light source optical fibers and detector optical fibers on the probe surface in this embodiment; wherein S1 and S8 represent light source optical fibers, D11 to D15 represent detector optical fibers, and D81 to D85 represent detector optical fibers.

[0043] like Figure 6 , which is a physical schematic diagram of the optical fiber probe described in an embodiment of the present invention.

[0044] S102: Read the normalized light intensity time autocorrelation curve g2(h,τ) of the complete 8*6 array (i.e., 8 complete optical switch switching) at the initial moment from the SCT device, where h=(m,j) is the position index of the optical fiber in the light source-detector (SD) array, where m is the index of the light source position, j is the index of the detector position, and τ represents time.

[0045] S103: According to the Siegert relationship, the normalized light intensity time autocorrelation curve g2(h,τ) is converted into the normalized light field time autocorrelation curve g1(h,τ) as photon data.

[0046] In this embodiment, the first-order linear expression of the normalized light field temporal autocorrelation curve g1(h,τ) is:

[0047]

[0048] because:

[0049]

[0050] Then its N-order linear expression can be expressed as:

[0051] A*Db (N) =S1 (N) ; (3)

[0052] Among them, A is the coefficient matrix, and its matrix element can be expressed as:

[0053] A(h,i)=B(m,j,i); (4)

[0054]

[0055] Where N is the order; τ is the delay time; h = (m, j) represents the combination of the m-th light source and the j-th detector, w(q, h) is the energy weight of the q-th photon obtained by the combination at position h, s(i, q, h) is the transmission path of the q-th photon in the ith unit (i.e., voxel); k0 is a parameter related to the tissue refractive index and wavelength; μ s '(i) is the reduced scattering coefficient of the ith unit; the values ​​of the above parameters can be obtained through literature review or Monte Carlo simulation of photon transmission.

[0056] The equation for the red blood cell motility index is:

[0057] Db=[Db(1),Db(2),...Db(n)] T ; (6)

[0058] Where Db is the red blood cell motility index, Db(1), Db(2), ...Db(n) represent the red blood cell motility index of the 1st, 2nd, ...nth units respectively; the symbol T represents the transpose of the matrix, n is the unit number, and the one-dimensional row vector is converted into a one-dimensional column vector.

[0059] Step S2: Based on the equation for obtaining the complete SCT signal and the red blood cell motion index, the red blood cell motion tomographic image is calculated by linear iteration.

[0060] Specifically, in this embodiment, step S2 includes the following steps:

[0061] S201: For any set of light source-detector combinations, select g1(h, τ) of several delayed time points τ (generally the first 50); perform linear regression on τ and the normalized light field temporal autocorrelation curve g1(h, τ) obtained in step S1 to obtain the slope, expressed as b1…b n The linear regression was implemented by least squares fitting, and a first-order linear equation system was obtained, which included 48 (i.e., 8*6) linear equations, containing 1536 (i.e., 16*16*6 three-dimensional voxels) red blood cell motility index (Db (N) )variable.

[0062] S202: In the first-order linear equation system, let x = Db (N) ,b=Sl (N) Index; the specific form obtained is:

[0063]

[0064] This system of equations is recorded as:

[0065] A*X=b;(8)

[0066] Where * represents matrix multiplication, the kth equation of the system of equations is:

[0067] a k,1 x1+a k,2 x2+...+a k,j x p +...+a k,n x n =b k ; (9)

[0068] The variable to be found x=(x 1, x 2,… x j, …,x n ) T The initial value of is:

[0069]

[0070] The above initial values ​​are determined as follows:

[0071]

[0072] After determining the above initial values, let x current =x 0 , and perform the following algebraic calculations:

[0073]

[0074] Among them, x current Represents the current value; xupdate Represents the updated value; A (k,·) represents the kth row of the A matrix; the above calculation traverses rows 1-48 of the A matrix (representing 48 equations), and the specific expression of the second-order modulus square in formula (12) is:

[0075]

[0076] In this embodiment, to determine x update As x current Perform recursive calculation; the above calculation traverses rows 1-48 of matrix A and repeats this process until all x j (j=1,...,n) satisfies

[0077]

[0078] Then stop the recursive calculation and obtain the first-order red blood cell movement index Db (1) =x current .

[0079] S203: The first-order red blood cell movement index Db obtained in step S202 is (1) Substitute the left side of the first-order linear expression and obtain the second-order slope Sl by linear regression of τ and the left side of the expression (2) , further obtain the second-order linear equations, and solve the objective function in step S202 to obtain the second-order red blood cell movement index Db (2) .

[0080] S204: Repeat steps S202 to S203 multiple times to obtain the third to fifth order red blood cell movement index Db (3) …Db (5) , Db (5) As the final red blood cell movement tomographic image.

[0081] The calculation step of the method of the present invention can obtain complete SCT photon information through 8 optical switches, and realize tomographic imaging of tissue red blood cell movement through linear iteration.

[0082] Working process:

[0083] The imaging method of the present invention was quantitatively verified in a human breast tumor red blood cell movement experiment, and the experiment included the following process:

[0084] (i) The subject lies flat on the bed; the researcher uses medical alcohol cotton to wipe the test area for the subject.

[0085] (ii) The optical probe is positioned on the surface of the target location (breast) through a flexible tube. The probe is embedded with several optical fibers for transmitting and receiving red light to the breast tissue. The other end of the optical fiber is connected to the red blood cell movement meter and the optical switch. By switching the optical switch, the light source sequentially emits red light to different parts of the breast, and the detector optical fibers at different positions collect photons simultaneously.

[0086] (iii) The signal acquisition time of each group of optical switches is 12 seconds, so the measurement time of each breast is 12 seconds / group*8 groups=96 seconds. After the detection is completed, the optical probe is removed to end the breast detection test.

[0087] After each optical switch is switched, a light source and six detectors located at different positions, marked as S i and D j ,i=1,...8;j=1,...,6;transmit and receive photons;digital time autocorrelator reads the photons of the detector and calculates the time autocorrelation curve of the light intensity g2(h,τ) and calculates it according to the Siegert relation (g2(h,τ)=1+β|g1(h,τ) 2 ) is converted into the normalized light field temporal autocorrelation curve g1(h,τ), where β is taken as 0.5;

[0088] These complete photon data are processed by the imaging method of this embodiment, that is, to obtain the tomographic image of the movement of breast red blood cells, such as Figure 7 shown.

[0089] The imaging method of this embodiment can be extended to a more general case (i.e., from 8 optical switch switching to n optical switch switching, n≥2) according to actual needs, so as to obtain a tomographic image of the red blood cell movement.

[0090] Embodiment 2:

[0091] Embodiment 2 of the present invention provides an electronic device, including a processor and a memory connected to the processor for storing instructions executable by the processor, wherein the processor is used to execute a near-infrared scattered light time autocorrelation red blood cell motion tomography method described in Embodiment 1.

[0092] Embodiment three:

[0093] Embodiment 3 of the present invention provides a server, comprising at least one processor, and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor so that the at least one processor executes a near-infrared scattered light time autocorrelation red blood cell motion tomography method as described in Embodiment 1.

[0094] Embodiment 4:

[0095] Embodiment 4 of the present invention provides a computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, the method for near-infrared scattered light time autocorrelation red blood cell motion tomography described in Embodiment 1 is implemented.

[0096] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0097] In the several embodiments provided in the present application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the division of the units described above is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The above-mentioned units may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

[0099] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A near-infrared scattered light time autocorrelation red blood cell motion tomography method, characterized by: The steps include: Step S1: Establish a mathematical model, obtain the equation for the red blood cell motility index, and obtain the complete SCT signal by multiple optical switch switching; Step S2: Based on the equation for obtaining the complete SCT signal and the red blood cell motion index, the red blood cell motion tomographic image is calculated by linear iteration.

2. The near-infrared scattered light time autocorrelation red blood cell motion tomography method according to claim 1, characterized in that: The step S1 specifically includes the following steps: Step S101: dividing the tissue to be tested into multiple units; Step S102: reading a complete normalized light intensity time autocorrelation curve from the SCT device by switching the optical switch multiple times; Step S103: converting the normalized light intensity time autocorrelation curve into a normalized light field time autocorrelation curve as photon data according to the Siegert relationship; Step S104: The equation for determining the red blood cell movement index according to the photon data of step S103 is: Db=[Db(1),Db(2),...Db(n)] T ; Where Db is the red blood cell motility index, Db(1), Db(2), ...Db(n) represent the red blood cell motility index of the 1st, 2nd ... nth units respectively; the symbol T represents the transpose of the matrix, and n is the unit number.

3. The near-infrared scattered light time autocorrelation red blood cell motion tomography method according to claim 1, characterized in that: The step S2 specifically includes the following steps: S201: for any set of light source-detector combinations, select a number of delay time points, obtain a first-order slope by least squares fitting, and then obtain a first-order linear equation group, wherein the first-order linear equation group includes a plurality of linear equations and a plurality of red blood cell movement index variables; S202: In the first-order linear equation system, perform the following algebraic calculations: Among them, x current Represents the current value; x update Represents the updated value; A (k,·) represents the kth row of matrix A. The above calculation traverses the first-order linear equation system and obtains x update As x current Perform recursive calculations until all column vectors x j (j=1,...,n) The current value corresponding to and update value satisfy Stop the recursion and obtain the first-order red cell motility index, x j is a column vector with n components; S203: The first-order red blood cell movement index obtained in step S202 is fitted by the least square method to obtain the second-order slope, and then the second-order linear equation system is obtained, and the second-order red blood cell movement index is obtained by the objective function in step S202; S204: Repeat steps S202 to S203 multiple times to obtain the third to fifth order red blood cell movement indexes Db(3) ... Db(5) in sequence, and use the fifth order red blood cell movement index Db(5) as the red blood cell movement tomographic image.

4. An electronic device, comprising a processor and a memory connected to the processor for storing instructions executable by the processor, characterized in that: The processor is used to execute the near-infrared scattered light time autocorrelation red blood cell motion tomography method described in any one of claims 1 to 3.

5. A server, characterized in that: It includes at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor so that the at least one processor performs a near-infrared scattered light time autocorrelation red blood cell motion tomography method as described in any one of claims 1-3.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the near-infrared scattered light time autocorrelation red blood cell motion tomography method described in any one of claims 1 to 3 is implemented.

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