A method and system for chromatic aberration correction of single-molecule localization super-resolution imaging

Abstract

The present invention relates to the field of image data processing or generation technology, and in particular to a method and system for chromatic aberration correction in single-molecule localization super-resolution imaging. The method comprises the following steps: eliminating misaligned points from multiple sets of lateral motion images of two different wavelength channels; performing registration between the retained points to obtain a chromatic aberration correction mapping function, thereby completing lateral chromatic aberration correction in single-molecule localization super-resolution imaging; performing Gaussian fitting and linear least squares fitting on multiple sets of axial height data for each wavelength channel to obtain the average deviation of the two channels at different axial heights, which is then compensated into the single-molecule localization super-resolution imaging chromatic aberration correction system to complete axial chromatic aberration correction in single-molecule localization super-resolution imaging. The method and system provided by the present invention achieve correction of lateral and axial chromatic aberration in multi-color single-molecule localization super-resolution imaging, improve the system's registration accuracy and correction speed, and reduce the cost of correction.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing or generation, and in particular to a method and system for chromatic aberration correction of single-molecule localization super-resolution imaging. Background Art

[0002] The resolution of conventional optical microscopes is limited by the diffraction limit of light, typically around 200 nanometers, making them ineffective for resolving subcellular structures. Single-molecule localization microscopy (SMLM) has surpassed the diffraction limit of conventional optical microscopes, achieving nanometer-scale resolution imaging and providing a powerful tool for cell biology research. However, in multicolor SMLM imaging, despite chromatic aberration correction in the optical system, residual chromatic aberration at the nanometer level persists. This chromatic aberration causes spatial offsets between imaging channels of different wavelengths, compromising the accuracy of colocalization analysis in multicolor imaging. A common method for correcting lateral chromatic aberration in multicolor SMLM imaging is the Locally Weighted Mean (LWM) algorithm. This algorithm calculates a weighted average based on the distance between adjacent points, so that closer pairs of points have a greater influence on the results, thereby achieving precise spatial registration within a local area. However, this method relies on the complex preparation of fluorescent microsphere arrays, the preparation equipment is relatively expensive and the preparation process is relatively complicated. At the same time, it requires the use of interpolation for global alignment, and the alignment accuracy is relatively low. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for chromatic aberration correction in single-molecule localization super-resolution imaging, which realizes the correction of lateral chromatic aberration and axial chromatic aberration in multi-color single-molecule localization super-resolution imaging, improves the alignment accuracy of the system, and has low cost and high speed.

[0004] A method for correcting chromatic aberration in single-molecule localization super-resolution imaging comprises the following steps:

[0005] Lateral chromatic aberration correction involves the following steps:

[0006] S11: using two different wavelengths of excitation light to sequentially excite the lateral chromatic aberration correction sample on the glass slide, and obtaining pixel information and lateral coordinate information of multiple sets of lateral movement images of two different wavelength channels;

[0007] S12: determining the positioning accuracy of the lateral chromatic aberration correction sample in each wavelength channel based on pixel information of the multiple sets of lateral movement images of the two different wavelength channels, eliminating points where the positioning accuracy of the lateral chromatic aberration correction sample in each wavelength channel is greater than a set threshold, and eliminating misaligned points between different wavelength channels;

[0008] S13: Align the points retained in the two different wavelength channels to obtain a chromatic aberration correction mapping function, and apply the chromatic aberration correction mapping function to complete the lateral chromatic aberration correction of single-molecule localization super-resolution imaging;

[0009] Longitudinal chromatic aberration correction includes the following steps:

[0010] S21: placing the axial chromatic aberration correction sample on a glass slide treated with poly-lysine, sequentially exciting the axial chromatic aberration correction sample using two different wavelengths of excitation light, and acquiring pixel information and axial height data of multiple sets of axial motion images of the two different wavelength channels;

[0011] S22: performing Gaussian fitting on the distribution of multiple sets of axial height data of each wavelength channel to obtain fitted axial height data of two channels;

[0012] S23: Based on the fitted axial height data of the two channels, a linear least squares fit is used to establish the relationship between the axial chromatic aberration of the channels, thereby obtaining the average deviation of the two channels at different axial heights;

[0013] S24: Compensate the average deviation of the two channels at different axial heights into the single-molecule localization super-resolution imaging chromatic aberration correction system to complete the single-molecule localization super-resolution imaging axial chromatic aberration correction.

[0014] Optimally, the lateral chromatic aberration correction sample in step S11 is a plurality of fluorescent microspheres, and the axial chromatic aberration correction sample in step S21 is a dual-color fluorescent secondary antibody.

[0015] Optimally, the two excitation lights of different wavelengths in step S11 and step S21 are respectively: excitation light of wavelength 647 nanometers and excitation light of wavelength 561 nanometers.

[0016] Optimized, when obtaining multiple sets of lateral movement images of two different wavelength channels in step S11, the exposure time of each frame is 100 milliseconds, and the field of view acquisition range of a single image is square micrometers, when obtaining multiple sets of axial motion images of two different wavelength channels in step S21, the exposure time of each frame is 10 milliseconds, and the field of view acquisition range of a single image is square micrometers.

[0017] Optimized, based on the pixel information of multiple sets of lateral movement images of two different wavelength channels, the lateral chromatic aberration correction sample positioning accuracy of the two different wavelength channels is calculated according to formula (1):

[0018] (1);

[0019] in: represents the sample positioning accuracy corrected for lateral chromatic aberration in two different wavelength channels, represents the expected value of the Gaussian distribution, represents the number of photons in the spot, Represents the pixel information of the horizontally shifted image of two different wavelength channels, Represents background noise.

[0020] Furthermore, in step S12, the DBSCAN clustering algorithm based on adaptive distance change is used to remove misaligned points between different wavelength channels, which specifically includes the following steps:

[0021] S121: The positioning points of all fluorescent microspheres in two different wavelength channels are taken as control point pairs, and the distance between the control point pairs formed by the fluorescent microspheres at the most edge positions in the two different wavelength channels is taken as the search radius. With the center of the image as the center of the circle, the scale factor is calculated according to formula (2):

[0022] (2);

[0023] in: represents the search radius, represents the scale factor, Indicates the distance from the current control point to the center of the image, represents the initial search radius;

[0024] S122: traverse all control points of one wavelength channel and calculate the search radius according to formula (2). Then, with the control point as the center of the circle, search within the calculated search radius to see whether there is a registration point in the other wavelength channel. If there is no registration point in the other wavelength channel, the control point is considered as a noise point and removed. If there are multiple registration points in the other wavelength channel, the control point and the multiple registration points are removed. If there is only one registration point in the other wavelength channel, the control point and the registration point are retained as a control point pair.

[0025] Furthermore, in step S13, the chromatic aberration correction mapping function is obtained by the following method:

[0026] S131: A second-order polynomial is used to establish the mapping relationship between the reference channel and the channel to be registered as formula (3):

[0027] (3);

[0028] in: Indicates that the control point is Axis coordinates, Indicates that the control point is Axis coordinates, represents the mapping function between the reference channel and the channel to be registered, represents the set of reference channel control points, represents the set of control points of the channel to be registered, express Axis direction mapping function, express Axis direction mapping function, express Axis direction mapping function The linear coefficient of express Axis direction mapping function The linear coefficient of express Axis direction mapping function The quadratic coefficient of express Axis direction mapping function The quadratic coefficient of express The cross-term coefficient of the axis direction mapping function, express The constant term coefficient of the axis direction mapping function, express Axis direction mapping function The linear coefficient of express Axis direction mapping function The linear coefficient of express Axis direction mapping function The quadratic coefficient of express Axis direction mapping function The quadratic coefficient of express The cross-term coefficient of the axis direction mapping function, express The constant term coefficient of the axial direction mapping function;

[0029] S132: Based on the reserved control point pairs, for any control point in the reference channel, find the control point closest to it in the channel to be registered, and calculate the square sum of the distances between the two control points. Based on the square sum of the distances between the two control points, calculate the error function according to formula (4):

[0030] (4);

[0031] in: represents the error function, Indicates the The influence weight of each control point, represents the sum of the squares of the distances between two control points, Indicates the The Euclidean distance between the control points and the registration point, represents the expected minimum target distance, Indicates the number of control points;

[0032] S133: Iterate based on the constructed error function, and compare the relationship between the current control point error and the set threshold during the iteration process. If the current control point error is greater than the set threshold, change the coefficient in the mapping relationship formula (3) and recalculate the error function until the current control point error is less than or equal to the set threshold. Then, obtain the color difference correction mapping function and output the mapping relationship. If the current control point error is less than or equal to the set threshold, directly obtain the color difference correction mapping function and output the current mapping relationship.

[0033] Optimized, before iterating based on the constructed error function in step S133, the initial value estimation and convergence optimization are first performed according to formula (5):

[0034] (5);

[0035] in: Indicates the The particle velocity at the iteration, Indicates the The particle velocity at the iteration, represents the inertia weight, represents the first learning factor, represents the first random number between 0 and 1, represents the historical optimal position of the current particle, Indicates the The particle position at the iteration, Indicates the The particle position at the iteration, represents the second learning factor, represents a second random number between 0 and 1, Represents the historical optimal position of particles in the entire swarm.

[0036] Furthermore, during the error function iteration in step S133, the registration index is accelerated as follows:

[0037] S1331: During the iteration process, a KD-tree index structure is constructed based on the retained control point pairs. Each node in the binary tree of the KD-tree index structure stores a data point, and the data points are divided along one dimension at a time. Data points that are less than or equal to the value of the data point in the dimension are divided into the left subtree, and data points that are greater than the value of the data point in the dimension are divided into the right subtree.

[0038] S1332: When performing a nearest neighbor search, first compare the data value of the current node with its value in the current dimension. If the value of the current data point is less than or equal to its value in the current dimension, search in the left subtree. If the value of the current data point is greater than its value in the current dimension, search in the right subtree and calculate the distance between the current node and the query point to find the nearest neighbor node.

[0039] S1333: Recursively search until a leaf node is reached. When backtracking, update the nearest neighbor node. Then search in another subtree and determine whether there is a nearest neighbor node. If there is no nearest neighbor node in the other subtree, save the nearest neighbor node updated when backtracking. If there is a nearest neighbor node in the other subtree, update the nearest neighbor node.

[0040] A single-molecule localization super-resolution imaging chromatic aberration correction system, used to perform any of the single-molecule localization super-resolution imaging chromatic aberration correction methods described above, comprising an excitation light source module, an excitation light path control module, a sample, a fluorescence collection and filtering module, a light detection module, and a data acquisition and processing module;

[0041] The excitation light source module is used to excite the sample using excitation light of different wavelengths;

[0042] The samples include lateral chromatic aberration correction samples and axial chromatic aberration correction samples;

[0043] The excitation light path control module is used to move the target surface laterally or axially, thereby obtaining multiple sets of laterally moving images and multiple sets of axially moving images of two different wavelength channels;

[0044] The fluorescence collection and filtering module is used to collect and filter fluorescence;

[0045] The light detection module is used to detect and process fluorescence;

[0046] The data acquisition and processing module is used to collect and process fluorescence detection conditions, multiple sets of lateral motion images, and multiple sets of axial motion images to achieve chromatic aberration correction for single-molecule localization super-resolution imaging.

[0047] Beneficial effects of the invention:

[0048] The present invention provides a method and system for chromatic aberration correction of single-molecule localization super-resolution imaging, which has the following advantages:

[0049] By using two different wavelengths of excitation light to sequentially excite the lateral chromatic aberration correction sample, and collecting two-wavelength channel image data at different lateral positions, and then combining local weighted average with iterative nearest neighbor point registration method for point registration, the correction of lateral chromatic aberration in multi-color single-molecule localization super-resolution imaging is achieved. By using two different wavelengths of excitation light to sequentially excite the axial chromatic aberration correction sample, and collecting two-wavelength channel image data at different axial positions, Gaussian fitting and least squares fitting are used to obtain the average deviation of the two channels at axial height and compensate for it, the correction of axial chromatic aberration in multi-color single-molecule localization super-resolution imaging is achieved, the registration accuracy of the system is improved, and the correction cost is relatively low and the correction speed is relatively fast. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the lateral chromatic aberration correction process of the present invention.

[0051] Figure 2 It is a schematic diagram of the axial chromatic aberration correction process of the present invention.

[0052] Figure 3 This is a Gaussian fitting peak position diagram of the two-channel axial height of the present invention.

[0053] Figure 4 It is a least square fitting curve diagram of the axial positions of the two channels of the present invention.

[0054] Figure 5 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0055] A method for chromatic aberration correction of single-molecule localization super-resolution imaging, the schematic diagram of its lateral chromatic aberration correction process is as follows Figure 1 As shown, the schematic diagram of the axial chromatic aberration correction process is as follows Figure 2 shown.

[0056] Lateral chromatic aberration correction involves the following steps:

[0057] S11: using two different wavelengths of excitation light to sequentially excite the lateral chromatic aberration correction sample on the glass slide, and obtaining pixel information and lateral coordinate information of multiple sets of lateral movement images of two different wavelength channels;

[0058] Specifically, a plurality of fluorescent microspheres may be selected as the sample for lateral chromatic aberration correction.

[0059] The two excitation lights of different wavelengths may preferably be: an excitation light of 647 nanometers and an excitation light of 561 nanometers.

[0060] When obtaining multiple sets of lateral motion images of two different wavelength channels, the exposure time of each frame of image can be preferably 100 milliseconds, and the field of view acquisition range of a single image is square micrometers.

[0061] The target surface can be moved laterally by using a two-dimensional translation stage to obtain multiple sets of laterally moving images of two different wavelength channels.

[0062] S12: determining the positioning accuracy of the lateral chromatic aberration correction sample in each wavelength channel based on pixel information of the multiple sets of lateral movement images of the two different wavelength channels, eliminating points where the positioning accuracy of the lateral chromatic aberration correction sample in each wavelength channel is greater than a set threshold, and eliminating misaligned points between different wavelength channels;

[0063] Since fluorescent microspheres may be bleached, aggregated, or blocked during actual imaging, mismatched point pairs must be removed first.

[0064] Specifically, when the points where the positioning accuracy of the lateral chromatic aberration correction sample in each wavelength channel is greater than the set threshold are eliminated,

[0065] The sample positioning accuracy of the lateral chromatic aberration correction of the two different wavelength channels can be calculated based on the pixel information of multiple sets of lateral movement images of the two different wavelength channels according to formula (1):

[0066] (1);

[0067] in: represents the sample positioning accuracy corrected for lateral chromatic aberration in two different wavelength channels, represents the expected value of the Gaussian distribution, represents the number of photons in the spot, Represents the pixel information of the horizontally shifted image of two different wavelength channels, Represents background noise.

[0068] After calculating the positioning accuracy of the lateral chromatic aberration correction sample for two different wavelength channels, it is compared with the set threshold. The points in each wavelength channel where the positioning accuracy of the lateral chromatic aberration correction sample is greater than the set threshold are eliminated, while the points in each wavelength channel where the positioning accuracy of the lateral chromatic aberration correction sample is less than or equal to the set threshold are retained.

[0069] The threshold here can be preferably set to 5 nanometers, so that more than 90% of the point pairs can be retained.

[0070] Specifically, the DBSCAN clustering algorithm based on adaptive distance change is used to eliminate misaligned points between different wavelength channels, including the following steps:

[0071] S121: The positioning points of all fluorescent microspheres in two different wavelength channels are taken as control point pairs, and the distance between the control point pairs formed by the fluorescent microspheres at the most edge positions in the two different wavelength channels is taken as the search radius. With the center of the image as the center of the circle, the scale factor is calculated according to formula (2):

[0072] (2);

[0073] in: represents the search radius, represents the scale factor, Indicates the distance from the current control point to the center of the image, represents the initial search radius;

[0074] Initial search radius The choice of is related to the threshold of positioning accuracy. In the system we are currently using, the drift caused by two consecutive frame acquisitions will not exceed 5 nanometers, and the threshold of positioning accuracy is limited to 5 nanometers. The positioning range of the same point at the center of the target surface between different channels will not exceed 25 nanometers, so the initial search radius It may preferably be 25 nanometers.

[0075] The scale factor The choice of is related to the lateral chromatic aberration of the system itself, and the farther away from the center of the target surface, the greater the lateral chromatic aberration. Therefore, by collecting fluorescent microspheres at the edge of the target surface and locating them separately in two channels, the distance between the control points is used as the threshold, and the distance between the control points is used as the threshold. For a target surface of square micrometers, this value is about 80 nanometers. This value is set as the search radius. The upper limit of , so the proportional factor can be calculated according to formula (2) .

[0076] S122: traverse all control points of one wavelength channel and calculate the search radius according to formula (2). Then, with the control point as the center of the circle, search within the calculated search radius to see whether there is a registration point in the other wavelength channel. If there is no registration point in the other wavelength channel, the control point is considered as a noise point and removed. If there are multiple registration points in the other wavelength channel, the control point and the multiple registration points are removed. If there is only one registration point in the other wavelength channel, the control point and the registration point are retained as a control point pair.

[0077] The above method makes full use of the physical property that lateral chromatic aberration increases with the centrifugal distance to pre-eliminate mismatched point pairs, thereby reducing the subsequent workload and improving the accuracy of point pair registration, which is conducive to improving the chromatic aberration correction accuracy of the system.

[0078] S13: Align the points retained in the two different wavelength channels to obtain a chromatic aberration correction mapping function, and apply the chromatic aberration correction mapping function to complete the lateral chromatic aberration correction of single-molecule localization super-resolution imaging;

[0079] Specifically, the chromatic aberration correction mapping function can be obtained by the following method:

[0080] S131: A second-order polynomial is used to establish the mapping relationship between the reference channel and the channel to be registered as formula (3):

[0081] (3);

[0082] in: Indicates that the control point is Axis coordinates, Indicates that the control point is Axis coordinates, represents the mapping function between the reference channel and the channel to be registered, represents the set of reference channel control points, represents the set of control points of the channel to be registered, express The axis direction mapping function is exist The derivative in the direction of the axis, express The axis direction mapping function is exist The derivative in the direction of the axis, express Axis direction mapping function The linear coefficient of express Axis direction mapping function The linear coefficient of express Axis direction mapping function The quadratic coefficient of express Axis direction mapping function The quadratic coefficient of express The cross-term coefficient of the axis direction mapping function, express The constant term coefficient of the axis direction mapping function, express Axis direction mapping function The linear coefficient of express Axis direction mapping function The linear coefficient of express Axis direction mapping function The quadratic coefficient of express Axis direction mapping function The quadratic coefficient of express The cross-term coefficient of the axis direction mapping function, express The constant term coefficient of the axial direction mapping function;

[0083] S132: Based on the reserved control point pairs, for any control point in the reference channel, find the control point closest to it in the channel to be registered, and calculate the square sum of the distances between the two control points. Based on the square sum of the distances between the two control points, calculate the error function according to formula (4):

[0084] (4);

[0085] in: represents the error function, Indicates the The influence weight of each control point, represents the sum of the squares of the distances between two control points, Indicates the The Euclidean distance between the control points and the registration point, represents the expected minimum target distance, Indicates the number of control points;

[0086] For any control point in the reference channel, the nearest control point can be found in the channel to be registered according to the following formula:

[0087] ;

[0088] in: Indicates the first The closest control point to the control point, Indicates the norm, Indicates the reference channel control points, Represents the coordinate index of the control point in the channel to be registered, Represents a function that takes the minimum value.

[0089] The sum of the squares of the distances between two control points can be calculated as follows: , The meaning of is the registration error between point pairs:

[0090] ;

[0091] Since the point pairs farther from the center are more affected by lateral chromatic aberration when correcting lateral chromatic aberration, the correction of these point pairs is the focus of registration, and the error function is calculated according to formula (4) based on the sum of the squares of the distances between the two control points.

[0092] The minimum target distance expected here Can be set to 5 nanometers.

[0093] It represents the uncertainty penalty between the registration point pairs, ensuring that the transformed point pair registration distance is as close as possible to the set final registration distance, while increasing the stability of the transformation.

[0094] Indicates the The influence weight of each control point can be defined as follows:

[0095] ;

[0096] in: represents the normalized distance, ;

[0097] in: Indicates that the target surface center is Axis coordinates, Indicates that the target surface center is Axis coordinates, Indicates the The control points are Axis coordinates, Indicates the The control points are Axis coordinates, Indicates the reference channel The distance between a control point and its nearest neighbor among all control points, Indicates the The distance from a control point to the center of the target surface.

[0098] S133: Iterate based on the constructed error function, and compare the relationship between the current control point error and the set threshold during the iteration process. If the current control point error is greater than the set threshold, change the coefficient in the mapping relationship formula (3) and recalculate the error function until the current control point error is less than or equal to the set threshold. Then, obtain the color difference correction mapping function and output the mapping relationship. If the current control point error is less than or equal to the set threshold, directly obtain the color difference correction mapping function and output the current mapping relationship.

[0099] The coefficients in the change mapping equation (3) include 、 、 、 、 、 、 、 、 、 、 、 .

[0100] The above point registration method combines the local weighted mean (LWM) with the iterative nearest neighbor point (ICP) algorithm, which is more suitable for the lateral chromatic aberration transformation of non-rigid bodies.

[0101] Optimized, before iterating based on the constructed error function in step S133, the initial value estimation and convergence optimization are first performed according to formula (5):

[0102] (5);

[0103] in: Indicates the The particle velocity at the iteration, Indicates the The particle velocity at the iteration, represents the inertia weight, represents the first learning factor, represents the first random number between 0 and 1, represents the historical optimal position of the current particle, that is, the individual optimal position, Indicates the The particle position at the iteration, Indicates the The particle position at the iteration, represents the second learning factor, represents a second random number between 0 and 1, It represents the best historical position of particles in the entire swarm, that is, the global optimal position.

[0104] By optimizing the initial value through the above particle swarm optimization algorithm, it is possible to avoid falling into the local optimum.

[0105] Furthermore, during the error function iteration in step S133, the registration index is accelerated as follows:

[0106] S1331: During the iteration process, a KD-tree index structure is constructed based on the retained control point pairs. Each node in the binary tree of the KD-tree index structure stores a data point, and the data points are divided along one dimension at a time. Data points that are less than or equal to the value of the data point in the dimension are divided into the left subtree, and data points that are greater than the value of the data point in the dimension are divided into the right subtree.

[0107] S1332: When performing a nearest neighbor search, first compare the data value of the current node with its value in the current dimension. If the value of the current data point is less than or equal to its value in the current dimension, search in the left subtree. If the value of the current data point is greater than its value in the current dimension, search in the right subtree and calculate the distance between the current node and the query point to find the nearest neighbor node.

[0108] S1333: Recursively search until a leaf node is reached. When backtracking, update the nearest neighbor node. Then search in another subtree and determine whether there is a nearest neighbor node. If there is no nearest neighbor node in the other subtree, save the nearest neighbor node updated when backtracking. If there is a nearest neighbor node in the other subtree, update the nearest neighbor node.

[0109] By adopting the above method of constructing the KD-tree index structure in iteration, the complexity of finding the nearest neighbor can be reduced, thereby improving the computational efficiency.

[0110] Longitudinal chromatic aberration correction includes the following steps:

[0111] S21: placing the axial chromatic aberration correction sample on a glass slide treated with poly-lysine, sequentially exciting the axial chromatic aberration correction sample using two different wavelengths of excitation light, and acquiring pixel information and axial height data of multiple sets of axial motion images of the two different wavelength channels;

[0112] Specifically, the axial chromatic aberration correction sample may preferably be a dual-color fluorescent secondary antibody.

[0113] The two excitation lights of different wavelengths may preferably be: an excitation light of 647 nanometers and an excitation light of 561 nanometers.

[0114] When obtaining multiple sets of axial motion images of two different wavelength channels, the exposure time of each frame of image can be preferably 10 milliseconds, and the field of view acquisition range of a single image can be preferably square micrometers.

[0115] S22: Gaussian fitting is performed on the distribution of multiple sets of axial height data for each wavelength channel to obtain the axial height data after fitting for two channels. The specific peak position of the axial height Gaussian fitting for the two channels is shown in the figure below. Figure 3 As shown, FIG1 represents the channel axial height Gaussian fitting peak position diagram corresponding to the excitation light of 561 nanometers, and the axial height corresponding to the Gaussian fitting peak is 203 nanometers.

[0116] 2 represents the channel axial height Gaussian fitting peak position diagram corresponding to the excitation light of 647 nm, and the axial height corresponding to the Gaussian fitting peak is 304 nm.

[0117] S23: Based on the axial height data of the two channels after fitting, a linear least squares fitting is used to establish the relationship between the axial chromatic aberration of the channels, thereby obtaining the average deviation of the two channels at different axial heights; the specific least squares fitting curve of the axial position of the two channels is shown in the figure below. Figure 4 As shown, the unit nm in the figure is nanometer.

[0118] The relationship between the axial chromatic aberration between channels established here is as follows:

[0119] ;

[0120] in: represents the axial height of the reference channel, represents the axial height of the channel to be registered, It represents the average deviation of the two channels at different axial heights.

[0121] S24: Compensate the average deviation of the two channels at different axial heights into the single-molecule localization super-resolution imaging chromatic aberration correction system to complete the single-molecule localization super-resolution imaging axial chromatic aberration correction.

[0122] A single-molecule localization super-resolution imaging chromatic aberration correction system is used to perform any of the above-mentioned single-molecule localization super-resolution imaging chromatic aberration correction methods. The system structure diagram is as follows: Figure 5 As shown, it includes an excitation light source module, an excitation light path control module, a sample, a fluorescence collection and filtering module, a light detection module, and a data acquisition and processing module;

[0123] The excitation light source module is used to excite the sample using excitation light of different wavelengths;

[0124] The samples include lateral chromatic aberration correction samples and axial chromatic aberration correction samples;

[0125] The excitation light path control module is used to move the target surface laterally or axially, thereby obtaining multiple sets of laterally moving images and multiple sets of axially moving images of two different wavelength channels;

[0126] The fluorescence collection and filtering module is used to collect and filter fluorescence;

[0127] The light detection module is used to detect and process fluorescence;

[0128] The data acquisition and processing module is used to collect and process fluorescence detection conditions, multiple sets of lateral motion images, and multiple sets of axial motion images to achieve chromatic aberration correction for single-molecule localization super-resolution imaging.

[0129] Specifically, the excitation light path control module includes a two-dimensional translation stage and a microscope axial adjustment device. The two-dimensional translation stage is used to move the target surface laterally, and the microscope axial adjustment device is used to move the target surface axially.

[0130] In summary, the present invention provides a method and system for correcting chromatic aberration in single-molecule localization super-resolution imaging, which combines local weighted averaging with an iterative nearest neighbor point registration method for point registration, thereby achieving correction of lateral chromatic aberration in multi-color single-molecule localization super-resolution imaging. Gaussian fitting and least squares fitting are used to obtain the average deviation of the two channels at axial height and compensate for it, thereby achieving correction of axial chromatic aberration in multi-color single-molecule localization super-resolution imaging, thereby improving the registration accuracy of the system, reducing the correction cost, and increasing the correction speed.

[0131] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for chromatic aberration correction in single-molecule localization super-resolution imaging, characterized by: Lateral chromatic aberration correction involves the following steps: S11: using two different wavelengths of excitation light to sequentially excite the lateral chromatic aberration correction sample on the glass slide, and obtaining pixel information and lateral coordinate information of multiple sets of lateral movement images of two different wavelength channels; The lateral chromatic aberration correction sample is a plurality of fluorescent microspheres, S12: determining the positioning accuracy of the lateral chromatic aberration correction sample in each wavelength channel based on pixel information of the multiple sets of lateral movement images of the two different wavelength channels, eliminating points where the positioning accuracy of the lateral chromatic aberration correction sample in each wavelength channel is greater than a set threshold, and eliminating misaligned points between different wavelength channels; The DBSCAN clustering algorithm based on adaptive distance change is used to eliminate misaligned points between different wavelength channels. The specific steps include the following: S121: The positioning points of all fluorescent microspheres in two different wavelength channels are taken as control point pairs, and the distance between the control point pairs formed by the fluorescent microspheres at the most edge positions in the two different wavelength channels is taken as the search radius. With the center of the image as the center of the circle, the scale factor is calculated according to formula (2): （2）； in: represents the search radius, represents the scale factor, Indicates the distance from the current control point to the center of the image, represents the initial search radius; S122: traverse all control points of one wavelength channel and calculate the search radius according to formula (2). Then, with the control point as the center of the circle, search within the calculated search radius to see whether there is a registration point in the other wavelength channel. If there is no registration point in the other wavelength channel, the control point is considered as a noise point and removed. If there are multiple registration points in the other wavelength channel, the control point and the multiple registration points are removed. If there is only one registration point in the other wavelength channel, the control point and the registration point are retained as a control point pair. S13: Align the points retained in the two different wavelength channels to obtain a chromatic aberration correction mapping function, and apply the chromatic aberration correction mapping function to complete the lateral chromatic aberration correction of single-molecule localization super-resolution imaging; The chromatic aberration correction mapping function is obtained by the following method: S131: A second-order polynomial is used to establish the mapping relationship between the reference channel and the channel to be registered as formula (3): （3）； in: Indicates that the control point is Axis coordinates, Indicates that the control point is Axis coordinates, represents the mapping function between the reference channel and the channel to be registered, represents the set of reference channel control points, represents the set of control points of the channel to be registered, express Axis direction mapping function, express Axis direction mapping function, express Axis direction mapping function The linear coefficient of express Axis direction mapping function The linear coefficient of express Axis direction mapping function The quadratic coefficient of express Axis direction mapping function The quadratic coefficient of express The cross-term coefficient of the axis direction mapping function, express The constant term coefficient of the axis direction mapping function, express Axis direction mapping function The linear coefficient of express Axis direction mapping function The linear coefficient of express Axis direction mapping function The quadratic coefficient of express Axis direction mapping function The quadratic coefficient of express The cross-term coefficient of the axis direction mapping function, express The constant term coefficient of the axial direction mapping function; S132: Based on the reserved control point pairs, for any control point in the reference channel, find the control point closest to it in the channel to be registered, and calculate the square sum of the distances between the two control points. Based on the square sum of the distances between the two control points, calculate the error function according to formula (4): （4）； in: represents the error function, Indicates the The influence weight of each control point, represents the sum of the squares of the distances between two control points, Indicates the The Euclidean distance between the control points and the registration point, represents the expected minimum target distance, Indicates the number of control points; S133: Iterate based on the constructed error function, and compare the relationship between the current control point error and the set threshold during the iteration process. If the current control point error is greater than the set threshold, change the coefficient in the mapping relationship formula (3) and recalculate the error function until the current control point error is less than or equal to the set threshold. Then, obtain the color difference correction mapping function and output the mapping relationship. If the current control point error is less than or equal to the set threshold, directly obtain the color difference correction mapping function and output the current mapping relationship. Longitudinal chromatic aberration correction includes the following steps: S21: placing an axial chromatic aberration correction sample on a glass slide treated with poly-lysine, sequentially exciting the sample with two different wavelengths of excitation light, and acquiring pixel information and axial height data of multiple sets of axial motion images in two different wavelength channels. The axial chromatic aberration correction sample is a dual-color fluorescent secondary antibody; S22: performing Gaussian fitting on the distribution of multiple sets of axial height data of each wavelength channel to obtain fitted axial height data of two channels; S23: Based on the fitted axial height data of the two channels, a linear least squares fit is used to establish the relationship between the axial chromatic aberration of the channels, thereby obtaining the average deviation of the two channels at different axial heights; S24: Compensate the average deviation of the two channels at different axial heights into the single-molecule localization super-resolution imaging chromatic aberration correction system to complete the single-molecule localization super-resolution imaging axial chromatic aberration correction.

2. The method for chromatic aberration correction in single-molecule localization super-resolution imaging according to claim 1, characterized in that: The two excitation lights of different wavelengths in step S11 and step S21 are respectively: an excitation light of wavelength 647 nanometers and an excitation light of wavelength 561 nanometers.

3. The method for chromatic aberration correction in single-molecule localization super-resolution imaging according to claim 1, characterized in that: When obtaining multiple sets of lateral motion images of two different wavelength channels in step S11, the exposure time of each frame is 100 milliseconds, and the field of view acquisition range of a single image is square micrometers, when obtaining multiple sets of axial motion images of two different wavelength channels in step S21, the exposure time of each frame is 10 milliseconds, and the field of view acquisition range of a single image is square micrometers.

4. The method for chromatic aberration correction in single-molecule localization super-resolution imaging according to claim 1, characterized in that: In step S12, based on the pixel information of the multiple groups of lateral movement images of the two different wavelength channels, the positioning accuracy of the lateral chromatic aberration correction sample of the two different wavelength channels is calculated according to formula (1): （1）； in: represents the sample positioning accuracy corrected for lateral chromatic aberration in two different wavelength channels, represents the expected value of the Gaussian distribution, represents the number of photons in the spot, Represents the pixel information of the horizontally shifted image of two different wavelength channels, Represents background noise.

5. The method for correcting chromatic aberration in single-molecule localization super-resolution imaging according to claim 1, wherein: Before iterating based on the constructed error function in step S133, initial value estimation and convergence optimization are performed according to formula (5): （5）； in: Indicates the The particle velocity at the iteration, Indicates the The particle velocity at the iteration, represents the inertia weight, represents the first learning factor, represents the first random number between 0 and 1, represents the historical optimal position of the current particle, Indicates the The particle position at the iteration, Indicates the The particle position at the iteration, represents the second learning factor, represents a second random number between 0 and 1, Represents the historical optimal position of the particle in the entire swarm.

6. The method for chromatic aberration correction in single-molecule localization super-resolution imaging according to claim 1, characterized in that: During the error function iteration in step S133, the registration index is accelerated as follows: S1331: During the iteration process, a KD-tree index structure is constructed based on the retained control point pairs. Each node in the binary tree of the KD-tree index structure stores a data point, and the data points are divided along one dimension at a time. Data points that are less than or equal to the value of the data point in the dimension are divided into the left subtree, and data points that are greater than the value of the data point in the dimension are divided into the right subtree. S1332: When performing a nearest neighbor search, first compare the data value of the current node with its value in the current dimension. If the value of the current data point is less than or equal to its value in the current dimension, search in the left subtree. If the value of the current data point is greater than its value in the current dimension, search in the right subtree and calculate the distance between the current node and the query point to find the nearest neighbor node. S1333: Recursively search until a leaf node is reached. When backtracking, update the nearest neighbor node. Then search in another subtree and determine whether there is a nearest neighbor node. If there is no nearest neighbor node in the other subtree, save the nearest neighbor node updated when backtracking. If there is a nearest neighbor node in the other subtree, update the nearest neighbor node.

7. A single-molecule localization super-resolution imaging chromatic aberration correction system, configured to perform the single-molecule localization super-resolution imaging chromatic aberration correction method according to any one of claims 1 to 6, characterized in that: It includes excitation light source module, excitation light path control module, sample, fluorescence collection and filtering module, light detection module, and data acquisition and processing module; The excitation light source module is used to excite the sample using excitation light of different wavelengths; The samples include lateral chromatic aberration correction samples and axial chromatic aberration correction samples; The excitation light path control module is used to move the target surface laterally or axially, thereby obtaining multiple groups of laterally moved images and multiple groups of axially moved images of two different wavelength channels; The fluorescence collection and filtering module is used to collect and filter fluorescence; The light detection module is used to detect and process fluorescence; The data acquisition and processing module is used to acquire and process fluorescence detection conditions, multiple groups of lateral motion images, and multiple groups of axial motion images to achieve chromatic aberration correction for single-molecule localization super-resolution imaging.

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