A resonance-driven scanner trajectory detection method based on reconstructed image optimization

Abstract

The application belongs to the technical field of scanning imaging, and discloses a resonance driving type scanner trajectory detection method based on reconstruction image optimization, which comprises the following steps: initializing scanning trajectory parameters of the resonance driving type scanner, and setting the boundary of the scanning trajectory parameters; after the current scanning trajectory parameters are corrected within the boundary, the corrected scanning trajectory parameters are brought into a trajectory model to obtain a simulation trajectory under the current iteration number; an image is reconstructed by using the current simulation trajectory and real-time imaging data, and an image artifact evaluation index is calculated; whether the preset iteration termination condition is reached is judged; if not, the current image artifact evaluation index is taken as the optimization target, the current scanning trajectory parameters are updated, and the next iteration is performed; if yes, the simulation trajectory under the current iteration number and the corresponding reconstructed image are output, and the simulation trajectory and the reconstructed image are subjected to affine deformation repair. The application can improve the detection precision of the scanning trajectory and the quality of the reconstructed image, and reduce the implementation complexity and cost.

Description

Technical Field

[0001] This invention belongs to the field of scanning imaging technology, and more specifically, relates to a resonance-driven scanner trajectory detection method based on reconstructed image optimization. Background Technology

[0002] Resonant-driven scanners, such as fiber optic scanners or MEMS galvanometers, offer advantages like simple drive control, continuous scanning trajectory, and high scanning speed, making them an important component of miniature microscopic imaging devices such as confocal endoscopes. However, resonant-driven scanners are sensitive to environmental and structural characteristics. When the environment changes or the scanner mechanism ages, the scanning trajectory alters. If the scanner trajectory is not recalibrated, image quality degradation will occur.

[0003] In existing technologies, the scanning imaging signal can be physically encoded using an aperture stop mirror. The high-level signals in the imaging signal are decoded to obtain the scanner's frequency, phase, and amplitude. Then, a negative feedback circuit corrects the scanning trajectory to match the target trajectory, reconstructing a distortion-free image. This method relies on hardware, and the additional feedback circuit increases the system's complexity and cost. Another approach addresses the issue that pixel interlacing artifacts caused by trajectory phase drift increase high-frequency components in the image. It proposes using the summation of the image's frequency domain power spectrum as an evaluation metric, employing an ergodic algorithm to find the optimal phase for trajectory reconstruction, thus completing image artifact repair. However, this method, relying on a single-variable ergodic optimization algorithm, can only optimize the phase in one direction, making it unsuitable for situations where phase optimization is needed in two orthogonal directions simultaneously. Furthermore, it doesn't consider the influence of amplitude, limiting its applicability. Additionally, the detection accuracy of the scanning trajectory is low, resulting in low image accuracy reconstructed based on the detected trajectory.

[0004] Furthermore, mechanical cross-coupling refers to the non-planar resonance phenomenon where a vibration system generates coupled vibrations in directions orthogonal to the excitation direction. For biaxial resonant driven scanners, the impact of mechanical coupling on the scanning trajectory can be eliminated through direct correction. However, for uniaxial resonant driven scanners, especially under Lissajous scanning trajectories, mechanical cross-coupling severely affects the scanning trajectory and cannot be eliminated through direct correction. Although the Lissajous scanning trajectory of a uniaxial resonant driven scanner with mechanical cross-coupling is also periodically repetitive, it does not conform to the mathematical description of a standard Lissajous trajectory. In practical applications, due to limitations in manufacturing processes, assembly precision, and size, mechanical cross-coupling in Lissajous micro-scanners is difficult to avoid. For such Lissajous scanning trajectories with mechanical cross-coupling, direct detection methods are generally used, such as directly measuring the time-series trajectory using sensors like position-sensitive detectors (PSDs). This method not only increases system cost and size but is also constrained by system errors, detection accuracy, and noise, resulting in less than ideal image reconstruction results. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a resonant driven scanner trajectory detection method based on reconstructed image optimization. Its purpose is to acquire the high-precision scanning trajectory of the resonant driven scanner in real time and reduce the implementation complexity and cost.

[0006] To achieve the above objectives, the present invention provides a resonant-driven scanner trajectory detection method based on reconstructed image optimization, comprising:

[0007] S1. Initialize the scanning trajectory parameters of the resonant-driven scanner and set the boundaries of the scanning trajectory parameters; wherein, the scanning trajectory parameters include the phase and amplitude components of the scanning trajectory in the orthogonal direction of the scanner;

[0008] S2. After boundary correction of the current scanning trajectory parameters, substitute them into the trajectory model to obtain the simulated trajectory at the current iteration number i; reconstruct the image based on the current simulated trajectory and the acquired real-time imaging data;

[0009] S3. Calculate the artifact evaluation index s of the current reconstructed image. i The artifact evaluation index s i Used to measure the significance of image stagger artifacts caused by inaccurate simulated trajectories;

[0010] S4. Determine whether the preset iteration termination condition has been met. If not, then use the artifact evaluation index s. i Minimize the optimization target, optimize the current scanning trajectory parameters, let i = i + 1, and jump to S2; if so, output the simulated trajectory and the corresponding reconstructed image under the current iteration number.

[0011] Furthermore, under Lissajous scanning, the trajectory model of the resonant-driven scanner is as follows:

[0012] x(t)=a x sin(2πf1t+θ 1x )+b x sin(2πf2t+θ 2x )

[0013] y(t)=b y sin(2πf1t+θ 1y )+a y sin(2πf2t+θ 2y )

[0014] Where x(t) and y(t) represent the trajectory coordinates at time t, and t is the sampling time of the imaging device where the scanner is located; f1 and f2 are the resonant frequencies of the scanner in the x and y directions under Lissajous scanning, and the x and y directions constitute the orthogonal directions of the scanner; a x and a y b represents the amplitude of the scanner in the x and y directions, respectively. y and b x θ represents the amplitude of the coupled vibration generated by the scanner in the x-direction and the amplitude of the coupled vibration generated in the y-direction, respectively; 1x and θ 2y θ represents the phase of the scanner in the x and y directions, respectively. 1y and θ 2x These are the phases of the coupled vibrations generated by the scanner in the x-direction and the phases of the coupled vibrations generated in the y-direction, respectively.

[0015] Furthermore, the artifact evaluation index s i for:

[0016]

[0017] Where M and N are the number of rows and columns of the reconstructed image, respectively, and u and v are the coordinates in the frequency domain; |F i (u,v)| represents the frequency domain amplitude spectrum obtained by discrete Fourier transform and modulus taking of the reconstructed image f(x,y) in the i-th iteration, where D u,v Represents the weight matrix;

[0018] Alternatively, the artifact evaluation index s i for:

[0019]

[0020] Where p(r) represents the frequency domain amplitude spectrum |F i The distribution ratio of the r-th gray level in the gray level set (u,v)|.

[0021] Furthermore, following S4, affine deformation repair is performed on the output simulated trajectory and the corresponding reconstructed image, specifically including:

[0022] In the imaging system where the scanner is located, a reference object with a known shape is preset. The linear transformation matrix corresponding to the affine deformation is calculated based on the field of view before and after imaging of the reference object. The simulated trajectory and the reconstructed image are linearly transformed using the linear transformation matrix to obtain the simulated trajectory and reconstructed image after affine deformation repair.

[0023] Furthermore, the reference object with a known shape is a target or an aperture that limits the imaging field of view, and the target or aperture is positioned in front of or behind the imaging objective.

[0024] Furthermore, when reconstructing the image using the current simulated trajectory and the acquired real-time imaging data, the image reconstruction algorithm employs a grid interpolation algorithm; the interpolation strategy of the grid interpolation algorithm is nearest neighbor interpolation, linear interpolation, quadratic spline interpolation, or natural interpolation.

[0025] Furthermore, a multi-objective optimization algorithm is used to optimize the current scanning trajectory parameters. The multi-objective optimization algorithm is either a swarm optimization algorithm or a stochastic gradient descent algorithm with global optimization capability.

[0026] The iteration termination condition is determined by the optimization strategy of the multi-objective optimization algorithm, including the artifact evaluation index s after multiple iterations. i If there is no change within the set threshold range, the preset maximum number of iterations is reached, or an anomaly occurs in the current trajectory.

[0027] The present invention also provides a resonant-driven scanner trajectory detection system based on reconstructed image optimization, including a computer-readable storage medium and a processor;

[0028] The computer-readable storage medium is used to store executable instructions;

[0029] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the resonant-driven scanner trajectory detection method based on reconstructed image optimization described above.

[0030] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the resonant-driven scanner trajectory detection method based on reconstructed image optimization as described in any of the preceding claims.

[0031] The present invention also provides a computer program product, including a computer program that, when the computer program is run on a computer, causes the computer to execute the resonant-driven scanner trajectory detection method based on reconstructed image optimization as described above.

[0032] In summary, the above-described technical solutions conceived in this invention can achieve the following beneficial effects:

[0033] (1) The resonant-driven scanner trajectory detection method based on reconstructed image optimization of the present invention achieves image restoration (image reconstruction) by modifying the trajectory, and the image restoration and trajectory detection are completed simultaneously. In the process of trajectory detection and image reconstruction, a multi-iterative optimization method is adopted, with the goal of minimizing the artifact evaluation index of the current image, while optimizing each phase and amplitude component of the scanning trajectory in the orthogonal direction of the scanner. High-precision scanning trajectory of the resonant-driven scanner is obtained with the help of real-time imaging data. Compared with other trajectory detection methods, the trajectory detection technology of the present invention does not rely on hardware detection equipment, is simple to implement, has high detection accuracy, good real-time performance, and low cost.

[0034] (2) Preferably, in this invention, considering that the mechanical coupling phenomenon cannot be corrected due to system limitations for single-axis resonant driven scanners under Lissajous scanning trajectories, a Lissajous scanning trajectory model considering mechanical coupling is provided. This trajectory model is based on the vibration equation of the vibration system under excitation and boundary conditions when the scanner vibrates. This invention analyzes the vibration equation of the vibration system under excitation and boundary conditions when the scanner vibrates. The two resonant frequencies of the scanner in the orthogonal directions will generate corresponding coupling terms b. y sin(2πf1t+θ 1y ) and b x sin(2πf2t+θ 2x Therefore, a Lissajous scanning trajectory model considering mechanical coupling was constructed. This trajectory model simultaneously considers eight parameters to be optimized under Lissajous scanning: the amplitude and phase of the scanner in the orthogonal direction, as well as the amplitude and phase of the corresponding coupling terms. Based on an iterative optimization method, trajectory detection is indirectly achieved using imaging functionality, eliminating the need for additional hardware such as a position-sensitive detector (PSD), thus reducing system cost, minimizing size, and broadening applicability, especially in confined spaces. Furthermore, the detected trajectory more closely approximates the actual trajectory, significantly reducing the requirements for scanner design and manufacturing assembly precision. Additionally, while optimizing the four phase terms, correction of synchronization clock errors was also achieved.

[0035] (3) Preferably, the artifact evaluation index constructed based on the average value of the dot product of the frequency domain amplitude spectrum and the weight matrix of the current reconstructed image considers the frequency distribution ratio information of the image's frequency domain amplitude spectrum. Compared with directly calculating the average amplitude, when optimizing based on the minimum value of this artifact evaluation index as the optimization objective, the convergence region near the optimal value is larger, which is beneficial to improving the optimization speed. The artifact evaluation index constructed based on information entropy considers the intensity ratio information of the image's frequency domain amplitude spectrum, is less affected by the image structure, and is more suitable for situations where the imaging target structure is complex.

[0036] (4) Furthermore, considering that the artifact evaluation index constructed in this invention cannot evaluate the field deformation of the image, and that it was found during the optimization process that the constructed evaluation index would also introduce a certain degree of field deformation, after completing the image artifact repair, it also includes solving the linear transformation matrix of the field deformation based on the field prior information, completing the affine deformation repair of the simulated trajectory and the reconstructed image, and further improving the detection accuracy of the scanning trajectory and the quality of the reconstructed image.

[0037] (5) Preferably, in S4, considering that the present invention is a multi-parameter optimization and the image information interferes with the image degradation index, the optimization process may result in local optima. Therefore, the multi-objective optimization algorithm is selected as either the swarm optimization algorithm or the stochastic gradient descent algorithm. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of a resonant-driven scanner trajectory detection method based on reconstructed image optimization in an embodiment of the present invention.

[0039] Figure 2 This is a flowchart of the resonance-driven scanner trajectory detection method based on reconstructed image optimization in an embodiment of the present invention.

[0040] Figure 3 This relates to the ideal trajectory of the Lissajous microscanner and the non-ideal trajectory that generates mechanical cross-coupling.

[0041] Figure 4 To illustrate how the method described in this invention can be applied to a fiber optic scanning confocal endoscope probe to achieve the imaging results obtained during image degradation restoration, figures 1-8 represent... Figure 4 The sequence number of the neutron diagram. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0043] Example 1

[0044] like Figures 1-2 As shown, this embodiment of the invention provides a resonant-driven scanner trajectory detection method based on reconstructed image optimization, mainly including:

[0045] S1. Initialization of scanning trajectory model parameters: Initialize the scanning trajectory parameters of the resonant-driven scanner randomly or based on prior values, set the boundaries of the scanning trajectory parameters, and preset the maximum number of iterations MaxIteration; wherein, the scanning trajectory parameters include the phase and amplitude components of the scanning trajectory in the orthogonal direction of the scanner;

[0046] S2. Image reconstruction based on simulated trajectory: After boundary correction of the current scanning trajectory parameters, they are substituted into the trajectory model and normalized to obtain the simulated trajectory at the current iteration number i; the image is reconstructed based on the current simulated trajectory and the acquired real-time imaging data.

[0047] S3. Calculate the evaluation index of image artifacts: Calculate the artifact evaluation index s of the current reconstructed image. i The artifact evaluation index is a statistical index of image frequency domain information, used to measure the significance of image stagger artifacts caused by inaccurate simulated trajectories.

[0048] S4. Iteratively optimize the simulated trajectory parameters: Determine whether the preset iteration termination condition has been met. If not, take the minimum current artifact evaluation index as the optimization objective, use a multi-objective optimization algorithm to update the current scanning trajectory parameters, let i = i + 1, and jump to S2. If yes, output the simulated trajectory and the corresponding reconstructed image under the current iteration number to complete the pixel interlacing artifact repair.

[0049] As a further design of the present invention, after image artifact repair is completed, it also includes:

[0050] S5. Perform affine deformation repair on the output simulated trajectory and the corresponding reconstructed image to obtain the simulated trajectory and the corresponding reconstructed image after affine deformation repair.

[0051] As a preferred option, in S1, the boundary of the trajectory parameter refers to the range of values ​​for the trajectory parameter. It is set according to the actual empirical values ​​of the trajectory model to exclude unreasonable situations and reduce computational costs.

[0052] Those skilled in the art will know that in S2, boundary correction is required after each iteration of trajectory parameter update. When the amplitude in the updated trajectory parameter exceeds the boundary value, it is made equal to the boundary value. When the phase exceeds the boundary value, it is made ±2kπ (k is an integer) and converted into an equivalent phase value within the phase range.

[0053] The trajectory model is constructed based on the vibration characteristics of the scanner. Optionally, the trajectory model can be a Lissajous scanning trajectory model, a spiral scanning trajectory model, etc.

[0054] As a preferred embodiment of the invention, a Lissajous scanning trajectory model considering mechanical coupling is provided, as shown in the following formula:

[0055] x(t)=ax sin(2πf1t+θ 1x )+b x sin(2πf2t+θ 2x )

[0056] y(t)=b y sin(2πf1t+θ 1y )+a y sin(2πf2t+θ 2y )

[0057] Where x(t) and y(t) represent the trajectory coordinates at time t, and t is the sampling time of the imaging device where the scanner is located; f1 and f2 are the two resonant frequencies of the scanner in the orthogonal directions under Lissajous scanning, that is, f1 is the resonant frequency of the scanner in the x-direction and f2 is the resonant frequency of the scanner in the y-direction, and the x-direction and y-direction constitute two mutually orthogonal directions of the scanner; a x b x a y b y θ 1x θ 1y θ 2x θ 2y For the eight parameters to be optimized related to the driving load, scanner mechanical characteristics, boundary conditions, etc., a x and a y b represents the amplitude of the scanner in the x and y directions, respectively. y and b x These represent the amplitudes of the coupled vibrations generated in the x-direction and the y-direction, respectively; θ 1x and θ 2y θ represents the phase of the scanner in the x and y directions, respectively. 1y and θ 2x These are the phases of the coupled vibrations generated in the x-direction and the y-direction, respectively; that is, b y sin(2πf1t+θ 1y ) represents the coupled vibration generated by the scanner in the x-direction, b x sin(2πf2t+θ 2x The image shows the coupled vibrations generated by the scanner in the y-direction; each parameter update requires domain constraint correction. The ideal trajectory and the non-ideal trajectory that produces mechanical cross-coupling are shown below. Figure 3 As shown.

[0058] In this embodiment of the invention, the resonant-driven scanner is regarded as a stable and continuous vibration system. The scanning trajectory model is solved based on the vibration equation of the scanner. The actual scanning trajectory of the scanner under different working conditions (changing the excitation, structural parameters, etc. of the scanner) is collected using a position-sensitive detector. The trajectory model is used to fit each set of trajectory data, and the correctness of the trajectory model is verified by correlation analysis.

[0059] In S2, the trajectory is normalized to facilitate image reconstruction. After translating the trajectory and scaling each coordinate (x,y) on the trajectory proportionally, the maximum value of the generated simulated trajectory is equal to 1 and the minimum value is equal to 0.

[0060] In S2, the real-time imaging data is a one-dimensional grayscale signal obtained by isochronous sampling of the imaging device where the scanner is located. The imaging target in this embodiment of the invention has structural or texture information, and in order to ensure the accuracy of the trajectory detection effect, the imaging result needs to have a certain signal-to-noise ratio.

[0061] Preferably, S2 also includes preprocessing of the real-time imaging data, including frame cropping (fractionating the real-time imaging data into individual frames), noise reduction, normalization, and other operations.

[0062] Specifically, in S2, the image is reconstructed using the current simulated trajectory and the acquired real-time imaging data, employing an image reconstruction algorithm. Preferably, the image reconstruction algorithm uses a grid interpolation algorithm, and the interpolation strategy can be nearest neighbor interpolation, linear interpolation, quadratic spline interpolation, natural interpolation, etc.

[0063] Preferably, in S3, the frequency domain amplitude spectrum obtained after the current reconstructed image undergoes discrete Fourier transform and modulus calculation is calculated, and the average value of the dot product of this frequency domain amplitude spectrum and the weight matrix is ​​used as the current image artifact evaluation index s. i The weight matrix is ​​constructed by summing the p-th powers of the distances from each point in the frequency domain amplitude spectrum to the center of the spectrum along the horizontal and vertical axes, and then taking the negative values; the corresponding calculation formula is:

[0064]

[0065] Where M and N are the number of rows and columns of the reconstructed image, respectively, and u and v are the coordinates in the frequency domain; |F i (u,v)| represents the frequency domain amplitude spectrum of the reconstructed image f(x,y) obtained by discrete Fourier transform and modulus taking in the i-th iteration. The calculation formula is as follows:

[0066]

[0067] Among them, Re(F i (u,v)) and Im(F) i (u,v) are respectively Fi The real and imaginary parts of (u,v); F i (u,v) represents the discrete Fourier transform of the reconstructed image f(x,y).

[0068] D u,v The weight matrix is ​​composed of the negative sum of the p-th powers of the distances from each point (u,v) in the frequency domain amplitude spectrum to the center of the spectrum on the horizontal and vertical axes. The smaller the value, the more low-frequency components there are in the frequency domain.

[0069]

[0070] Preferably, p can be 1.

[0071] Preferably, the artifact evaluation index s in the embodiments of the present invention i It can also be expressed as:

[0072]

[0073] Where p(r) represents the frequency domain amplitude spectrum |F i The distribution ratio of the r-th gray level in the gray level set (u,v)|.

[0074] As a preferred option, in S4, considering that the present invention involves multi-parameter optimization and that image information interferes with the image degradation index, the optimization process may result in local optima. Therefore, the multi-objective optimization algorithm is selected as either a swarm optimization algorithm or a stochastic gradient descent algorithm, which has global optimization capabilities.

[0075] The iteration termination condition is determined based on the selected global optimization algorithm, including the image artifact evaluation index s after multiple iterations. i The following conditions may apply: no change within the set threshold range, reaching the preset maximum number of iterations, or anomalies appearing in the current trajectory.

[0076] As a further design of the present invention, the image artifact evaluation index constructed in the present invention cannot evaluate the field-of-view deformation of the image. Furthermore, during the optimization process, it was found that the constructed evaluation index would also introduce a certain degree of field-of-view deformation. Therefore, after completing the image artifact repair, an affine deformation repair process is also included. Affine deformation repair refers to performing affine transformations (rotation, shearing, and scaling transformations) on the optimized trajectory and image to repair the overall geometric deformation of the field of view. This deformation will not produce staggered artifacts.

[0077] In S5, affine deformation repair is performed on the output simulated trajectory and the corresponding reconstructed image, including:

[0078] In the imaging system, a reference object with a known shape is preset. The linear transformation matrix corresponding to the affine deformation is calculated based on the field of view before and after imaging of the reference object. The output simulated trajectory and the corresponding reconstructed image are linearly transformed using the linear transformation matrix to obtain the simulated trajectory and reconstructed image after affine deformation repair.

[0079] Preferably, the reference object with a known shape is a target or an aperture that limits the imaging field of view, and the target or aperture is set in front of or behind the imaging objective.

[0080] Considering that the field of view of the image after artifact restoration in this invention is generally obliquely elliptical, the reference object with a known shape in this invention is preferably a circular aperture. The circular aperture is set in front of the imaging objective lens. Based on the elliptical field of view after imaging with the circular aperture and the circular field of view before imaging, the linear transformation matrix corresponding to the affine deformation is calculated. The output simulated trajectory and the corresponding reconstructed image are linearly transformed by the linear transformation matrix to obtain the simulated trajectory and reconstructed image after affine deformation restoration.

[0081] The method of the present invention will now be applied to a fiber optic scanning confocal endoscope system, wherein the imaging sample is a resolution version, and the resolution version also serves as a reference object with a known shape.

[0082] (1) Imaging data preprocessing: The excitation signals of the scanner are known information, with frequencies of f1 = 2408 Hz and f2 = 2304 Hz. The imaging parameters are set to a frame rate of 8 fps, a sampling rate of 10 MHz, and a single frame data length of 1.25 M samples. After acquiring the single frame data, sliding window Gaussian filtering and normalization are performed sequentially to obtain standardized imaging data.

[0083] (2) Parameter initialization: When the reconstruction result of the pre-stored trajectory degrades significantly or there is no pre-stored trajectory, eight initial parameter values ​​for the trajectory model can be randomly generated. Based on experience, the two frequencies occupy the main components in the orthogonal directions, and a is limited. x a y As the principal component, its value range is [0.9, 1]; b x b y As a secondary component (coupling component), its value range is [0, 0.2], θ 1x θ 1y θ 2x θ 2y The value range is [0, 2π]. Since no pre-stored trajectory was used for this correction, a set of trajectory parameters was randomly generated, and the reconstruction result is... Figure 4As shown in subplot number 1, it can be seen that the reconstructed image suffers from severe degradation when no iterations are performed. The trajectory parameters generated randomly or updated in subsequent iterations require boundary correction. Conversely, when the reconstruction result of the pre-stored trajectory does not suffer from severe degradation, the pre-stored scan trajectory can be used as the initial value, reducing computation time. The maximum number of iterations, MaxIteration, is set to 1000.

[0084] (3) Substitute the formula for the mechanical coupling trajectory of the Lissajous microscanner into the formula to generate the simulated trajectory, and then normalize it.

[0085] (4) Using a general grid interpolation algorithm, the interpolation strategy uses nearest neighbor interpolation to reconstruct the degraded image; and calculate the artifact evaluation index of the current image.

[0086] (5) The Adam optimization algorithm is used to update the trajectory parameters. The loop termination condition is determined when: the image artifact evaluation index remains unchanged within a set threshold range after 50 iterations or the maximum iteration count of 1000 is reached. If either condition is met, the loop exits, and the simulated trajectory and corresponding reconstructed image at the current iteration count are output, completing the image artifact repair. Several intermediate optimization processes are shown in the algorithm correction process. Figure 4 As shown in sub-images 2-6, the image is clearer than that of sub-image 1. The inverse matrix of the linear transformation is calculated using prior information from the resolution version, and then applied to... Figure 4 The subgraph with index 7 in the middle is obtained as follows: Figure 4 The resolution imaging result is shown in number 8. The results demonstrate that the method of this invention can achieve the function of detecting the true trajectory of the scanner, and the reconstructed image quality is good.

[0087] Example 2

[0088] This invention provides a resonant-driven scanner trajectory detection system based on reconstructed image optimization, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the resonant-driven scanner trajectory detection method based on reconstructed image optimization in Embodiment 1 above.

[0089] The relevant technical solutions are the same as above, and will not be repeated here.

[0090] Example 3

[0091] This invention provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the resonant-driven scanner trajectory detection method based on reconstructed image optimization in Embodiment 1 above.

[0092] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0093] The relevant technical solutions are the same as above, and will not be repeated here.

[0094] Example 4

[0095] This application provides a computer program product, including a computer program that, when run on a computer, causes the computer to perform the steps of the resonant-driven scanner trajectory detection method based on reconstructed image optimization in Embodiment 1 above.

[0096] The relevant technical solutions are the same as above, and will not be repeated here.

[0097] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A resonance-driven scanner trajectory detection method based on reconstructed image optimization, characterized in that, include: S1. Initialize the scanning trajectory parameters of the resonant-driven scanner and set the boundaries of the scanning trajectory parameters; wherein, the scanning trajectory parameters include the phase and amplitude components of the scanning trajectory in the orthogonal direction of the scanner; S2. After boundary correction of the current scanning trajectory parameters, substitute them into the trajectory model to obtain the simulated trajectory at the current iteration number i; reconstruct the image based on the current simulated trajectory and the acquired real-time imaging data; S3. Calculate the artifact evaluation index s of the current reconstructed image. i The artifact evaluation index s i Used to measure the significance of image stagger artifacts caused by inaccurate simulated trajectories; S4. Determine whether the preset iteration termination condition has been met. If not, then use the artifact evaluation index s. i Minimize the optimization target, optimize the current scanning trajectory parameters, let i = i + 1, and jump to S2; if so, output the simulated trajectory and the corresponding reconstructed image under the current iteration number.

2. The resonant-driven scanner trajectory detection method based on reconstructed image optimization according to claim 1, characterized in that, Under Lissajous scanning, the trajectory model of the resonant-driven scanner is as follows: x(t)=a x sin(2πf1t+θ 1x )+b x sin(2πf2t+θ 2x ) y(t)=b y sin(2πf1t+θ 1y )+a y sin(2πf2t+θ 2y ) Where x(t) and y(t) represent the trajectory coordinates at time t, and t is the sampling time of the imaging device where the scanner is located; f1 and f2 are the resonant frequencies of the scanner in the x and y directions under Lissajous scanning, and the x and y directions constitute the orthogonal directions of the scanner; a x and a y b represents the amplitude of the scanner in the x and y directions, respectively. y and b x θ represents the amplitude of the coupled vibration generated by the scanner in the x-direction and the amplitude of the coupled vibration generated in the y-direction, respectively; 1x and θ 2y θ represents the phase of the scanner in the x and y directions, respectively. 1y and θ 2x These are the phases of the coupled vibrations generated by the scanner in the x-direction and the phases of the coupled vibrations generated in the y-direction, respectively.

3. The resonant-driven scanner trajectory detection method based on reconstructed image optimization according to claim 1 or 2, characterized in that, The artifact evaluation index s i for: Where M and N are the number of rows and columns of the reconstructed image, respectively, and u and v are the coordinates in the frequency domain; |F i (u,v)| represents the frequency domain amplitude spectrum obtained by discrete Fourier transform and modulus taking of the reconstructed image f(x,y) in the i-th iteration, where D u,v Represents the weight matrix; Alternatively, the artifact evaluation index s i for: Where p(r) represents the frequency domain amplitude spectrum |F i The distribution ratio of the r-th gray level in the gray level set (u,v)|.

4. The resonant-driven scanner trajectory detection method based on reconstructed image optimization according to claim 3, characterized in that, Following S4, affine deformation repair is performed on the output simulated trajectory and the corresponding reconstructed image, specifically including: In the imaging system where the scanner is located, a reference object with a known shape is preset. The linear transformation matrix corresponding to the affine deformation is calculated based on the field of view before and after imaging of the reference object. The simulated trajectory and the reconstructed image are linearly transformed using the linear transformation matrix to obtain the simulated trajectory and reconstructed image after affine deformation repair.

5. The resonant-driven scanner trajectory detection method based on reconstructed image optimization according to claim 4, characterized in that, The reference object with a known shape is a target or an aperture that limits the imaging field of view, and the target or aperture is set in front of or behind the imaging objective.

6. The resonant-driven scanner trajectory detection method based on reconstructed image optimization according to claim 1 or 5, characterized in that, When reconstructing an image using the current simulated trajectory and the acquired real-time imaging data, the image reconstruction algorithm employs a grid interpolation algorithm; the interpolation strategy of the grid interpolation algorithm is nearest neighbor interpolation, linear interpolation, quadratic spline interpolation, or natural interpolation.

7. The resonant-driven scanner trajectory detection method based on reconstructed image optimization according to claim 1 or 5, characterized in that, The current scanning trajectory parameters are optimized using a multi-objective optimization algorithm, which is either a swarm optimization algorithm or a stochastic gradient descent algorithm with global optimization capability. The iteration termination condition is determined by the optimization strategy of the multi-objective optimization algorithm, including the artifact evaluation index s after multiple iterations. i If there is no change within the set threshold range, the preset maximum number of iterations is reached, or an anomaly occurs in the current trajectory.

8. A resonant-driven scanner trajectory detection system based on reconstructed image optimization, characterized in that, Includes computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the resonant-driven scanner trajectory detection method based on reconstructed image optimization 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 the program is executed by the processor, it implements the resonant-driven scanner trajectory detection method based on reconstructed image optimization as described in any one of claims 1-7.

10. A computer program product, characterized in that, Includes a computer program that, when run on a computer, causes the computer to perform the resonant-driven scanner trajectory detection method based on reconstructed image optimization as described in any one of claims 1-7.

Images