Printing effect evaluation method and device applicable to volumetric bioprinting projection

Abstract

The present invention provides a method for evaluating printing effects in volumetric bioprinting projection, which includes: taking transverse slice samples, performing Radon transform on each transverse slice sample to obtain projection data, then using a to-be-determined window function to perform filtered back-projection transform on the projection data obtained from each transverse slice sample, obtaining a back-projection reconstruction image, normalizing and pixelating it to obtain a simulated printed transverse slice, performing an operation on the simulated printed transverse slice and the transverse slice sample at the corresponding height, obtaining 254 similarity evaluation values corresponding to 254 simulated printed transverse slices at each height, respectively calculating the average value of the M similarity evaluation values corresponding to M simulated printed transverse slices under the same critical curing brightness value, and taking the optimal average value as the printing evaluation index of this to-be-determined window function. Comparing the printing evaluation indexes of different to-be-determined window functions, and taking the window function corresponding to the optimal printing evaluation index as the optimal window function.

Description

Technical Field

[0001] The present invention belongs to the technical field of 3D printing, and particularly relates to a method, device, equipment and storage medium for evaluating printing effects in volumetric bioprinting projection. Background Art

[0002] Biological 3D printing is an application of 3D printing technology in the biological field. It uses biological materials or bioactive substances to create biological tissues or organs with specific structures and functions by means of layer-by-layer stacking or layer-by-layer jetting.

[0003] Biological 3D printing technology helps to solve some challenges in the medical field, such as tissue defect repair, organ transplantation and other problems. By using biological materials such as bioinks, cells, proteins and other biomolecules can be precisely printed into three-dimensional structures to reconstruct damaged or missing tissues.

[0004] Currently, the conventional technology of biological 3D printing is to adopt a layer-by-layer printing technology. The so-called layer-by-layer printing is to slice a three-dimensional model perpendicular to the Z-axis. During printing, these two-dimensional XY-axis slices are vertically printed in the Z-axis direction in the order of position, and finally a three-dimensional object is stacked layer by layer from "two-dimensional" to "three-dimensional".

[0005] In volumetric bioprinting projection, generally a filtering operation needs to be performed on the projection slices (corresponding to transverse slices in the prior art). The filtering effect of the filtering operation will directly affect the data of the projection slices, and thus affect the printing effect. And the filtering effect of the filtering operation directly depends on the selected window function.

[0006] The conventional method for selecting the optimal window function is to actually print using different window functions respectively, compare the similarity between the finally printed three-dimensional object and the original three-dimensional model, and select the window function used in the group with the highest similarity. This method is slow, consumes a large amount of resources, and it is difficult to accurately measure the shape feature values of the printed object. Summary of the Invention

[0007] The object of the present invention is to provide a method, device, equipment and storage medium for evaluating printing effects in volumetric bioprinting projection, which can effectively solve the above technical problems existing in the prior art.

[0008] To achieve the above object, an embodiment of the present invention provides a method for evaluating printing effects in volumetric bioprinting projection, and the method includes the steps of:

[0009] S1. Take M of the transverse slices obtained by equally spacing the transverse slices of the three-dimensional model along the Z-axis as transverse slice samples; wherein, each of the transverse slice samples is a transverse slice binary image, and M≥1;

[0010] S2. Perform a 360-degree Radon transform on each of the said horizontal slice samples, so that each of the said horizontal slice samples obtains W rows of projection data; wherein, each row of projection data is the projection data of a Radon transform of an angle of each of the said horizontal slice samples. Among the W rows of projection data obtained by performing a Radon transform on the same horizontal slice sample, the w-th row of projection data is the projection data of the Radon transform of the (w - 1)*θ angle of the said horizontal slice sample, w = 1, 2... W, 0° < θ ≤ 1°, W*θ = 360°;

[0011] S3. Perform filtered back-projection transforms on the W rows of projection data obtained for each of the said horizontal slice samples using undetermined window functions respectively, so as to obtain M back-projection reconstruction maps with resolutions corresponding to each horizontal slice sample respectively; wherein, the back-projection reconstruction map is the light intensity cumulative distribution of the horizontal slice corresponding to the height of the printed object in the volumetric bioprinting projection;

[0012] S4. Normalize the pixel values of each of the said back-projection reconstruction maps to integers from 0 to 255 to obtain M normalized reconstruction maps;

[0013] S5. Set a variable parameter critical curing brightness value. When the critical curing brightness value is set to different integers in [1, 254] respectively, set the uncured pixels in each of the said normalized reconstruction maps to 0 and the cured pixels to 1 according to the critical curing brightness value, so as to correspondingly obtain M*254 simulated printed horizontal slices; wherein, the pixels in the normalized reconstruction map that are less than the critical curing brightness value are uncured pixels, and the pixels that are greater than or equal to the critical curing brightness value are cured pixels;

[0014] S6. Perform operations on the 254 simulated printed horizontal slices at each height and the horizontal slice samples at the corresponding height through the following formula (1) to obtain 254 similarity evaluation values corresponding to the 254 simulated printed horizontal slices at each height:

[0015]

[0016] wherein, P is the similarity evaluation value, X and Y are the horizontal resolution and vertical resolution of the horizontal slice sample and the simulated printed horizontal slice; f o (x, y) is the value of the pixel at the position with coordinates (x, y) in the horizontal slice sample with the upper left corner as the coordinate (1, 1) and the positive directions being downward and leftward; f(x, y) is the value of the pixel at the position with coordinates (x, y) in the simulated printed horizontal slice with the upper left corner as the coordinate (1, 1) and the positive directions being downward and leftward;

[0017] S7. Calculate the average value of the M similarity evaluation values corresponding to the M simulated printing horizontal slices under the same critical curing brightness value respectively, and take the average value closest to 0 as the printing evaluation index of the to-be-determined window function;

[0018] S8. Compare the printing evaluation indexes of different window functions obtained by performing filtered back-projection transformation on the W-line projection data of each of the horizontal slice samples in step S3 using different window functions respectively and passing through steps S4 to S7, and take the window function corresponding to the printing evaluation index closest to 0 among the printing evaluation indexes of different window functions as the optimal window function for the horizontal slice at the corresponding height.

[0019] As an improvement of the above, in step S6, replace formula (1) with the following formula (2) to perform operations on 254 simulated printing horizontal slices at each height and the horizontal slice sample at the corresponding height, and obtain 254 similarity evaluation values corresponding to the 254 simulated printing horizontal slices at each height:

[0020]

[0021] Among them, in S7, take the average value closest to 1 as the printing evaluation index of the to-be-determined window function; in S8, take the window function corresponding to the printing evaluation index closest to 1 among the printing evaluation indexes of different parameters as the optimal window function.

[0022] As an improvement of the above, in step S2, perform the Radon transform on each angle of 360 degrees on the side of each horizontal slice sample in turn, so as to obtain the projection data of the Radon transform corresponding to each angle of each horizontal slice sample.

[0023] As an improvement of the above, in step S3, perform filtered back-projection transformation on the projection data with a projection data angle range greater than or equal to 180 degrees in the W-line projection data of each horizontal slice sample using the to-be-determined window function respectively, so as to obtain M back-projection reconstruction maps with resolutions respectively the same as those corresponding to each horizontal slice sample.

[0024] As an improvement of the above, the different window functions refer to the Kaiser window functions under different parameters β, and the formula of the Kaiser window function is as shown in formula (3):

[0025]

[0026] Among them, n = 1, 2, 3,..., N - 1, N represents the total length of the window function; I0 represents the first-kind Bessel function; β is a variable parameter;

[0027] The M transverse slice samples respectively correspond to the transverse slices at the 50th layer, 100th layer, 150th layer, 100th layer, 150th layer, 200th layer, 250th layer, 300th layer, 350th layer, 400th layer and 450th layer in height.

[0028] As an improvement of the above, in the step S3, for the W - row projection data obtained from each of the transverse slice samples, Fourier transform, filtering, and inverse Fourier transform operations in the filtered back - projection transform are respectively performed using a to - be - determined window function, specifically including:

[0029] S31. Perform a fast Fourier transform on the W - row projection data obtained from each of the transverse slice samples; wherein, the width N in the adopted fast Fourier transform is greater than or equal to the number of pixels in each row of projection data for obtaining each of the transverse slice samples.

[0030] S32. Perform a windowing operation on the ramp filter using the to - be - determined window function, and filter each row of projection data obtained from each of the transverse slice samples after the fast Fourier transform using the windowed ramp filter.

[0031] S33. Perform an inverse fast Fourier transform on each row of projection data obtained from each of the transverse slice samples after filtering, so as to obtain M back - projection reconstruction maps with resolutions corresponding to each of the transverse slice samples respectively.

[0032] As an improvement of the above, the step S32 specifically includes:

[0033] S321. Discretely sample the ramp filter in the frequency domain; the number of sampling points is an even number greater than or equal to N and closest to N.

[0034] S322. Perform point - by - point multiplication of the discretized ramp filter and the discretized to - be - determined window function with the same number of sampling points to perform the windowing operation.

[0035] S323. Perform a translation operation on the windowed ramp filter data, so as to move the latter half of the ramp filter data to the head.

[0036] S324. Perform point - by - point multiplication of the windowed and translated ramp filter and each row of projection data obtained from each of the transverse slice samples after the fast Fourier transform to perform the filtering operation, and discard the points that the windowed and translated ramp filter has more than each point of each row of projection data obtained from each of the transverse slice samples after the fast Fourier transform.

[0037] Another embodiment of the present invention correspondingly provides a printing effect evaluation device applicable to volume bioprinting projection, which is characterized by including:

[0038] A sampling module, configured to use M of the transverse slices obtained by equally spacing and horizontally slicing a three-dimensional model along the Z-axis as transverse slice samples; wherein, each of the transverse slice samples is a transverse slice binary map, and M≥1;

[0039] A projection data conversion module, configured to perform a 360-degree Radon transform on each of the transverse slice samples, so that each of the transverse slice samples obtains W rows of projection data; wherein, each row of projection data is the projection data of a Radon transform of an angle of each of the transverse slice samples, and among the W rows of projection data obtained by performing a Radon transform on the same transverse slice sample, the w-th row of projection data is the projection data of a Radon transform of the (w - 1)*θ angle of the transverse slice sample, w = 1, 2... W, 0° < θ ≤ 1°, and W*θ = 360°;

[0040] A filtered back-projection transform module, configured to perform a filtered back-projection transform on the W rows of projection data obtained from each of the transverse slice samples using a to-be-determined window function respectively, so as to obtain M back-projection reconstruction maps with resolutions respectively corresponding to and the same as each transverse slice sample; wherein, the back-projection reconstruction map is the light intensity cumulative distribution of the transverse slice corresponding to the height of the printed object in the volume bioprinting projection;

[0041] A normalization processing module, configured to normalize the pixel values of each of the back-projection reconstruction maps to integers between 0 and 255, so as to obtain M normalized reconstruction maps;

[0042] An analog printing transverse slice acquisition module, configured to set a variable parameter critical curing brightness value, and respectively when setting the critical curing brightness value to different integers in [1, 254], set the non-cured pixels in each of the normalized reconstruction maps to 0 and the cured pixels to 1 according to the critical curing brightness value, so as to correspondingly obtain M*254 analog printing transverse slices; wherein, the pixels in the normalized reconstruction map that are less than the critical curing brightness value are non-cured pixels, and the pixels that are greater than or equal to the critical curing brightness value are cured pixels;

[0043] A similarity evaluation value calculation module, configured to perform an operation on the 254 analog printing transverse slices at each height and the transverse slice samples at the corresponding height through the following formula (1), so as to obtain 254 similarity evaluation values corresponding to the 254 analog printing transverse slices at each height:

[0044]

[0045] Wherein, P is the similarity evaluation value, X and Y are the horizontal resolution and vertical resolution of the transverse slice sample and the analog printing transverse slice; f o(x, y) is the value of the pixel at the position with coordinates (x, y) in the horizontal slice sample, where the coordinate system has its upper left corner at (1, 1), the positive direction is downward and to the left; f(x, y) is the value of the pixel at the position with coordinates (x, y) in the simulated printed horizontal slice, where the coordinate system has its upper left corner at (1, 1), the positive direction is downward and to the left.

[0046] The printing evaluation index calculation module is used to calculate the average value of the M similarity evaluation values corresponding to M simulated printed horizontal slices under the same critical curing brightness value respectively, and take the average value closest to 0 as the printing evaluation index of the to-be-determined window function.

[0047] The optimal window function determination module is used to compare the printing evaluation indexes of different window functions obtained by performing filtered back-projection transformation on the W-line projection data obtained for each of the horizontal slice samples in the filtered back-projection transformation module using different window functions and passing through the filtered back-projection transformation module, the normalization processing module, the simulated printed horizontal slice acquisition module, and the printing evaluation index calculation module, and take the window function corresponding to the printing evaluation index closest to 0 among the printing evaluation indexes of different window functions as the optimal window function.

[0048] Another embodiment of the present invention provides an electronic device, including a processor and a memory. Among them, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the printing effect evaluation method applicable to volume bioprinting projection described in any one of the above embodiments.

[0049] Another embodiment of the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to execute the printing effect evaluation method applicable to volume bioprinting projection described in any one of the above embodiments.

[0050] Compared with the prior art, a method, apparatus, device, and storage medium for evaluating printing effects in volumetric bioprinting projection provided by an embodiment of the present invention have the following technical effects: (1) The evaluation of printing effects in volumetric bioprinting projection is faster. The evaluation method adopted in the embodiment of the present invention can be run entirely by a computer, avoiding the time loss caused by actual printing. (2) Resource consumption is reduced. The evaluation method adopted in the embodiment of the present invention does not require actual printing throughout the process, avoiding the consumption of resources such as bioink and machine wear in actual printing. (3) The evaluation results are more accurate. The evaluation method adopted in the embodiment of the present invention uses strict algorithm logic and digital simulation operations throughout the process. It avoids the physical environment interference (such as different printing times, machine vibrations, etc.) brought by the actual printing steps in the conventional method, as well as the errors in measuring the shape feature data of the printed object. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] To more clearly illustrate the technical solutions of the present invention, the drawings required for implementation will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 It is a schematic flowchart of a method for evaluating printing effects in volumetric bioprinting projection provided by an embodiment of the present invention.

[0053] Figure 2 It is a schematic structural diagram of a transverse slice generated by using a method for evaluating printing effects in volumetric bioprinting projection provided by an embodiment of the present invention.

[0054] Figure 3 It is a schematic diagram of the Radon transform performed on a transverse slice sample by a method for evaluating printing effects in volumetric bioprinting projection provided by an embodiment of the present invention.

[0055] Figure 4 It shows the generation (transformation) process of the projection slice obtained in the printing method evaluated by a method for evaluating printing effects in volumetric bioprinting projection provided by an embodiment of the present invention.

[0056] Figure 5 It is a schematic diagram of volume printing of a three-dimensional model for the projection slice obtained in the printing method evaluated by a method for evaluating printing effects in volumetric bioprinting projection provided by an embodiment of the present invention.

[0057] Figure 6 It is a change curve graph of the printing evaluation index and the critical curing brightness value under the Kaiser window function with different parameters β.

[0058] Figure 7 Shows the printing evaluation indexes under the Kaiser window function with different parameters β.

[0059] Figure 8 It is a structural block diagram of a printing effect evaluation device applicable to volumetric bioprinting projection provided by an embodiment of the present invention.

[0060] Figure 9 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] Figure 1 It is a flowchart of a projection slicing method applicable to volumetric bioprinting provided by an embodiment of the present application. The method includes steps S1 to S8:

[0063] S1. Take M of the transverse slices obtained by equally spaced transverse slicing of the three-dimensional model along the Z-axis as transverse slice samples; wherein, each of the transverse slice samples is a transverse slice binary image, and M≥1;

[0064] S2. Perform a 360-degree Radon transform on each of the transverse slice samples, so that each of the transverse slice samples obtains W rows of projection data; wherein, each row of projection data is the projection data of a Radon transform of an angle of each of the transverse slice samples. Among the W rows of projection data obtained by performing a Radon transform on the same transverse slice sample, the w-th row of projection data is the projection data of the Radon transform of the (w - 1)*θ angle of the transverse slice sample, w = 1, 2...W, 0° < θ ≤ 1°, and W*θ = 360°;

[0065] S3. Perform filtered back-projection transformation on the W rows of projection data obtained from each of the transverse slice samples using a to-be-determined window function, so as to obtain M back-projection reconstruction images with resolutions respectively corresponding to each transverse slice sample; wherein, the back-projection reconstruction image is the light intensity cumulative distribution of the transverse slice corresponding to the height of the printed object in the volumetric bioprinting projection;

[0066] S4. Normalize the pixel values of each of the back-projection reconstruction images to integers between 0 and 255 to obtain M normalized reconstruction images;

[0067] S5. Set the critical curing brightness value of the variable parameter. When the critical curing brightness value is set to different integers in the range of [1, 254] respectively, for each of the normalized reconstructed images, set the uncured pixels to 0 and the cured pixels to 1 according to the critical curing brightness value, so as to correspondingly obtain M * 254 simulated printing horizontal slices; wherein, in the normalized reconstructed image, the pixels less than the critical curing brightness value are uncured pixels, and the pixels greater than or equal to the critical curing brightness value are cured pixels.

[0068] S6. Perform operations on the 254 simulated printing horizontal slices at each height and the horizontal slice samples at the corresponding height through the following formula (1) to obtain 254 similarity evaluation values corresponding to the 254 simulated printing horizontal slices at each height:

[0069]

[0070] where P is the similarity evaluation value, X and Y are the horizontal resolution and vertical resolution of the horizontal slice sample and the simulated printing horizontal slice; f o (x, y) is the value of the pixel at the position with coordinates (x, y) in the horizontal slice sample with the upper left corner as the coordinate (1, 1) and the positive directions being downward and leftward; f(x, y) is the value of the pixel at the position with coordinates (x, y) in the simulated printing horizontal slice with the upper left corner as the coordinate (1, 1) and the positive directions being downward and leftward.

[0071] S7. Calculate the average value of the M similarity evaluation values corresponding to the M simulated printing horizontal slices under the same critical curing brightness value respectively, and take the average value closest to 0 as the printing evaluation index of the to-be-determined window function.

[0072] S8. Compare the printing evaluation indexes of different window functions obtained by performing filtered back-projection transformation on the W-row projection data obtained for each of the horizontal slice samples in step S3 using different window functions respectively and passing through steps S4 to S7, and take the window function corresponding to the printing evaluation index closest to 0 among the printing evaluation indexes of different window functions as the optimal window function.

[0073] Next, each step of a printing effect evaluation method applicable to volumetric bioprinting provided by the embodiments of the present application will be described in detail.

[0074] First, in the step S1, the step S1 is a sampling step, that is, several (for example, M) transverse slices are taken from the transverse slices obtained by equally spaced transverse slicing of the three-dimensional model along the Z-axis as transverse slice samples, and the printing effect evaluation method of the embodiment of the present invention is performed on the M transverse slice samples to obtain an evaluation result. Specifically, in the process of equally spaced transverse slicing of the three-dimensional model along the Z-axis to obtain transverse slices, a large number of three-dimensional coordinates of triangles can be stored, then the triangles that meet the conditions are calculated, the coordinates of the vertices of the triangles are calculated, and then the points are connected to obtain a contour map, the contour image is pixelated, and all plane triangles are moved upward by a fixed distance, and the same operation is performed until the plane parallel to the X and Y axes reaches the top layer of the three-dimensional model or exceeds the top layer for the first time.

[0075] It can be understood that the smaller the fixed distance by which the plane parallel to the X and Y axes is moved upward, the better. Because the number of transverse slice binary images determines the accuracy of the finally printed three-dimensional object. The number of transverse slice binary images is obtained according to the total height of the 3D model / fixed moving distance. It can be understood that equally spaced transverse slicing of the three-dimensional model along the Z-axis is a discretization of it. When the number of discretized faces is large enough, preferably when the number of equally spaced transverse slicing of the three-dimensional model along the Z-axis is large enough, it is more similar to the original three-dimensional model, and the reduction degree of the printed object is higher.

[0076] It can be understood that each transverse slice is a transverse slice binary image, and correspondingly, each of the transverse slice samples is also a transverse slice binary image. Specifically, in combination with Figure 2 , how to obtain the transverse slice binary image of each transverse slice of the three-dimensional model will be described in detail. It can be understood that in the S1, the three-dimensional model is composed of a large number of triangles, and each triangle is provided with corresponding vertex three-dimensional coordinates. Let the expression of the plane parallel to the X and Y axes be: Z = z0, where Z represents the coordinate of the Z-axis, and z0 is a constant, and z0 is initialized to 0; the step S1 specifically includes steps S11 to S15:

[0077] S11. Compare the Z-axis coordinates of the three vertices of each triangle with z0, and select the triangles in which there are both vertices with Z coordinates greater than z0 and vertices with Z coordinates less than z0, or the triangles in which the Z coordinates of two vertices are equal to z0 at the same time.

[0078] S12. For the triangles in which there are both vertices with Z coordinates greater than z0 and vertices with Z coordinates less than z0, when there are vertices with Z coordinates equal to z0, connect the remaining two vertices and calculate the intersection points of the connected line segments and the plane parallel to the X and Y axes.

[0079] S13. For a triangle that has no vertex with a Z - coordinate equal to z0, calculate the intersection points of the line segments connecting two vertices with Z - coordinates greater than z0 to the vertex with a Z - coordinate less than z0, respectively, and the plane parallel to the X, Y - axis plane, or calculate the intersection points of the line segments connecting two vertices with Z - coordinates less than z0 to the vertex with a Z - coordinate greater than z0, respectively, and the plane parallel to the X, Y - axis plane;

[0080] Among them, in the above steps S22 and S23, the vector method is used to calculate each of the intersection points, and the formula is as follows:

[0081]

[0082]

[0083] Among them, the coordinates of the point P1 with a Z - coordinate greater than z0 are (x1, y1, z1), the coordinates of another point P2 with a Z - coordinate less than z0 are (x2, y2, z2), the intersection point is P, the expression of the plane parallel to the X, Y - axis plane is ax + by+cz + d = 0, where a, b, c, d are constants, x, y, z are variables of the X - axis, Y - axis, and Z - axis, and the origin is O. is the vector from O to P. is the vector from O to P1. is the vector from P1 to P2.

[0084] Since the plane parallel to the X, Y - axis plane is parallel to the X, Y - axis, so a = 0, b = 0, d=-z0, and the formula is transformed into:

[0085]

[0086] Among them, represents the vector from P1 to P.

[0087] Thus, the coordinates of each intersection point P are obtained as Therefore, each triangle obtains one intersection point, one endpoint with a Z - coordinate equal to z0, or two intersection points or two endpoints with a Z - coordinate equal to z0. The two points obtained from one triangle are taken as a group.

[0088] S14. Connect the two points of all point groups to obtain a contour map of the cross - section of the three - dimensional model with a Z - coordinate of z0 and parallel to the X, Y - axis plane. Pixelize the contour map, with the pixel points inside the contour map being white and the pixel points outside the contour map being black, thereby obtaining the binary map of the current layer's horizontal slice. Among them, Figure 2 respectively show the binary maps of the horizontal slices (original binary maps) of the 150th layer and the 450th layer of the three - dimensional model.

[0089] It can be understood that in this embodiment, the M transverse slice samples for sampling are obtained by dividing the microneedle model (see Figure 2 ) into 500 slices of the same thickness, and sampling the transverse slices corresponding to the 50th, 100th, 150th, 100th, 150th, 200th, 250th, 300th, 350th, 400th, and 450th layers in height.

[0090] It can be understood that in this step, the contour image is pixelated. For each pixel of the contour map, the center point coordinates are taken as the pixel coordinates, and the ray method is used to determine whether it is inside the contour. The pixel points inside the contour are white, and the pixel points outside the contour are black. The black represents that this pixel point is outside the three-dimensional model and is empty, indicating that the pixel point position does not need to be photocured into an object; the white represents that this pixel point is inside the three-dimensional model and is a solid, indicating that the pixel point position needs to be photocured into an object. Among them, the specific operation of pixelating the contour image is: covering the contour map with square grids of the same size, each grid is a pixel, and each grid is set with a color, and the side length of the grid is a self-defined length.

[0091] S15. Determine whether the current z0 is less than H. If so, replace the current z0 with the value of z0 + h and return to step S11; if not, end. Where H is the total height of the three-dimensional model, and h < H. As a preferred solution, h ≤ 0.01 mm.

[0092] It can be understood that in this step S15, h is a fixed distance moved upward parallel to the X and Y axis planes. Among them, the total height of the three-dimensional model is calculated by rounding. Specifically, the total height of the three-dimensional model / the rounding of the fixed distance moved upward = the number of layers + 1, that is: [H / h] = M + 1. When the total height of the three-dimensional model is the same, the smaller the fixed distance moved upward, the more layers. The highest point of the three-dimensional model is located in the topmost transverse slice.

[0093] It can be understood that after obtaining the binary images of the transverse slices of the three-dimensional model through step S1, the printing method to be evaluated by the printing effect evaluation method of the embodiment of the present invention does not directly use the transverse slices as the projection slices for 3D volume printing. Instead, a 360-degree Radon transform is performed on all the transverse slices of the three-dimensional model to obtain the projection data of the Radon transform at different angles of all the transverse slices. Then, the projection data of the same angle of all the transverse slices are stacked in sequence along the Z-axis direction to form a projection slice at the same angle, so as to obtain the projection slices at different angles of 360 degrees on the side of the three-dimensional model. Then, the obtained projection slices are used for 3D volume printing in the order of angles. Therefore, when implementing the printing effect evaluation method of the embodiment of the present invention, it is also necessary to first perform the corresponding 360-degree Radon transform on each of the transverse slice samples.

[0094] Specifically, in step S2, the Radon transform is sequentially performed on each angle of 360 degrees on the side of each of the transverse slice samples, so as to obtain the projection data of the Radon transform at the corresponding angle of each of the transverse slice samples. For example, taking θ = 1°, W = 360 as an example, starting from the 0° angle of 360 degrees on the side of each of the transverse slice samples, the Radon transform is performed to obtain the first row of projection data. Then, the Radon transform is performed on the 1° angle of 360 degrees on the side of each of the transverse slice samples to obtain the second row of projection data. Then, the Radon transform is performed on the 2° angle of 360 degrees on the side of each of the transverse slice samples to obtain the third row of projection data... and so on, so as to obtain the wth row of projection data at the (w - 1)*θ angle of 360 degrees on the side of each of the transverse slice samples. Another example is taking θ = 0.5°, W = 720 as an example. Starting from the 0° angle of 360 degrees on the side of each of the transverse slice samples, the Radon transform is performed to obtain the first row of projection data. Then, the Radon transform is performed on the 0.5° angle of 360 degrees on the side of each of the transverse slice samples to obtain the second row of projection data. Then, the Radon transform is performed on the 1° angle of 360 degrees on the side of each of the transverse slice samples to obtain the third row of projection data... and so on, so as to obtain the wth row of projection data at the (w - 1)*θ angle of 360 degrees on the side of each of the transverse slice samples.

[0095] It can be understood that a 360-degree Radon transform is performed on all the transverse slice samples (for example, M pieces). The Radon transform is the projection of the intensity of the binary image (two-dimensional grayscale image) of each transverse slice sample along the radial line at a specific angle. Specifically, the Radon transform of the binary image of each transverse slice sample is the sum of the Radon transforms of each pixel. For example, combined with Figure 3As shown in the figure, the Radon transform operation process for each binary image of the horizontal slice sample is as follows:

[0096] First, each pixel of the binary image of the horizontal slice sample is divided into four sub-pixels, and each sub-pixel is projected separately;

[0097] According to the distance between the projection position and the bin center, the contribution of each sub-pixel is split proportionally into the two nearest bins;

[0098] According to the situation of the sub-pixel projected onto the bin center point, calculate the sum of the pixel values;

[0099] Among them, the situation where the sub-pixel is projected onto the bin center point is specifically:

[0100] (1) If the sub-pixel is projected onto the center point of the bin, the bin on the axis will obtain the full value of the sub-pixel, which is one-fourth of the pixel value;

[0101] (2) If the sub-pixel is projected onto the boundary between two bins, the sub-pixel value is evenly split between these two bins.

[0102] It can be understood that the Radon transform for the two-dimensional grayscale image shown in Figure 3 is already familiar to those skilled in the art and will not be elaborated in detail here.

[0103] In this embodiment, after obtaining the projection data of each angle of each horizontal slice sample through step S2, through step S3, the projection data of each angle of each horizontal slice is subjected to a Filter back-projection operation, so as to obtain a back-projection reconstruction image with the same resolution corresponding to each horizontal slice sample. It can be understood that the back-projection reconstruction image is the light intensity cumulative distribution of the horizontal slice corresponding to the height (for example, the 100th layer) of the printed object in the volumetric bioprinting projection.

[0104] It can be understood that when the printing method to be evaluated by the printing effect evaluation method of the embodiment of the present invention is actually executed, the filtered projection data of the same angle of all horizontal slices of the three-dimensional model will be stacked in sequence in the Z-axis direction to form a projection slice of the same angle, so as to obtain the projection slices of different angles of 360 degrees on the side of the three-dimensional model. Combining Figure 4 shown in Figure 4 shows the generation (transformation) process of the projection slice. Specifically, it shows that the three-dimensional object is horizontally sliced to obtain multiple (sheets) of horizontal slices, and then the Radon transform is performed on each layer (sheet) of the horizontal slice to obtain the projection data, and then the same angle (for example, Figure 4The projection data (preferably after filtering) shown at 0° are stacked in sequence along the Z-axis direction to form projection slices at the same angle (e.g., Figure 4 shown at 0°). For example, in combination with Figure 5 , during actual printing execution, during the volumetric printing of the three-dimensional model, the projection slices are projected into the printing bottle filled with photocurable biologic ink in sequence according to the angle order (e.g., at regular intervals), and at the same time, the printing bottle is rotated at a constant speed so that the angle of the projected projection slice corresponds to the angle of the printing bottle. Specifically, starting from a certain angle, the projection slices are projected into the printing bottle filled with photocurable biologic ink at regular intervals in a clockwise or counterclockwise order, and at the same time, the printing bottle is rotated at a constant speed so that the angle of the projected projection slice corresponds one by one to the angle of the printing bottle. The projection light passes through the printing bottle at the corresponding angle and performs an operation similar to back-projection on all the transverse cross-sections in each printing bottle. That is, a string of projection data at each angle (here, the gray scale of each row of pixels) is "smudged" along the original angle and averaged onto all the two-dimensional pixel points, which will cause the light intensity in the transverse cross-section of the printing bottle to accumulate into the shape of the corresponding transverse cross-section of the three-dimensional object, so that this transverse cross-section is cured into a transverse slice of the three-dimensional object. The light intensity accumulation refers to the superposition of the light intensity energy at the same position during the entire printing process. Since the projection slices at each angle are formed by stacking the projection data after filtering at this angle of all the transverse cross-sections of the three-dimensional object (the projection data after filtering at this angle of the transverse cross-section of the three-dimensional object is a string of data, and this string of data is arranged row by row from bottom to top according to the transverse slice to which it belongs), when it is projected into the printing bottle, an operation similar to back-projection will be performed on all the transverse cross-sections at the same time. The biologic ink in all the transverse cross-sections in the printing bottle is photocured into the corresponding transverse slices of the three-dimensional object at the same time. From a three-dimensional perspective, this is the three-dimensional object. Thus, the volumetric printing of the 3D model is completed.

[0105] Returning to the embodiments of the present invention, continuing to refer in combination to Figure 2 、 Figure 6 and Figure 7 , preferably, in step S3, the Fourier transform, filtering, and inverse Fourier transform operations in the filtered back-projection transform are respectively performed on the W rows of projection data obtained for each of the transverse slice samples using a window function to be determined, specifically including:

[0106] S31. Perform a fast Fourier transform on the W rows of projection data obtained for each of the transverse slice samples; wherein, the width N in the adopted fast Fourier transform is greater than or equal to the number of pixels in each row of projection data for obtaining each of the transverse slice samples;

[0107] S32. Window the ramp filter using the to-be-determined window function, and filter each row of the projection data after fast Fourier transform of each of the transverse slice samples using the windowed ramp filter;

[0108] S33. Perform inverse fast Fourier transform on each row of the projection data of each of the transverse slice samples after filtering, so as to obtain M back-projection reconstruction images with resolutions corresponding to each of the transverse slice samples respectively.

[0109] Among them, the step S32 specifically includes:

[0110] S321. Discretely sample the ramp filter in the frequency domain; the number of sampling points is an even number greater than or equal to N and closest to N;

[0111] S322. Perform point-by-point multiplication of the discretized ramp filter and the to-be-determined window function discretized with the same number of sampling points to perform the windowing operation;

[0112] S323. Perform a translation operation on the windowed ramp filter data, so as to move the latter half of the ramp filter data to the head;

[0113] S324. Perform point-by-point multiplication of the windowed and translated ramp filter and each row of the projection data of each of the transverse slice samples after fast Fourier transform to perform the filtering operation, and discard the points that the windowed and translated ramp filter has more than each point of each row of the projection data of each of the transverse slice samples after fast Fourier transform.

[0114] It can be understood that filtering the projection data of each angle of each of the transverse slices using the above-mentioned Filter back-projection can effectively reduce star artifacts. Specifically, the Fourier transform of the projection data of each angle is a straight line passing through the center of the frequency domain coordinates, and finally forms a shape of point scattering. The density of the central segment in the origin of the ωx-ωy plane is higher than that in the region far from the origin, and the region near the origin of the Fourier space is the low-frequency region. Overweighting the low-frequency components causes star artifacts to appear in the image. In order to reduce star artifacts, weighted correction is performed on the Fourier space to make its density uniform. Therefore, a low-frequency filter, |w| (i.e., the ramp filter), is used to suppress the low-frequency components, reduce star artifacts, and improve the clarity of the image.

[0115] In addition, in step S323, a translation operation needs to be performed on the ramp filter data after windowing. Considering that the data after the fast Fourier transform does not correspond one-to-one with the ramp filter data after windowing, because the data after the fast Fourier transform is the first half (when the width N is odd, it is the first (N + 1) / 2 data) representing data from low frequency to high frequency, and the subsequent data is mirror-symmetrical to the previous data.

[0116] It can be understood that in step S3, as a preferred solution, according to the basic principle of Filterback-projection, only the projection data with a projection data angle range greater than or equal to 180 degrees in the W-row projection data obtained from each of the transverse slice samples (that is, not all projection data at all angles need to be used) can be separately subjected to the filtered back-projection transformation using the to-be-determined window function, so as to obtain M back-projection reconstruction images with resolutions respectively corresponding to each transverse slice sample.

[0117] Furthermore, in step S3, the to-be-determined window function refers to multiple different window functions used to participate in the evaluation of the printing effect evaluation method provided in the embodiments of the present invention to finally determine the optimal window function. The different window functions refer to Kaiser window functions under different parameters β, and the formula of the Kaiser window function is shown in formula (3):

[0118]

[0119] where n = 1, 2, 3,..., N - 1, N represents the total length of the window function; I0 represents the first kind of Bessel function; β is a variable parameter. For example, in this embodiment, 11 different parameters with β values ranging from 0 to 10 can be taken to obtain 11 corresponding different window functions for evaluation.

[0120] Furthermore, in steps S4 to S5, in order to obtain the simulated printing transverse slice of the "corresponding height" of the printed object, first, the pixel values of the back-projection reconstruction image are normalized to integers from 0 to 255 to obtain the normalized reconstruction image. Then, a variable parameter "critical curing brightness value" is set, and the range is any integer from 1 to 254. The critical curing brightness value represents that in the normalized reconstruction image, the part lower than the critical curing brightness value belongs to the uncured part, that is, the part without the printed object, and the part higher than or equal to the critical curing brightness value belongs to the cured part, that is, the part where the printed object is formed. Through the critical curing brightness value, the uncured pixels in the normalized reconstruction image are set to 0, and the cured pixels are set to 1, so as to obtain the transverse slice binary image (that is, the simulated printing transverse slice) of the "corresponding height" of the printed object. From the volume bioprinting projection process, it can be seen that the transverse slice is a binary image, where the pixel value of 1 represents the three-dimensional model, and the pixel value of 0 represents the void.

[0121] Reference Figure 2 , Figure 2 shows the transverse slice samples (original binary images) of the 150th layer and the 450th layer of the three-dimensional model (microneedle model), the back-projection reconstruction images (filtered back-projection grayscale images) corresponding to the transverse slice samples of the 150th layer and the 450th layer obtained by performing Step 3 using the Kaiser window function with parameter β = 6, and the simulated printed transverse slices (binary images) corresponding to the transverse slice samples of the 150th layer and the 450th layer obtained by performing Step 5 when the critical curing brightness value is set to 123.

[0122] Next, in combination with Figures 6 - 7 , the steps S6 - S8 of the embodiment of the present invention will be described in detail. As Figure 6 shown, Figure 6 is a change curve graph of the printing evaluation index and the critical curing brightness value under the Kaiser window function with different parameters β, showing the change of the printing evaluation index and the critical curing brightness value under the Kaiser window function with different parameters β. In Figure 6 , the abscissa represents different "critical curing brightness values", and the ordinate represents the "average similarity evaluation value" of the transverse slice samples, that is, the printing evaluation index. Figure 7 shows the printing evaluation index under the Kaiser window function with different parameters β. In Figure 7 , the abscissa represents the Kaiser window function with different β values, and the ordinate is the printing evaluation index.

[0123] Specifically, in steps S6 - S7, the simulated printed transverse slices are operated with the transverse slice samples at the corresponding height through the above formula (1) to obtain the similarity evaluation values corresponding to 254 simulated printed transverse slices at each height. When specifically performing this operation, the pixels at the same position of the transverse slice samples and the simulated printed transverse slices at the same height are compared. If they are different, 1 is added, and then the proportion of the final result in the total number of pixels (the total number of pixels of the transverse slice samples or the simulated printed transverse slices) is calculated. It can be seen from formula (1) that the closer the similarity evaluation value P is to 0, the higher the similarity between the transverse slice samples and the simulated printed transverse slices at the same height, and vice versa when P is closer to 1.

[0124] As an alternative implementation, in step S6, the following formula (2) is used to replace formula (1) to operate 254 simulated printed transverse slices at each height with the transverse slice samples at the corresponding height, and 254 similarity evaluation values corresponding to 254 simulated printed transverse slices at each height are obtained:

[0125]

[0126] Among them, when implementing the solution of the above formula (2), in the step S7, the average value closest to 1 is used as the printing evaluation index of the to-be-determined window function; in the step S8, the window function corresponding to the printing evaluation index closest to 1 among the printing evaluation indexes with different parameters is used as the optimal window function.

[0127] For all horizontal slice samples (for example, M sheets, and each sheet corresponds to a height, for example, the 100th layer), 254 simulated printing horizontal slices at each height are calculated with the corresponding horizontal slice samples through the above formula (1) or formula (2), and 254 similarity evaluation values corresponding to the 254 simulated printing horizontal slices at each height are obtained. Then, the average values of the M similarity evaluation values corresponding to all simulated printing horizontal slices (for example, M simulated printing horizontal slices) corresponding to the same critical curing brightness value (the specific value range is 1 to 254) are calculated respectively, and the optimal average value (when using formula (1), the closer the average value is to 0, the better; when using formula (2), the closer the average value is to 1, the better) is used as the printing evaluation index of the to-be-determined window function.

[0128] Then, in the step S8, for all different window functions that need to be judged for superiority as the to-be-determined window function in the step S3, filtered backprojection transformation is respectively performed, and after passing through the steps S4 to S7, the printing evaluation indexes of different window functions are obtained. The printing evaluation indexes of different window functions are compared, and the window function corresponding to the optimal printing evaluation index (when using formula (1), the closer the printing evaluation index is to 0, the better; when using formula (2), the closer the printing evaluation index is to 1, the better) is used as the optimal window function.

[0129] In summary, a printing effect evaluation method applicable to volumetric bioprinting projection provided by an embodiment of the present invention uses an algorithm to simulate the process of volumetric bioprinting projection, uses different window functions to obtain different digital printing results, and compares them with the original three-dimensional model, which speeds up the evaluation of the printing effect in volumetric bioprinting projection, reduces resource consumption, and makes the evaluation result more accurate.

[0130] As Figure 8 shown, an embodiment of the present invention provides a printing effect evaluation device applicable to volumetric bioprinting projection, which includes:

[0131] A sampling module 81, configured to use M of the horizontal slices obtained by equally spacing and horizontally slicing a three-dimensional model along the Z axis as horizontal slice samples; wherein, each of the horizontal slice samples is a horizontal slice binary image, and M≥1;

[0132] The projection data conversion module 82 is used to perform a 360-degree Radon transform on each of the transverse slice samples, so that each of the transverse slice samples obtains W rows of projection data; wherein, each row of projection data is the projection data of a Radon transform of an angle of each of the transverse slice samples. Among the W rows of projection data obtained by performing a Radon transform on the same transverse slice sample, the w-th row of projection data is the projection data of the Radon transform of the (w - 1)*θ angle of the transverse slice sample, where w = 1, 2... W, 0° < θ ≤ 1°, and W*θ = 360°;

[0133] The filtered backprojection transform module 83 is used to perform a filtered backprojection transform on the W rows of projection data obtained for each of the transverse slice samples using a to-be-determined window function, so as to obtain M backprojection reconstruction images with resolutions respectively corresponding to and the same as each of the transverse slice samples; wherein, the backprojection reconstruction image is the light intensity cumulative distribution of the transverse slice corresponding to the height of the printed object in the volumetric bioprinting projection;

[0134] The normalization processing module 84 is used to normalize the pixel values of each of the backprojection reconstruction images to integers between 0 and 255, obtaining M normalized reconstruction images;

[0135] The simulated printing transverse slice acquisition module 85 is used to set a variable parameter critical curing brightness value. When the critical curing brightness value is set to different integers in the range of [1, 254], for each of the normalized reconstruction images, the uncured pixels are set to 0 and the cured pixels are set to 1 according to the critical curing brightness value, thereby correspondingly obtaining M * 254 simulated printing transverse slices; wherein, in the normalized reconstruction image, the pixels less than the critical curing brightness value are uncured pixels, and the pixels greater than or equal to the critical curing brightness value are cured pixels;

[0136] The similarity evaluation value calculation module 86 is used to perform an operation on the 254 simulated printing transverse slices at each height and the transverse slice sample at the corresponding height through the following formula (1), obtaining 254 similarity evaluation values corresponding to the 254 simulated printing transverse slices at each height:

[0137]

[0138] wherein, P is the similarity evaluation value, X and Y are the horizontal resolution and vertical resolution of the transverse slice sample and the simulated printing transverse slice; f o (c, y) is the value of the pixel at the position with coordinates (x, y) in the transverse slice sample with the upper left corner as the coordinate (1, 1) and the positive directions being downward and to the left; f(x, y) is the value of the pixel at the position with coordinates (x, y) in the simulated printing transverse slice with the upper left corner as the coordinate (1, 1) and the positive directions being downward and to the left;

[0139] A printing evaluation index calculation module 87 is configured to calculate, respectively, the average value of M similarity evaluation values corresponding to M simulated printing horizontal slices under the same critical curing brightness value, and use the average value closest to 0 as the printing evaluation index of the to-be-determined window function; and

[0140] An optimal window function determination module 88 is configured to perform filtered back-projection transformation on the W-row projection data obtained for each of the horizontal slice samples in the filtered back-projection transformation module using different window functions, and compare the printing evaluation indexes of different window functions obtained after passing through the filtered back-projection transformation module, the normalization processing module, the simulated printing horizontal slice acquisition module, and the printing evaluation index calculation module, and use the window function corresponding to the printing evaluation index closest to 0 among the printing evaluation indexes of different window functions as the optimal window function.

[0141] The specific implementation manner of the printing effect evaluation device applicable to volumetric bioprinting projection in this embodiment may refer to the description of the printing effect evaluation method applicable to volumetric bioprinting projection in the above embodiment, and will not be elaborated here.

[0142] As Figure 9 shown, an embodiment of the present invention provides an electronic device 300, including a memory 310 and a processor 320. The memory 310 is used to store one or more computer instructions, and the processor 320 is used to call and execute the one or more computer instructions, so as to implement any one of the above-mentioned printing effect evaluation methods applicable to volumetric bioprinting projection.

[0143] That is to say, the electronic device 300 includes: a processor 320 and a memory 310. Computer program instructions are stored in the memory 310. When the computer program instructions are run by the processor, the processor 320 is caused to execute any one of the above-mentioned printing effect evaluation methods applicable to volumetric bioprinting projection.

[0144] Further, as Figure 9 shown, the electronic device 300 further includes a network interface 330, an input device 340, a hard disk 350, and a display device 360.

[0145] Each of the above interfaces and devices can be interconnected through a bus architecture. The bus architecture can include any number of interconnected buses and bridges. Specifically, one or more central processing units (CPUs) represented by the processor 320 and various circuits of one or more memories represented by the memory 310 are connected together. The bus architecture can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. It can be understood that the bus architecture is used to achieve connection and communication between these components. In addition to the data bus, the bus architecture also includes a power bus, a control bus, and a status signal bus, which are well known in the art and thus will not be described in detail herein.

[0146] The network interface 330 can be connected to a network (such as the Internet, a local area network, etc.), obtain relevant data from the network, and can be stored in the hard disk 350.

[0147] The input device 340 can receive various instructions input by an operator and send them to the processor 320 for execution. The input device 340 can include a keyboard or a pointing device (for example, a mouse, a trackball, a touchpad, or a touch screen, etc.).

[0148] The display device 360 can display the results obtained by the processor 320 executing instructions.

[0149] The memory 310 is used to store programs and data necessary for the operation of the operating system, as well as data such as intermediate results during the calculation of the processor 320.

[0150] It can be understood that the memory 310 in the embodiments of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. The memory 310 of the devices and methods described herein is intended to include, but is not limited to, these and any other suitable types of memories.

[0151] In some embodiments, the memory 310 stores the following elements, executable modules, or data structures, or subsets thereof, or extended sets thereof: an operating system 311 and an application program 312.

[0152] Among them, the operating system 311 includes various system programs, such as the framework layer, the core library layer, the driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program 312 includes various application programs, such as a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present invention may be included in the application program 312.

[0153] When the above-mentioned processor 320 calls and executes the application programs and data stored in the memory 310, specifically, when it is the program or instruction stored in the application program 312, it will take M of the transverse slices obtained by equally spaced transverse slicing of the three-dimensional model along the Z-axis as transverse slice samples, perform a 360-degree Radon transform on each of the transverse slice samples, so that each of the transverse slice samples obtains W rows of projection data, and then perform filtered back-projection transformation on the W rows of projection data obtained for each of the transverse slice samples using a to-be-determined window function respectively, so as to obtain M back-projection reconstruction maps with resolutions corresponding to each transverse slice sample respectively. Normalize the pixel values of each of the back-projection reconstruction maps to integers from 0 to 255 to obtain M normalized reconstruction maps. Set a variable parameter critical curing brightness value. When the critical curing brightness value is set to different integers in [1, 254] respectively, set the uncured pixels in each of the normalized reconstruction maps to 0 and the cured pixels to 1 according to the critical curing brightness value, so as to correspondingly obtain M * 254 simulated printing transverse slices. Perform operations on the 254 simulated printing transverse slices at each height and the transverse slice samples at the corresponding height to obtain 254 similarity evaluation values corresponding to the 254 simulated printing transverse slices at each height. Calculate the average value of the M similarity evaluation values corresponding to the M simulated printing transverse slices corresponding to the same critical curing brightness value respectively, and take the average value closest to 0 as the printing evaluation index of the to-be-determined window function. Finally, compare the printing evaluation indexes of different window functions obtained by performing filtered back-projection transformation on the W rows of projection data obtained for each of the transverse slice samples using different window functions respectively and passing through the steps S4 to S7, and take the window function corresponding to the printing evaluation index closest to 0 among the printing evaluation indexes of different window functions as the optimal window function.

[0154] The printing effect evaluation method applicable to volumetric bioprinting projection disclosed in the above embodiments of the present invention can be applied to or implemented by the processor 320. The processor 320 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 320 or the instructions in the form of software. The above-mentioned processor 320 may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 310, and the processor 320 reads the information in the memory 310 and combines its hardware to complete the steps of the above method.

[0155] It can be understood that these embodiments described herein can be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, or other electronic units for performing the functions described in this application, or a combination thereof.

[0156] For software implementation, the techniques described herein can be implemented by modules (e.g., procedures, functions, etc.) that execute the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented within the processor or external to the processor.

[0157] Specifically, the processor 320 is further configured to read the computer program and execute any one of the above-described printing effect evaluation methods applicable to volumetric bioprinting projection.

[0158] This application also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, and the program instructions, when executed by a processor, cause the processor to execute the above method, such as the method executed by the above-described electronic device, which will not be elaborated herein.

[0159] Optionally, the storage medium involved in the present application, such as a computer-readable storage medium, can be non-volatile or volatile.

[0160] Optionally, the computer-readable storage medium may mainly include a storage program area and a storage data area. Among them, the storage program area may store an operating system, application programs required for at least one function, etc.; the storage data area may store data created according to the use of the blockchain node, etc. Among them, the blockchain referred to in the present application is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm. Blockchain, in essence, is a decentralized database, a series of data blocks generated by using cryptographic methods. Each data block contains information about a batch of network transactions, which is used to verify the validity (anti-counterfeiting) of the information and generate the next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, etc.

[0161] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0162] In several embodiments provided by the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0163] In addition, each functional unit in various embodiments of the present invention can be integrated in a processing unit, or each unit can be physically included separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a hardware plus a software functional unit.

[0164] The integrated unit implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional units stored in a storage medium include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute some steps of the transceiver method described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0165] The foregoing disclosures are only some preferred embodiments of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. Those of ordinary skill in the art can understand the entire or partial processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.

Claims

1. A method for evaluating printing effects in volumetric bioprinting projection, characterized in that, The method includes the steps of: S1. Taking M of the transverse slices obtained by equally spacing and laterally slicing a three-dimensional model along the Z-axis as transverse slice samples; wherein, each of the transverse slice samples is a transverse slice binary image, and M≥1; S2. Performing a 360-degree Radon transform on each of the transverse slice samples, so that each of the transverse slice samples obtains W rows of projection data; wherein, each row of projection data is the projection data of a Radon transform at an angle of each of the transverse slice samples. Among the W rows of projection data obtained by performing a Radon transform on the same transverse slice sample, the w-th row of projection data is the projection data of the Radon transform at an angle of (w - 1)*θ of the transverse slice sample, w = 1, 2... W, 0° < θ ≤ 1°, and W*θ = 360°; S3. Performing a filtered back-projection transform on the W rows of projection data obtained for each of the transverse slice samples using a window function to be determined, thereby obtaining M back-projection reconstruction images with resolutions respectively corresponding to and the same as each of the transverse slice samples; wherein, the back-projection reconstruction image is the light intensity cumulative distribution of the transverse slice corresponding to the height in the volume bioprinting projection of the printed object; S4. Normalizing the pixel values of each of the back-projection reconstruction images to integers from 0 to 255, obtaining M normalized reconstruction images; S5. Setting a variable parameter critical curing brightness value. When the critical curing brightness value is set to different integers in the range of [1, 254], setting the uncured pixels in each of the normalized reconstruction images to 0 and the cured pixels to 1 according to the critical curing brightness value, thereby correspondingly obtaining M*254 simulated printing transverse slices; wherein, the pixels in the normalized reconstruction image that are less than the critical curing brightness value are uncured pixels, and the pixels that are greater than or equal to the critical curing brightness value are cured pixels; S6. Performing an operation on the 254 simulated printing transverse slices at each height and the transverse slice samples at the corresponding height through the following formula (1) to obtain 254 similarity evaluation values corresponding to the 254 simulated printing transverse slices at each height: Where P is the similarity evaluation value, X and Y are the horizontal and vertical resolutions of the horizontal slice sample and the simulated printed horizontal slice; f o (x, y) is the value of the pixel at the position with coordinates (x, y) in the horizontal slice sample, with the coordinate system having the upper left corner as (1, 1) and the positive directions being downward and to the left; f(x, y) is the value of the pixel at the position with coordinates (x, y) in the simulated printed horizontal slice, with the coordinate system having the upper left corner as (1, 1) and the positive directions being downward and to the left; S7. Respectively calculating the average values of the M similarity evaluation values corresponding to the M simulated printing transverse slices under the same critical curing brightness value, and taking the average value closest to 0 as the printing evaluation index of the window function to be determined; S8. Comparing the printing evaluation indexes of different window functions obtained by performing a filtered back-projection transform on the W rows of projection data obtained for each of the transverse slice samples in step S3 using different window functions and passing through steps S4 to S7, and taking the window function corresponding to the printing evaluation index closest to 0 among the printing evaluation indexes of different window functions as the optimal window function.

2. The printing effect evaluation method applicable to volumetric bioprinting projection according to claim 1, characterized in that, In step S6, the following formula (2) is used to replace formula (1) to perform an operation on the 254 simulated printing transverse slices at each height and the transverse slice samples at the corresponding height, obtaining 254 similarity evaluation values corresponding to the 254 simulated printing transverse slices at each height: Among them, in the S7, the average value closest to 1 is used as the printing evaluation index of the to-be-determined window function; in the S8, the window function corresponding to the printing evaluation index closest to 1 among the printing evaluation indexes with different parameters is used as the optimal window function.

3. The printing effect evaluation method applicable to volumetric bioprinting projection according to claim 1, wherein, In the step S2, the Radon transform is sequentially performed on each angle of 360 degrees on the side of each of the transverse slice samples, so as to obtain the projection data of the Radon transform corresponding to each angle of each of the transverse slice samples.

4. A method for evaluating printing effect in volumetric bioprinting projection according to claim 1, characterized in that, In the step S3, the projection data with the projection data angle range greater than or equal to 180 degrees in the W-row projection data obtained for each of the transverse slice samples is respectively subjected to filtered back-projection transformation using the to-be-determined window function, so as to obtain M back-projection reconstruction images with resolutions respectively corresponding to each of the transverse slice samples.

5. A method for evaluating printing effects in volumetric bioprinting projection according to claim 1, characterized in that The different window functions refer to the Kaiser window functions under different parameters β, and the formula of the Kaiser window function is shown in formula (3): Among them, n = 1, 2, 3,..., N - 1, N represents the total length of the window function; I0 represents the first-kind Bessel function; β is a variable parameter; The M transverse slice samples respectively correspond to the transverse slices at the 50th layer, 100th layer, 150th layer, 200th layer, 250th layer, 300th layer, 350th layer, 400th layer, and 450th layer in height.

6. The printing effect evaluation method applicable to volumetric bioprinting projection according to claim 1, wherein, In the step S3, the Fourier transform, filtering, and inverse Fourier transform operations in the filtered back-projection transformation are respectively performed on the W-row projection data obtained for each of the transverse slice samples using the to-be-determined window function, specifically including: S31. Perform a fast Fourier transform on the W-row projection data obtained for each of the transverse slice samples; among them, the width N in the adopted fast Fourier transform is greater than or equal to the number of pixels of each row of projection data obtained for each of the transverse slice samples. S32. Perform a windowing operation on the ramp filter using the to-be-determined window function, and filter each row of projection data obtained by the fast Fourier transform of each of the transverse slice samples using the windowed ramp filter. S33. Perform an inverse fast Fourier transform on each row of projection data obtained by filtering each of the transverse slice samples, so as to obtain M back-projection reconstruction images with resolutions respectively corresponding to each of the transverse slice samples.

7. A method for evaluating printing effects in volumetric bioprinting projection according to claim 6, characterized in that, The step S32 specifically includes: S321. Perform discrete sampling on the ramp filter in the frequency domain; the number of sampling points is an even number greater than or equal to N and closest to N. S322. Perform point-by-point multiplication of the discretized ramp filter and the discretized to-be-determined window function with the same number of sampling points to perform the windowing operation. S323. Perform a translation operation on the data of the windowed ramp filter, so as to move the latter half of the ramp filter data to the head. S324. Perform a point-by-point multiplication of the ramp filter after windowing and translation with each row of projection data after the fast Fourier transform of each of the transverse slice samples to perform a filtering operation, and discard the extra points of the ramp filter after windowing and translation compared to the points of each row of projection data after the fast Fourier transform of each of the transverse slice samples.

8. A printing effect evaluation device applicable to volumetric bioprinting projection, characterized in that, Including: A sampling module, configured to use M of the transverse slices obtained by equally spacing transverse slicing of a three-dimensional model along the Z axis as transverse slice samples; wherein, each of the transverse slice samples is a transverse slice binary image, and M≥1; A projection data conversion module, configured to perform a 360-degree Radon transform on each of the transverse slice samples, so that each of the transverse slice samples obtains W rows of projection data; wherein, each row of projection data is the projection data of a Radon transform of an angle of each of the transverse slice samples, and among the W rows of projection data obtained by performing a Radon transform on the same transverse slice sample, the w-th row of projection data is the projection data of the Radon transform of the (w - 1)*θ angle of the transverse slice sample, w = 1, 2... W, 0° < θ ≤ 1°, and W*θ = 360°; A filtered backprojection transform module, configured to perform a filtered backprojection transform on the W rows of projection data obtained by each of the transverse slice samples using a window function to be determined, so as to obtain M backprojection reconstruction images with resolutions respectively corresponding to each of the transverse slice samples; wherein, the backprojection reconstruction image is the light intensity cumulative distribution of the transverse slice corresponding to the height of the printed object in the volumetric bioprinting projection; A normalization processing module, configured to normalize the pixel values of each of the backprojection reconstruction images to integers from 0 to 255 to obtain M normalized reconstruction images; A simulated printing transverse slice acquisition module, configured to set a variable parameter critical curing brightness value, and when the critical curing brightness value is set to different integers in [1, 254] respectively, set the uncured pixels in each of the normalized reconstruction images to 0 and the cured pixels to 1 according to the critical curing brightness value, so as to correspondingly obtain M*254 simulated printing transverse slices; wherein, the pixels in the normalized reconstruction image that are less than the critical curing brightness value are uncured pixels, and the pixels that are greater than or equal to the critical curing brightness value are cured pixels; A similarity evaluation value calculation module, configured to perform an operation on the 254 simulated printing transverse slices at each height and the transverse slice sample at the corresponding height through the following formula (1) to obtain 254 similarity evaluation values corresponding to the 254 simulated printing transverse slices at each height: Where P is the similarity evaluation value, X and Y are the horizontal and vertical resolutions of the horizontal slice sample and the simulated printed horizontal slice; f o (x, y) is the value of the pixel at the position with coordinates (x, y) in the horizontal slice sample, with the upper left corner as the coordinate (1, 1) and the positive directions being downward and leftward; f(x, y) is the value of the pixel at the position with coordinates (x, y) in the simulated printed horizontal slice, with the upper left corner as the coordinate (1, 1) and the positive directions being downward and leftward; A printing evaluation index calculation module, configured to calculate the average value of the M similarity evaluation values corresponding to the M simulated printing transverse slices corresponding to the same critical curing brightness value respectively, and use the average value closest to 0 as the printing evaluation index of the window function to be determined; An optimal window function determination module, which is configured to perform filtered back-projection transformation on the W-row projection data obtained for each of the transverse slice samples in the filtered back-projection transformation module using different window functions, and compare the printing evaluation metrics of different window functions obtained after passing through the filtered back-projection transformation module, the normalization processing module, the simulated printing transverse slice acquisition module, and the printing evaluation metric calculation module, and use the window function corresponding to the printing evaluation metric closest to 0 among the printing evaluation metrics of different window functions as the optimal window function.

9. An electronic device, characterized in that, It includes a processor and a memory. Among them, the memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions to execute the printing effect evaluation method applicable to volumetric bioprinting projection according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to execute the printing effect evaluation method applicable to volumetric bioprinting projection according to any one of claims 1-7.

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