A method and apparatus for computed tomography

Abstract

This invention belongs to the field of computed tomography (CT) scanning technology. It discloses a CT scanning method and apparatus. The CT scanning method includes: determining a final region of interest (ROI) set based on the area, volume, absorbed gradient energy, and entropy of possible poses of a preliminary ROI set from a pre-determined CT scan image; determining the positional entropy of all final ROIs in the final ROI set based on a global constructed image of the final ROI set; updating the final ROI set based on the positional entropy; and scanning the updated final ROI set according to a second focal point; wherein the first focal point is larger than the second focal point. This invention utilizes the adjustable focal point of a micro-focus X-ray source to provide a dual-focus hierarchical scanning method that balances global efficiency and local accuracy. Furthermore, it shortens the X-ray source movement path and improves the scanning efficiency of multiple ROIs.

Description

Technical Field

[0001] This invention belongs to the field of computed tomography scanning, and specifically relates to a computed tomography scanning method and apparatus. Background Technology

[0002] With the widespread application of complex samples such as nanomaterials and biological tissues, computed tomography (CT) imaging technology can simultaneously meet the needs of rapid global scanning and high-resolution local imaging. However, existing CT imaging technologies have the following limitations: Existing micro-CT imaging methods rely heavily on human experience for Region of Interest (ROI) selection, resulting in low automation and long processing times. In multi-ROI imaging, unoptimized equipment movement paths lead to frequent idle cycles of the robotic arm or stage, further extending imaging time. Finally, fixed sampling frequencies cannot adapt to different material thicknesses or properties (e.g., higher-density areas require denser sampling), leading to resource waste or data loss. Summary of the Invention

[0003] One object of the present invention is to provide a computed tomography scanning method that solves at least a portion of the aforementioned technical problems in the prior art.

[0004] Another object of the present invention is to provide a computed tomography (CT) scanning apparatus. A further object of the present invention is to provide an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the steps of the aforementioned CT scanning method. A further object of the present invention is to provide a readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the aforementioned CT scanning method.

[0005] To address the technical problems in the background section of this invention, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for scanning computed tomography, comprising: The final region of interest (ROI) set is determined based on the area, volume, absorbed gradient energy, and entropy of possible poses of the preliminary ROI set of the computed tomography scan images. The positional entropy of all final regions of interest in the final region of interest set is determined based on the global constructed image of the final region of interest set; wherein, the global constructed image is obtained by scanning the final region of interest set through a first focal point; Update the final set of regions of interest based on the location information entropy; The final set of regions of interest is updated according to the second focus scan; wherein the first focus is larger than the second focus.

[0006] In some embodiments of the present invention, determining the positional entropy of all final regions of interest in the final region of interest set based on the globally constructed image of the final region of interest set includes: The positional entropy of the current final region of interest is determined based on the location of the current region of interest and the location of the field of view center in the current scan round.

[0007] In some embodiments of the present invention, updating the final region of interest set based on the location information entropy includes: The imaging quality of the corresponding final region of interest is determined based on the location information entropy. The final set of regions of interest is selected based on the imaging quality. The scanning center for the next scanning round is determined based on the initial weights of the final regions of interest in the final set of filtered regions of interest. The final set of regions of interest is updated based on the scanning center.

[0008] In some embodiments of the present invention, determining the final region of interest set based on the area, volume, absorbed gradient energy, and entropy of possible poses of a preliminary region of interest set of a pre-determined computed tomographic scan image includes: Extract the area, volume, absorbed gradient energy, and user-defined values ​​of the preliminary region of interest from a pre-determined computed tomography scan image; Determine the weights for area, volume, absorbed gradient energy, and user-defined values; The entropy gain of the possible pose of the preliminary region of interest is determined based on the current scan center of the preliminary region of interest; The preliminary regions of interest are selected based on the entropy gain, area weight, volume weight, and the weight of the absorbed gradient energy, and the final set of regions of interest is determined.

[0009] In some embodiments of the present invention, the preliminary region of interest is screened based on the entropy gain, the area weight, the volume weight, and the weight of the absorbed gradient energy to determine the final set of regions of interest, including: The scanning trajectory of multiple preliminary regions of interest is determined based on the entropy gain; The multiple preliminary regions of interest are scanned according to the scanning trajectory, and the preliminary regions of interest are filtered according to the weights of the area, volume, and absorbed gradient energy to determine the final region of interest.

[0010] In some embodiments of the present invention, the scanning trajectory of multiple preliminary regions of interest is determined based on the gain of the entropy, including: The entropy gain of all regions of interest under the current scan center is determined based on the current scan center of the initial region of interest; The next scan center is determined based on the maximum value of the entropy gain of the possible poses; The scanning trajectory is determined based on the next scanning center.

[0011] In some embodiments of the present invention, the determination of the weights for area, volume, absorbed gradient energy, and user-defined values ​​includes: The weights for the area, volume, absorbed gradient energy, and user-defined values ​​of the initial region of interest are determined. The weights of the area, volume, absorbed gradient energy, and user-defined value are determined sequentially based on the characteristics of the area, volume, absorbed gradient energy, and user-defined value.

[0012] In some embodiments of the present invention, the step of determining the preliminary region of interest includes: Extract morphological features and intensity information from computed tomography scan images; Based on the morphological features and intensity information, several preliminary regions of interest were determined.

[0013] In a second aspect, the present invention provides a computed tomography (CT) scanning apparatus, the apparatus comprising: The final set determination module is used to determine the final region of interest set based on the area, volume, absorbed gradient energy, and entropy of possible poses of the preliminary region of interest set of the pre-determined computed tomography scan image. The location information entropy determination module is used to determine the location information entropy of all final regions of interest in the final region of interest set based on the global constructed image of the final region of interest set; wherein, the global constructed image is obtained by scanning the final region of interest set through a first focal point; The final set update module is used to update the final region of interest set based on the location information entropy; The final set scanning module is used to scan and update the final set of regions of interest according to the second focus; wherein the first focus is greater than the second focus.

[0014] In some embodiments of the present invention, the location information entropy determination module includes: The location information entropy determination unit is used to determine the location information entropy of the current final region of interest based on the location of the current final region of interest and the location of the field of view center in the current scanning round.

[0015] In some embodiments of the present invention, the final set update module includes: An imaging quality determination unit is used to determine the imaging quality of the corresponding final region of interest based on the location information entropy. The final set filtering unit is used to filter the final region of interest set based on the imaging quality. The next scan center determination unit is used to determine the scan center of the next scan round based on the initial weights of the final regions of interest in the final region of interest set after filtering. The final set update unit is used to update the final region of interest set based on the scanning center.

[0016] In some embodiments of the present invention, the final set determination module includes: The parameter extraction unit is used to extract the area, volume, absorbed gradient energy, and user-defined values ​​of the preliminary region of interest in a pre-determined computed tomography scan image. The weight determination unit is used to determine the weights of area, volume, absorbed gradient energy, and user-defined values. The gain determination first unit is used to determine the entropy gain of the possible pose of the preliminary region of interest based on the current scan center of the preliminary region of interest; The final set determination unit is used to filter the preliminary region of interest based on the entropy gain, area weight, volume weight, and absorption gradient energy weight, and determine the final region of interest set.

[0017] In some embodiments of the present invention, the final set determination unit includes: A scanning trajectory determination unit is used to determine the scanning trajectory of multiple preliminary regions of interest based on the entropy gain; The final set determines the sub-unit, which is used to scan the multiple preliminary regions of interest according to the scanning trajectory, and to filter the preliminary regions of interest according to the weight of the area, the weight of the volume, and the weight of the absorbed gradient energy to determine the final region of interest.

[0018] In some embodiments of the present invention, the scanning trajectory determination unit includes: The gain determination second unit is used to determine the entropy gain of all regions of interest under the current scan center based on the current scan center of the preliminary region of interest; The next scan center determination unit is used to determine the next scan center based on the maximum value of the entropy gain of the possible poses; The scanning trajectory determination subunit is used to determine the scanning trajectory based on the next scanning center.

[0019] In some embodiments of the present invention, the weight determination unit includes: The first sub-unit for weight determination is used to determine the weights of the area, volume, absorbed gradient energy, and user-defined values ​​of the initial region of interest. The second sub-unit for weight determination is used to determine the weights of the area, volume, absorbed gradient energy, and user-defined value in sequence based on the characteristics of the area, volume, absorbed gradient energy, and user-defined value.

[0020] In some embodiments of the present invention, a computed tomography scanning apparatus further includes: A preliminary region determination module is used to determine the preliminary region of interest; the preliminary region determination module includes: The image feature extraction unit is used to extract morphological features and intensity information from computed tomography scan images; The preliminary region determination unit is used to determine multiple preliminary regions of interest based on the morphological features and intensity information.

[0021] Thirdly, the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of a computed tomography scanning method.

[0022] Fourthly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a computed tomography scanning method.

[0023] Fifthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a computed tomography scanning method.

[0024] As described above, embodiments of the present invention provide a method and apparatus for scanning computed tomography (CT). The method includes: first, determining a final region of interest (ROI) set based on the area, volume, absorbed gradient energy, and entropy of possible poses of a preliminary ROI set of a pre-determined CT scan image; next, determining the positional entropy of all final ROIs in the final ROI set based on a global constructed image of the final ROI set; wherein the global constructed image is obtained by scanning the final ROI set through a first focal point; updating the final ROI set based on the positional entropy; and finally, scanning the updated final ROI set according to a second focal point; wherein the first focal point is larger than the second focal point.

[0025] This invention proposes a computed tomography scanning method that utilizes the adjustable focus of a microfocus X-ray source to provide a dual-focus hierarchical scanning method. This method balances global efficiency and local accuracy. Furthermore, it shortens the X-ray source movement path and improves scanning efficiency across multiple regions of interest (ROIs). Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a computed tomography scanning method according to an embodiment of the present invention. Figure 1 ; Figure 2 A flowchart illustrating step 300 in an embodiment of the present invention; Figure 3 A flowchart illustrating step 100 in an embodiment of the present invention; Figure 4 A flowchart illustrating step 104 in an embodiment of the present invention; Figure 5 A flowchart illustrating step 1041 in an embodiment of the present invention; Figure 6 A flowchart illustrating step 102 in an embodiment of the present invention; Figure 7 A flowchart illustrating a computed tomography scanning method according to an embodiment of the present invention. Figure 2 ; Figure 8 A flowchart illustrating step 500 in an embodiment of the present invention; Figure 9This is a flowchart illustrating a computed tomography scanning method according to a specific embodiment of the present invention. Figure 10 This is a logic diagram of a computed tomography scanning method according to a specific embodiment of the present invention; Figure 11 A block of a computed tomography scanning device in an embodiment of the present invention. Figure 1 ; Figure 12 This is a block diagram of the final set update module 30 in an embodiment of the present invention; Figure 13 A block diagram of the final set determination module 10 in an embodiment of the present invention; Figure 14 This is a block diagram of the final set determination unit 10d in an embodiment of the present invention; Figure 15 This is a block diagram of the scanning trajectory determination unit 10d1 in an embodiment of the present invention; Figure 16 This is a block diagram of the weight determination unit 10b in an embodiment of the present invention; Figure 17 A block of a computed tomography scanning device in an embodiment of the present invention. Figure 2 ; Figure 18 A block diagram of the preliminary region determination module 50 in an embodiment of the present invention; Figure 19 This is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0030] It should be noted that the terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Without conflict, the embodiments and features in the embodiments of this invention can be combined with each other. The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] To address at least some of the technical problems in the prior art, embodiments of the present invention provide a specific implementation of a computed tomography scanning method, see [link to details]. Figure 1 The method specifically includes the following: Step 100: Determine the final region of interest set based on the area, volume, absorbed gradient energy, and entropy of possible poses of the preliminary region of interest set of the pre-determined computed tomography scan image; Step 200: Determine the positional information entropy of all final regions of interest in the final region of interest set based on the global constructed image of the final region of interest set; wherein, the global constructed image is obtained by scanning the final region of interest set through a first focal point; Step 300: Update the final set of regions of interest based on the location information entropy; Step 400: Update the final set of regions of interest according to the second focus; wherein the first focus is greater than the second focus.

[0032] As described above, the present invention provides a computed tomography scanning method comprising: first, determining a final region of interest set based on the area, volume, absorbed gradient energy, and entropy of possible poses of a preliminary region of interest set of a pre-determined computed tomography scan image; next, determining the positional entropy of all final regions of interest in the final region of interest set based on a global constructed image of the final region of interest set; wherein the global constructed image is obtained by scanning the final region of interest set through a first focal point; updating the final region of interest set based on the positional entropy; and finally, scanning the updated final region of interest set according to a second focal point; wherein the first focal point is larger than the second focal point.

[0033] This invention proposes a computed tomography scanning method that utilizes the adjustable focus of a microfocus X-ray source to provide a dual-focus hierarchical scanning method. This method balances global efficiency and local accuracy. Furthermore, it shortens the X-ray source movement path and improves scanning efficiency across multiple regions of interest (ROIs).

[0034] In addition, the present invention can automatically identify regions of interest in computed tomography scan images, and greatly improves the efficiency and accuracy of region of interest selection while taking into account both global efficiency and local accuracy.

[0035] Understandably, the Region of Interest (ROI) in step 100 is a concept in image processing and computer vision, referring to a specific area in an image or video that the user is focused on. This region contains the key part for analysis or processing, while other irrelevant areas can be ignored. Using ROI improves computational efficiency because, when processing an image, only the ROI is computed, rather than processing the entire image.

[0036] The location information entropy in step 200 is used to characterize the current imaging quality of the computed tomography. This location information entropy is calculated based on the distance from the region to the center of the field of view of all performed scans. The closer the distance, the more the value of the location information entropy decreases, indicating higher imaging quality.

[0037] Step 300 is an iterative process similar to steps 200 and 400. Specifically, in step 300, based on the current scan center dataset, all ROIs that do not meet the quality standards are identified. Among these substandard ROIs, the one with the highest initial weight is selected as the new scan center. The entropy gain of all possible pose ROIs is recalculated, and the pose scan center with the largest entropy gain is added to the trajectory. Then, angle data is gradually accumulated using a greedy algorithm to ensure that the key view of each ROI is covered, while avoiding conflicts caused by the needs of multiple ROIs.

[0038] For step 400, which is essentially a further fine scan compared to the global image construction in step 200, specifically, based on the first focal point, the focal point is adjusted to 0.5μm, and the device is moved to allow the ROI of this round to enter the scanning center X-ray source to move along the planned path. By combining dynamic focal point and sampling parameters, efficient and fine imaging is achieved, and a new projection dataset is obtained.

[0039] Preferably, the first focal point is 5 μm and the second focal point is 0.5 μm.

[0040] In some embodiments of the present invention, step 200 includes: The positional entropy of the current final region of interest is determined based on the location of the current region of interest and the location of the field of view center in the current scan round.

[0041] The location information entropy is calculated based on the distance from this region to the center of the field of view of all scanned areas. Specifically, the location information entropy is determined by the following formula:

[0042] In the formula, S is the location information entropy, where p k Let c be the location of the k-th region of interest. i Let c be the center of the field of view in the i-th scan, and α and σ be coefficients. α is a preset normalization constant, representing the relationship between a single scan and the center point c of the field of view. i The theoretical maximum quality contribution. σ is the spatial decay coefficient, which controls the rate at which the scan contribution decays with distance. The larger the σ value, the slower the quality improvement contribution of this scan decays with distance. Parameter m is the total number of scans.

[0043] In some embodiments of the present invention, see Figure 2 Step 300 includes: Step 301: Determine the imaging quality of the corresponding final region of interest based on the location information entropy; Specifically, by using the ROI with the highest weight as the scan center, the imaging quality Q of each ROI is determined by predicting the positional information entropy that can be achieved by performing 3D reconstruction in the current state. k .

[0044] Step 302: Filter the final set of regions of interest based on the imaging quality; Find all Q k threshold The ROI of (quality threshold) is the ROI of the area where the quality does not meet the standard.

[0045] Step 303: Determine the scanning center for the next scanning round based on the initial weights of the final regions of interest in the final region of interest set after filtering; Step 304: Update the final region of interest set based on the scan center.

[0046] In steps 303 and 304, among the ROIs that did not meet the criteria, their initial weights w are selected. k The highest entropy gain is used as the new scan center. The entropy gain of all possible pose ROIs is recalculated, and the pose scan center with the largest entropy gain is selected to be added to the trajectory.

[0047] In some embodiments of the present invention, see Figure 3 Step 100 includes: Step 101: Extract the area, volume, absorbed gradient energy, and user-defined value of the preliminary region of interest from the pre-determined computed tomography scan image; ​User-defined values ​​refer to the focus of the detection task. For example, uniform weighting is used if the focus is on defects, while high-frequency weighting is used if the focus is on dimensional measurement. If there are additional needs, such as defect detection or dimensional measurement, the rapidly reconstructed 2D slices can be preprocessed with simple noise reduction and then subjected to Fourier transform. When edge or dimensional detection is required, high-frequency information is assigned high weight. If defect detection is required and there are no special frequency requirements, uniform weighting can be applied.

[0048] Step 102: Determine the weights for area, volume, absorbed gradient energy, and user-defined values; The aforementioned weight w k Combining confidence level and key morphological features, its calculation method can be expressed as follows: This is an adjustment factor used to determine the relative weights of confidence level and morphological features in the final weights. This refers to a weighting function designed according to a specific detection task. If the task focuses on the absorbance coefficient of the sample, then the function can be designed as follows:

[0049] in, Indicates the first k Projection data for each ROI.

[0050]

[0051] in The absorption coefficient of the k-th ROI Priority weights for each ROI.

[0052] Step 103: Determine the entropy gain of the possible poses of the preliminary region of interest based on the current scan center of the preliminary region of interest; Step 104: Filter the preliminary region of interest based on the entropy gain, area weight, volume weight, and the weight of the absorbed gradient energy to determine the final region of interest.

[0053] In some embodiments of the present invention, see Figure 4 Step 104 includes: Step 1041: Determine the scanning trajectory of multiple preliminary regions of interest based on the entropy gain; Step 1042: Scan the plurality of preliminary regions of interest according to the scanning trajectory, and filter the preliminary regions of interest according to the weight of the area, the weight of the volume, and the weight of the absorbed gradient energy to determine the final region of interest.

[0054] In some embodiments of the present invention, see Figure 5 Step 1041 includes: Step 10411: Determine the entropy gain of all regions of interest under the current scan center based on the current scan center of the preliminary region of interest; Step 10412: Determine the next scan center based on the maximum value of the entropy gain of the possible poses; Step 10413: Determine the scanning trajectory based on the next scanning center.

[0055] In steps 10411 to 10413, for multiple ROIs: for independent feature processing (first iteration): in the first iteration, the optimal acquisition pose (rotation angle around the z-axis and y and z-axis offset of the sample stage) is selected individually for global optimization (subsequent iterations): In subsequent iterations, the algorithm recalculates the entropy gain of all possible poses based on the current angle and selects the pose with the maximum entropy gain to add to the trajectory. By gradually accumulating angles using a greedy algorithm, the algorithm ensures that the critical view of each ROI is covered, while avoiding conflicts caused by the needs of multiple ROIs.

[0056] In some embodiments of the present invention, see Figure 6 Step 102 includes: Step 1021: Determine the weights of the area, volume, absorbed gradient energy, and user-defined values ​​of the preliminary region of interest; Step 1022: Determine the weights of the area, volume, absorbed gradient energy, and user-defined value in sequence based on the characteristics of the area, volume, absorbed gradient energy, and user-defined value.

[0057] In some embodiments of the present invention, see Figure 7 A computed tomography scanning method further includes: Step 500: Determine the preliminary region of interest; then, see... Figure 8 Step 500 includes: Step 501: Extract morphological features and intensity information from the computed tomography scan images; Specifically, firstly, starting from the slice structure quickly reconstructed from the computed tomography scan image, the region with large absorption (usually a metallic region) is segmented using the OTU thresholding method to obtain its binarized image, and its morphological features (centroid, area, eccentricity (if the ROI is roughly circular) and intensity information (average gray value, contrast, gray value gradient change, etc.) are extracted. Then, through correlation matrix analysis and variance thresholding, some highly correlated or insignificant features are removed.

[0058] Step 502: Determine multiple preliminary regions of interest based on the morphological features and intensity information.

[0059] First, visualize the scatter plots of the first 2-3 principal components to observe the ROI distribution and select ROIs. Then, use fuzzy clustering to assign weights to each ROI, quantifying its confidence level in belonging to each cluster, and selecting regions with high confidence levels as ROIs. Simultaneously, allow users to add, delete, merge, and manually select ROIs on the interface until a preliminary set of ROIs is obtained.

[0060] As described above, the present invention provides a computed tomography scanning method comprising: first, determining a final region of interest set based on the area, volume, absorbed gradient energy, and entropy of possible poses of a preliminary region of interest set of a pre-determined computed tomography scan image; next, determining the positional entropy of all final regions of interest in the final region of interest set based on a global constructed image of the final region of interest set; wherein the global constructed image is obtained by scanning the final region of interest set through a first focal point; updating the final region of interest set based on the positional entropy; and finally, scanning the updated final region of interest set according to a second focal point; wherein the first focal point is larger than the second focal point.

[0061] This invention proposes a computed tomography scanning method that utilizes the adjustable focus of a microfocus X-ray source to provide a dual-focus hierarchical scanning method. This method balances global efficiency and local accuracy. Furthermore, it shortens the X-ray source movement path and improves scanning efficiency across multiple regions of interest (ROIs).

[0062] In addition, the present invention can automatically identify regions of interest in computed tomography scan images, and greatly improves the efficiency and accuracy of region of interest selection while taking into account both global efficiency and local accuracy.

[0063] For further explanation of the plan, see Figure 9 as well as Figure 10 Taking chip defect detection as an example, this invention provides a specific implementation method for a computed tomography scanning method, which includes the following:

[0064] S1: Perform the initial scan.

[0065] The global roughness structure of the sample was quickly acquired using a medium focal length (5 μm), and geometric parameters (material type, thickness) were extracted.

[0066] S2: Smart ROI selection.

[0067] The system automatically identifies ROIs through image processing algorithms (edge ​​detection / AI segmentation), supports manual correction, and improves selection efficiency and accuracy.

[0068] Specifically, starting with the rapidly reconstructed slice structure, edge detection is performed on the pre-scanned 2D or 3D slices. The OTU thresholding method is used to segment regions with high absorption (preferably metallic regions) to obtain their binarized images, thus obtaining preliminary metallic region masks. Morphological features and intensity information (average gray value, contrast, gray value / gradient change, etc.) are then extracted. Correlation matrix analysis and variance thresholding are used to remove some highly correlated or insignificant features.

[0069] Next, dimensionality reduction is achieved using PCA, and the scatter plots of the first 2-3 principal components are visualized to observe the ROI distribution. Fuzzy clustering is then used to assign weights to each ROI, quantifying its confidence in belonging to each cluster, and selecting regions with high confidence as ROIs. Users can also add, delete, merge, and manually select ROIs on the interface until the final set of ROIs is obtained.

[0070] The aforementioned morphological features include: centroid coordinates, area (or number of voxels), aspect ratio, and eccentricity (for determining solder balls / circular pads).

[0071] The aforementioned grayscale value / gradient changes include: average grayscale, local contrast, and grayscale gradient energy (edge ​​sharpness index).

[0072] Weighting: For each ROI, weights are assigned based on the following dimensions: Area or volume: Larger regions can be assigned a higher initial weight; Absorbed gradient energy: higher weights are applied at edges or where absorption is stronger; The inspection task focuses on two priorities: uniform weighting for defects and high-frequency weighting for dimensional measurements. If additional needs arise, such as defect detection or dimensional measurement, the rapidly reconstructed 2D slices can undergo simple noise reduction preprocessing followed by Fourier transform. When edge or dimensional detection is required, high-frequency information is assigned high weight. If defect detection is required but no specific frequency requirements exist, uniform weighting can be applied.

[0073] In defect detection tasks: Prioritize ROIs that belong to "abnormal clusters" (such as rough edges and abrupt grayscale changes) and have high confidence, and use area-weighted inverse methods to find ROIs in small regions.

[0074] In the measurement of solder ball or circular pad geometry: more reliance is placed on the high-frequency edge information of the template, so the weight of the absorption gradient value can be increased, and the area weight can be moderately increased to ensure that key large areas are fully scanned.

[0075] For the three weighting coefficients mentioned above, by calculating the entropy of the three sets of weights, a lower entropy indicates a more concentrated information distribution (greater discrimination), and a higher coefficient is assigned to determine the final w. k For example, the weights of the absorption coefficient are as follows:

[0076] in, Indicates the first k Projection data for each ROI.

[0077]

[0078] in The absorption coefficient of the k-th ROI Priority weights for each ROI.

[0079] S3: Entropy-based optimized scan trajectory.

[0080] For multiple Regions of Interest (ROIs), a dataset D is first initialized. For independent feature processing (first iteration): Quality prediction is performed first. In the first iteration, the ROI with the highest weight is used as the scan center, and the quality achievable for 3D reconstruction of each ROI in its current state is predicted. The reconstruction quality prediction module evaluates the current imaging quality by calculating the location entropy of each ROI. This location entropy is calculated based on the distance from the region to the center of the field of view of all scanned regions; the closer the distance, the greater the decrease in entropy, indicating higher imaging quality.

[0081] Next, global optimization (subsequent iterations) is performed: In subsequent iterations, the algorithm identifies all ROIs that do not meet the quality standards based on the current dataset. Among these substandard ROIs, the one with the highest initial weight is selected as the new scan center. The entropy gain of all ROIs is recalculated, and the scan center with the largest entropy gain is added to the trajectory. Finally, a greedy algorithm is used to gradually accumulate data, ensuring that the key view of each ROI is covered, while avoiding conflicts caused by the needs of multiple ROIs.

[0082] S4: Fine scan.

[0083] Based on the parameters, the focus is adjusted to 0.5μm, and the device is moved to allow the ROI of this round to enter the scanning center. The X-ray source moves along the planned path. By combining the dynamic focus and sampling parameters, efficient and fine imaging is achieved, and a new projection dataset P_new is obtained.

[0084] S5: Update the scanned dataset: D = D∪P_new (projected data), and repeat steps S2 to S5.

[0085] As described above, the present invention provides a computed tomography scanning method, comprising: first, determining a final region of interest set based on the area, volume, absorbed gradient energy, and entropy of possible poses of a preliminary region of interest set of a pre-determined computed tomography scan image; next, determining the positional entropy of all final regions of interest in the final region of interest set based on a global constructed image of the final region of interest set; wherein the global constructed image is obtained by scanning the final region of interest set through a first focal point; updating the final region of interest set based on the positional entropy; and finally, scanning the updated final region of interest set according to a second focal point; wherein the first focal point is larger than the second focal point.

[0086] This invention proposes a computed tomography scanning method that utilizes the adjustable focus of a microfocus X-ray source to provide a dual-focus hierarchical scanning method. This method balances global efficiency and local accuracy. Furthermore, it shortens the X-ray source movement path and improves scanning efficiency across multiple regions of interest (ROIs).

[0087] In addition, the present invention can automatically identify regions of interest in computed tomography scan images, and greatly improves the efficiency and accuracy of region of interest selection while taking into account both global efficiency and local accuracy.

[0088] Based on the same inventive concept, embodiments of the present invention also provide a computed tomography (CT) scanning apparatus, which can be used to implement the methods described in the above embodiments, as shown in the following embodiments. Since the principle of the CT scanning apparatus in solving the problem is similar to that of the CT scanning method, the implementation of the CT scanning apparatus can refer to the implementation of the CT scanning method, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0089] Embodiments of the present invention provide a specific implementation of a computed tomography (CT) scanning device capable of implementing a computed tomography scanning method, see below. Figure 11 A computed tomography (CT) scanning device specifically includes the following components: The final set determination module 10 is used to determine the final region of interest set based on the area, volume, absorbed gradient energy, and entropy of possible poses of the preliminary region of interest set of the pre-determined computed tomography scan image. The location information entropy determination module 20 is used to determine the location information entropy of all final regions of interest in the final region of interest set based on the global constructed image of the final region of interest set; wherein, the global constructed image is obtained by scanning the final region of interest set through a first focal point; The final set update module 30 is used to update the final region of interest set according to the location information entropy; The final set scanning module 40 is used to scan and update the final set of regions of interest according to the second focus; wherein the first focus is greater than the second focus.

[0090] In some embodiments of the present invention, the location information entropy determination module 20 includes: The location information entropy determination unit is used to determine the location information entropy of the current final region of interest based on the location of the current final region of interest and the location of the field of view center in the current scanning round.

[0091] In some embodiments of the present invention, see Figure 12 The final set update module 30 includes: The imaging quality determination unit 30a is used to determine the imaging quality of the corresponding final region of interest based on the location information entropy. The final set filtering unit 30b is used to filter the final region of interest set according to the imaging quality. The next scan center determination unit 30c is used to determine the scan center of the next scan round based on the initial weight of the final region of interest in the final region of interest set after filtering. The final set update unit 30d is used to update the final region of interest set according to the scanning center.

[0092] In some embodiments of the present invention, see Figure 13 The final set determination module 10 includes: The parameter extraction unit 10a is used to extract the area, volume, absorbed gradient energy, and user-defined values ​​of the preliminary region of interest in a pre-determined computed tomography scan image. The weight determination unit 10b is used to determine the weights of the area, volume, absorbed gradient energy, and user-defined values. Gain determination first unit 10c is used to determine the entropy gain of the possible pose of the preliminary region of interest based on the current scanning center of the preliminary region of interest; The final set determination unit 10d is used to filter the preliminary region of interest based on the entropy gain, area weight, volume weight, and absorption gradient energy weight, and determine the final region of interest set.

[0093] In some embodiments of the present invention, see Figure 14 The final set of determined unit 10d includes: The scanning trajectory determination unit 10d1 is used to determine the scanning trajectory of multiple preliminary regions of interest based on the gain of the entropy. The final set determines sub-unit 10d2, which is used to scan the multiple preliminary regions of interest according to the scanning trajectory, and to filter the preliminary regions of interest according to the weight of the area, the weight of the volume, and the weight of the absorbed gradient energy to determine the final region of interest.

[0094] In some embodiments of the present invention, see Figure 15 The scanning trajectory determination unit 10d1 includes: The gain determination second unit 10d11 is used to determine the entropy gain of all regions of interest under the current scan center based on the current scan center of the preliminary region of interest; The next scan center determination unit 10d12 is used to determine the next scan center based on the maximum value of the entropy gain of the possible poses; The scanning trajectory determination subunit 10d13 is used to determine the scanning trajectory based on the next scanning center.

[0095] In some embodiments of the present invention, see Figure 16 The weight determination unit 10b includes: The first subunit 10b1 for determining weights is used to determine the weights of the area, volume, absorbed gradient energy, and user-defined values ​​of the preliminary region of interest. The second subunit 10b2 for determining weights is used to sequentially determine the weights of the area, volume, absorbed gradient energy, and user-defined value based on the characteristics of the area, volume, absorbed gradient energy, and user-defined value.

[0096] In some embodiments of the present invention, see Figure 17 A computed tomography (CT) scanning device further includes: Preliminary region determination module 50 is used to determine the preliminary region of interest; see also Figure 18 The preliminary region determination module 50 includes: Image feature extraction unit 50a is used to extract morphological features and intensity information of computed tomography scan images; The preliminary region determination unit 50b is used to determine multiple preliminary regions of interest based on the morphological features and intensity information.

[0097] As described above, embodiments of the present invention provide a computed tomography scanning apparatus, comprising: a final set determination module, configured to determine a final region of interest (ROI) set based on the area, volume, absorbed gradient energy, and entropy of possible poses of a pre-determined preliminary ROI set of a computed tomography scan image; a position information entropy determination module, configured to determine the position information entropy of all final ROIs in the final ROI set based on a global constructed image of the final ROI set; wherein the global constructed image is obtained by scanning the final ROI set through a first focal point; a final set update module, configured to update the final ROI set based on the position information entropy; and a final set scanning module, configured to scan the updated final ROI set according to a second focal point; wherein the first focal point is larger than the second focal point.

[0098] This invention proposes a computed tomography scanning device that utilizes the adjustable focus of a microfocus X-ray source to provide a dual-focus hierarchical scanning method. This method balances global efficiency and local accuracy. Furthermore, it shortens the X-ray source movement path and improves scanning efficiency across multiple regions of interest (ROIs).

[0099] In addition, the present invention can automatically identify regions of interest in computed tomography scan images, and greatly improves the efficiency and accuracy of region of interest selection while taking into account both global efficiency and local accuracy.

[0100] Embodiments of the present invention also provide a specific implementation of an electronic device capable of implementing all steps of the computed tomography scanning method described in the above embodiments, see [link to specific implementation]. Figure 19 The electronic devices specifically include the following: Processor 1201, memory 1202, communications interface 1203, and bus 1204; The processor 1201, memory 1202, and communication interface 1203 communicate with each other via bus 1204; the communication interface 1203 is used to realize information transmission between server-side devices and user-side devices and other related devices. The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, it implements all the steps in the computed tomography scanning method in the above embodiment. For example, when the processor executes the computer program, it implements the following steps: The final region of interest (ROI) set is determined based on the area, volume, absorbed gradient energy, and entropy of possible poses of the preliminary ROI set of the computed tomography scan images. The positional entropy of all final regions of interest in the final region of interest set is determined based on the global constructed image of the final region of interest set; wherein, the global constructed image is obtained by scanning the final region of interest set through a first focal point; Update the final set of regions of interest based on the location information entropy; The final set of regions of interest is updated according to the second focus scan; wherein the first focus is larger than the second focus.

[0101] Embodiments of the present invention also provide a computer-readable storage medium capable of implementing all steps of the computed tomography scanning method in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the computed tomography scanning method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: The final region of interest (ROI) set is determined based on the area, volume, absorbed gradient energy, and entropy of possible poses of the preliminary ROI set of the computed tomography scan images. The positional entropy of all final regions of interest in the final region of interest set is determined based on the global constructed image of the final region of interest set; wherein, the global constructed image is obtained by scanning the final region of interest set through a first focal point; Update the final set of regions of interest based on the location information entropy; The final set of regions of interest is updated according to the second focus scan; wherein the first focus is larger than the second focus.

[0102] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are relatively simple in description because they are fundamentally similar to method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0103] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0104] While this invention provides method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual device or user terminal product execution, the method can be executed in the order shown in the embodiments or drawings or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0105] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware components, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0106] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0107] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0108] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0109] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, system embodiments are basically similar to method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. In the description of this specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments in this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0110] The above description is merely an embodiment of the present specification and is not intended to limit the embodiments of the present specification. For those skilled in the art, various modifications and variations can be made to the embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of the present specification should be included within the scope of the claims of the embodiments of the present specification.

Claims

1. A method for scanning with computed tomography, characterized in that, include: The final region of interest (ROI) set is determined based on the area, volume, absorbed gradient energy, and entropy of possible poses of the preliminary ROI set of the computed tomography scan images. The positional entropy of all final regions of interest in the final region of interest set is determined based on the global constructed image of the final region of interest set; wherein, the global constructed image is obtained by scanning the final region of interest set through a first focal point; Update the final set of regions of interest based on the location information entropy; The final set of regions of interest is updated according to the second focus scan; wherein the first focus is larger than the second focus.

2. The computed tomography scanning method according to claim 1, characterized in that, Determine the positional entropy of all final regions of interest (ROIs) in the final ROI set based on the globally constructed image of the final ROI set, including: The positional entropy of the current final region of interest is determined based on the location of the current region of interest and the location of the field of view center in the current scan round.

3. The computed tomography scanning method according to claim 1, characterized in that, Updating the final set of regions of interest based on the location information entropy includes: The imaging quality of the corresponding final region of interest is determined based on the location information entropy. The final set of regions of interest is selected based on the imaging quality. The scanning center for the next scanning round is determined based on the initial weights of the final regions of interest in the final set of filtered regions of interest. The final set of regions of interest is updated based on the scanning center.

4. The computed tomography scanning method according to claim 1, characterized in that, The process of determining the final region of interest (ROI) set based on the area, volume, absorbed gradient energy, and entropy of possible poses of the preliminary ROI set from the pre-determined computed tomography scan image includes: Extract the area, volume, absorbed gradient energy, and user-defined values ​​of the preliminary region of interest from a pre-determined computed tomography scan image; Determine the weights for area, volume, absorbed gradient energy, and user-defined values; The entropy gain of the possible pose of the preliminary region of interest is determined based on the current scan center of the preliminary region of interest; The preliminary regions of interest are selected based on the entropy gain, area weight, volume weight, and the weight of the absorbed gradient energy, and the final set of regions of interest is determined.

5. The computed tomography scanning method according to claim 4, characterized in that, The preliminary regions of interest are filtered based on the entropy gain, area weight, volume weight, and absorbed gradient energy weight to determine the final set of regions of interest, including: The scanning trajectory of multiple preliminary regions of interest is determined based on the entropy gain; The multiple preliminary regions of interest are scanned according to the scanning trajectory, and the preliminary regions of interest are filtered according to the weights of the area, volume, and absorbed gradient energy to determine the final region of interest.

6. The computed tomography scanning method according to claim 5, characterized in that, Based on the entropy gain, the scanning trajectory of multiple preliminary regions of interest is determined, including: The entropy gain of all regions of interest under the current scan center is determined based on the current scan center of the initial region of interest; The next scan center is determined based on the maximum value of the entropy gain of the possible poses; The scanning trajectory is determined based on the next scanning center.

7. The computed tomography scanning method according to claim 4, characterized in that, The weights for determining the area, volume, absorbed gradient energy, and user-defined values ​​include: The weights for the area, volume, absorbed gradient energy, and user-defined values ​​of the initial region of interest are determined. The weights of the area, volume, absorbed gradient energy, and user-defined value are determined sequentially based on the characteristics of the area, volume, absorbed gradient energy, and user-defined value.

8. The computed tomography scanning method according to claim 1, characterized in that, The steps for determining the preliminary region of interest include: Extract morphological features and intensity information from computed tomography scan images; Based on the morphological features and intensity information, several preliminary regions of interest were determined.

9. A computed tomography (CT) scanning device, characterized in that, include: The final set determination module is used to determine the final region of interest set based on the area, volume, absorbed gradient energy, and entropy of possible poses of the preliminary region of interest set of the pre-determined computed tomography scan image. The location information entropy determination module is used to determine the location information entropy of all final regions of interest in the final region of interest set based on the global constructed image of the final region of interest set; wherein, the global constructed image is obtained by scanning the final region of interest set through a first focal point; The final set update module is used to update the final region of interest set based on the location information entropy; The final set scanning module is used to scan and update the final set of regions of interest according to the second focus; wherein the first focus is greater than the second focus.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the computed tomography scanning method according to any one of claims 1 to 8.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the computed tomography scanning method according to any one of claims 1 to 8.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the computed tomography scanning method according to any one of claims 1 to 8.

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