Meniscus identification method, calculation processing unit and tomography system

Abstract

The invention provides a meniscus identification method, which comprises the following steps: S10, scanning a knee joint area by using first energy and second energy through dual-energy CT equipment, and generating plain scanning data of each voxel; s20, setting three basic substances of the dual-energy tomography three-substance separation mathematical model as water, compact collagenous fiber tissues and calcium; s30, processing the plain scanning data of each voxel by using the dual-energy tomography three-substance separation mathematical model to obtain enhanced data of each voxel; s40, calculating the water content percentage of each voxel according to the enhancement data of each voxel; and S50, generating an image for identifying the meniscus according to the water content percentage of each voxel. According to the meniscus identification method, the visualization effect and the detection accuracy of meniscus damage can be improved. The invention further provides a calculation processing unit and a tomography system which relate to the method.

Description

Technical Field

[0001] This invention relates to the field of medical imaging and medical image processing technology, and in particular to a method for identifying the meniscus, a computational processing unit, a tomographic scanning system, and a computer-readable storage medium. Background Technology

[0002] Meniscus injury is a common musculoskeletal disorder. Traditional diagnosis relies primarily on magnetic resonance imaging (MRI), but this method is time-consuming and expensive. There is an urgent clinical need for a computed tomography (CT) diagnostic solution to replace MRI. Current CT equipment and post-processing technologies cannot meet the demand for accurate diagnosis of meniscus injuries, lacking the ability to quantitatively analyze the tissue components within the meniscus (such as water and collagen fibers).

[0003] Dual-energy CT (DECT) refers to scanning an object using two different energies of X-rays without the use of contrast agents. Different tissues of the meniscus absorb X-rays differently, resulting in different attenuation coefficients μ. In recent years, dual-energy CT (DECT) technology has been widely used in medical imaging. By scanning tissues with two different X-ray beams, DECT can provide richer information about tissue composition than conventional CT. Utilizing this characteristic, dual-energy CT divides the meniscus along a selected plane into many cubic blocks called voxels; thus, the meniscus comprises multiple voxels. In dual-energy CT imaging, a three-substance separation mathematical model is typically used to process image data. This model achieves quantitative analysis of different tissue components by decomposing the tissue into three basic substances (such as fat, soft tissue, and water). However, this traditional three-substance separation model has limitations in identifying the meniscus. The main components of the meniscus include dense collagen fibers and water, but the traditional model does not consider dense collagen fibers as one of the basic substances. Therefore, the traditional three-substance separation model cannot accurately distinguish the meniscus from other soft tissues, resulting in poor meniscus identification.

[0004] In summary, traditional dual-energy CT imaging methods have significant shortcomings in meniscus identification. Summary of the Invention

[0005] The purpose of this invention is to provide a method, computing unit, system, and computer-readable storage medium for identifying meniscus injuries, which can improve the visualization and detection accuracy of meniscus injuries.

[0006] This invention provides a method for identifying the meniscus, comprising: scanning the knee joint region with a first energy and a second energy using a dual-energy CT scanner to generate plain scan data for each voxel; setting the three basic substances in the dual-energy tomography three-substance separation mathematical model as water, dense collagen fiber tissue, and calcium; processing the plain scan data of each voxel using the dual-energy tomography three-substance separation mathematical model to obtain enhanced data for each voxel; calculating the water content percentage of each voxel based on the enhanced data of each voxel; and generating an image for identifying the meniscus based on the water content percentage of each voxel.

[0007] The meniscus identification method provided by this invention modifies the existing dual-energy tomography three-substance separation mathematical model, setting the three basic substances as water, dense collagen fiber tissue, and calcium. The three-substance separation model is used to process the dual-energy plain scan data of the meniscus, normalize each voxel of the meniscus to the percentage of water content for quantification, and then generate image information based on the quantification results. This enables accurate display of the meniscus through CT equipment and post-processing technology, improving the visualization effect and detection accuracy of meniscus injury.

[0008] In another illustrative embodiment of the meniscus identification method, the dual-energy tomography (DCT) three-substance separation mathematical model includes the CT value of water, the CT value of dense collagen fiber tissue, and the slope of calcium. The CT value of water is used to characterize the X-ray attenuation characteristics of water at different energies. The CT value of dense collagen fiber tissue is used to characterize the X-ray attenuation characteristics of dense collagen fiber tissue at different energies. The slope of calcium is used to characterize the X-ray attenuation characteristics of calcium at different energies. This provides a more accurate basis for the diagnosis of meniscus injuries and lesions. The generated images can more clearly display the structure and water content distribution of the meniscus, helping doctors make more accurate diagnostic and treatment decisions.

[0009] In another illustrative embodiment of the meniscus identification method, the plain scan data of each voxel is processed using a dual-energy tomography (DCT) three-substance separation mathematical model to obtain enhanced data for each voxel. This includes generating an attenuation feature map and projecting each voxel point onto a scale based on a third matrix point to obtain enhanced data. The attenuation feature map includes voxel points corresponding to each voxel, a first matrix point corresponding to water, a second matrix point corresponding to dense collagen fiber tissue, a third matrix point corresponding to calcium, and a scale formed by the line connecting the first and second matrix points. By analyzing the attenuation feature map and the position of the projection points, the water content percentage of each voxel can be quantitatively calculated. The generated attenuation feature map and enhanced data can more clearly display the structure and water content distribution of the meniscus. The generated images have higher readability and diagnostic value.

[0010] In another illustrative embodiment of the meniscus identification method, enhanced data is obtained by projecting each voxel point onto a scale. Specifically, a ray is emitted from the third matrix point, passes through each voxel point, and intersects the scale to obtain enhanced data. By projecting all voxel points onto the scale, projection points for each voxel are obtained. These projection points constitute the enhanced data, which can more clearly reflect the compositional differences between different voxels. This increased difference value helps to more accurately identify the boundaries and structure of the meniscus.

[0011] In another illustrative embodiment of the meniscus identification method, the formula for calculating the percentage of water content is:

[0012] ;

[0013] in, This indicates the percentage of water content in each voxel. This represents the distance from the projection of each voxel point onto the scale to the first fundamental particle point. This represents the distance from the first elementary matter point to the second elementary matter point.

[0014] In another illustrative embodiment of the meniscus identification method, an image for identifying the meniscus is generated based on the water content percentage of each voxel, including: generating a grayscale image based on the water content percentage of each voxel; and overlaying an atomic number chromatogram on the grayscale image to generate an enhanced image. These steps improve the visual capture effect of the meniscus and aid in the diagnosis of meniscus injuries of grade II and above.

[0015] This invention also provides a computational processing unit configured to acquire plain scan data of each voxel, process the plain scan data of each voxel based on a dual-energy tomography three-substance separation mathematical model to obtain enhanced data of each voxel, calculate the water content percentage of each voxel based on the enhanced data of each voxel, and generate an image for identifying the meniscus based on the water content percentage of each voxel. The plain scan data of each voxel is generated by scanning the knee joint region with a dual-energy CT device using the first and second energy levels. The three basic substances in the dual-energy tomography three-substance separation mathematical model are water, dense collagen fiber tissue, and calcium.

[0016] In another illustrative embodiment of the computational processing unit, the computational processing unit is configured to generate an attenuation feature map, which includes voxel points corresponding to each voxel, a first matrix point corresponding to water, a second matrix point corresponding to dense collagen fiber tissue, and a third matrix point corresponding to calcium. The line connecting the first matrix point and the second matrix point forms a scale. The computational processing unit is also configured to project each voxel point onto the scale based on the third matrix point to obtain enhanced data.

[0017] In another illustrative embodiment of the calculation processing unit, the calculation processing unit calculates the water content percentage according to the following formula: ;

[0018] in, This indicates the percentage of water content in each voxel. This represents the distance from the projection of each voxel point onto the scale to the first fundamental particle point. This represents the distance from the first elementary matter point to the second elementary matter point.

[0019] In another illustrative embodiment of the computational processing unit, the unit is configured to generate a grayscale image based on the water content percentage of each voxel, and then overlay an atomic number chromatogram on the grayscale image to generate an enhanced image. This improves the visual capture of the meniscus and aids in the diagnosis of meniscus injuries of grade II and above.

[0020] The present invention also provides a computed tomography (CT) system, including a dual-energy CT scanner, the aforementioned computational processing unit, and a display device. The dual-energy CT scanner is configured to scan the knee joint region at a first energy and a second energy, generating plain scan data for each voxel. The computational processing unit is configured to acquire the plain scan data for each voxel. The display device is capable of displaying the generated image identifying the meniscus.

[0021] The present invention also provides a storage medium including a computer program, which, when executed by a processor, implements the above-described meniscus identification method. Attached Figure Description

[0022] The following figures are for illustrative purposes only and do not limit the scope of the invention.

[0023] Figure 1 This is a schematic flowchart illustrating one implementation method for identifying the meniscus.

[0024] Figure 2 This is a partial flowchart illustrating the meniscus identification method.

[0025] Figure 3 This is a schematic diagram of the attenuation feature map for the meniscus identification method.

[0026] Figure 4 This is a flowchart illustrating another part of the meniscus identification method.

[0027] Figure 5A and Figure 5B The illustration shows the generated image used to identify the meniscus.

[0028] Figure 6This is a schematic diagram illustrating one embodiment of a tomographic scanning system.

[0029] Label Explanation

[0030] 10 Computational Processing Units

[0031] 20 Dual-energy CT scanners

[0032] 30 display devices Detailed Implementation

[0033] To provide a clearer understanding of the technical features, objectives, and effects of the invention, specific embodiments of the invention are now described with reference to the accompanying drawings. In the drawings, the same reference numerals indicate components with the same or similar structures but the same function.

[0034] In this document, “illustrative” means “serving as an example, illustration or description”, and any illustration or implementation described herein as “illustrative” should not be construed as a more preferred or advantageous technical solution.

[0035] To keep the drawings simple, each drawing only schematically shows the parts related to the present invention, and they do not represent the actual structure of the product.

[0036] Figure 1 This is a schematic flowchart illustrating one embodiment of a method for identifying the meniscus. (Refer to...) Figure 1 The identification methods for meniscus include the following S10 to S50.

[0037] S10: The knee joint region is scanned using a dual-energy CT scanner with both first and second energy levels, generating plain scan data for each voxel. The X-ray tube of the dual-energy CT scanner emits X-rays. As the X-rays pass through the meniscus, each voxel along the ray direction absorbs a portion of the rays, causing X-ray attenuation. The rays after passing through the meniscus are received by a detector located opposite the X-ray tube. The first and second energies are different; one is set to high energy, and the other to low energy. In an illustrative embodiment, the first energy could be, for example, 140 kVp, and the second energy could be, for example, 80 kVp. By analyzing the X-ray energy received by the detector, the dual-energy CT scanner can determine the X-ray attenuation coefficient of each voxel, and thus obtain the CT value of each voxel.

[0038] S20: The three basic substances in the mathematical model of three-substance separation in dual-energy tomography are set as water, dense collagen fiber tissue, and calcium.

[0039] Dual-energy tomography (DCT) tri-substance separation mathematical model is an image processing technique that uses two different energies of X-rays simultaneously to achieve precise differentiation and quantitative analysis of three different components in tissues or materials. The three basic substances include a primary substance, a secondary substance, and a tertiary substance. Existing DCT tri-substance separation mathematical models use fat, soft tissue, and iodine as the three basic substances. However, the meniscus is primarily composed of water, dense collagen fibers, and calcium. Therefore, in S20, the CT value of fat in the DCT tri-substance separation mathematical model needs to be set to the CT value of water, the CT value of soft tissue needs to be set to the CT value of dense collagen fibers, and the slope of iodine needs to be set to the slope of calcium.

[0040] S30: The plain scan data of each voxel is processed using a three-substance separation mathematical model of dual-energy tomography to obtain the enhanced data of each voxel. Figure 2 This is a partial flowchart illustrating the meniscus identification method. (Refer to...) Figure 2 S30 specifically includes the following S31 and S32.

[0041] S31: Generate an attenuation feature map. The attenuation feature map includes voxel points corresponding to each voxel, a first matrix point corresponding to water, a second matrix point corresponding to dense collagen fiber tissue, and a third matrix point corresponding to calcium. The line connecting the first and second matrix points forms a scale. For each voxel, its position on the attenuation feature map, i.e., the voxel point, is calculated based on its CT values ​​at both energies. The first matrix point corresponding to the CT value of water is represented as a fixed point on the attenuation feature map. The second matrix point corresponding to the CT value of dense collagen fiber tissue is represented as another fixed point on the attenuation feature map. The third matrix point corresponding to the CT value of calcium is represented as a third fixed point on the attenuation feature map. Connecting the first and second matrix points forms a scale. This scale is used to quantify the relative positions of the voxel points between water and dense collagen fiber tissue, thereby reflecting the compositional characteristics of the voxels.

[0042] Figure 3 This is a schematic diagram of the attenuation characteristic map of the meniscus identification method. (Refer to...) Figure 3 The horizontal axis of the attenuation feature map represents the CT value at the first energy, and the vertical axis represents the CT value at the second energy. In the attenuation feature map, each voxel corresponds to a voxel point. In the illustrative embodiment, taking voxel A as an example, the coordinates of the voxel point corresponding to voxel A are (xa, ya), where xa is the CT value of voxel A at the first energy, and ya is the CT value of voxel A at the second energy. The CT values ​​of water, dense collagen fiber tissue, and calcium at the first and second energies can be measured in advance, thereby obtaining the first matrix point M1, the second matrix point M2, and the third matrix point M3 corresponding to water, dense collagen fiber tissue, and calcium, respectively.

[0043] S32: Based on the third matrix point, project each voxel point onto the scale to obtain enhanced data.

[0044] Reference Figure 3 Taking voxel A as an example, the projection process is equivalent to emitting a ray from the third matrix point M3. After passing through voxel point A, the ray intersects with the scale, resulting in a projection point A'. By projecting each voxel point onto the scale, enhanced data is obtained, increasing the difference between different voxels and eliminating the influence of calcium within the meniscus.

[0045] S40: Calculate the water content percentage of each voxel based on the enhancement data. For details, refer to... Figure 3 The formula for calculating the percentage of water content is:

[0046] ;

[0047] in, This indicates the percentage of water content in each voxel. This represents the distance from the projection of each voxel point onto the scale to the first fundamental particle point. This represents the distance from the first matrix point to the second matrix point. The water content percentage of each voxel is calculated using a normalized algorithm to quantify the voxels of the meniscus.

[0048] The normalization algorithm described above can unify the water content percentage of each voxel within a range of 0% to 100%. This normalization process not only allows for direct comparison of water content between different voxels but also facilitates subsequent image generation and analysis.

[0049] S50: Generates an image for identifying the meniscus based on the percentage of water content in each voxel.

[0050] The meniscus identification method provided by this invention modifies the existing dual-energy tomography three-substance separation mathematical model, setting the three basic substances as water, dense collagen fiber tissue, and calcium. The three-substance separation model is used to process the dual-energy plain scan data of the meniscus, normalize each voxel of the meniscus to the percentage of water content for quantification, and then generate image information based on the quantification results. This enables accurate display of the meniscus through CT equipment and post-processing technology, improving the visualization effect and detection accuracy of meniscus injury.

[0051] Figure 4 This is a flowchart illustrating another part of the meniscus identification method. Figure 5A and 5B The schematic diagram illustrates the generated image used to identify the meniscus. (See reference...) Figure 4 , Figure 5A and Figure 5BIn the illustrative embodiment, S50 specifically includes the following S51 and S52:

[0052] S51: Generate a grayscale image based on the water content percentage of each voxel. Specifically, after determining the water content percentage of each voxel, for each voxel, determine the corresponding grayscale value based on the water content percentage and the preset maximum grayscale value. Based on the grayscale values ​​corresponding to each voxel, generate a grayscale image to identify the meniscus region. Each voxel corresponds to one pixel in the grayscale image.

[0053] S52: Overlay an atomic number spectrum onto the grayscale image to generate an enhanced image. Specifically, grayscale values ​​are mapped to the color levels of the atomic number spectrum based on the percentage of water content in each voxel. The atomic number spectrum offers a rich range of colors with strong contrast between warm and cool tones, maximizing visual capture of the meniscus and aiding in the diagnosis of meniscus injuries of grade II and above.

[0054] This invention also provides a computational processing unit for implementing the aforementioned meniscus identification method. The computational processing unit includes a computational processing unit 10, configured to acquire plain scan data of each voxel, process the plain scan data of each voxel based on a dual-energy tomography (DCT) three-substance separation mathematical model to obtain enhanced data of each voxel, calculate the water content percentage of each voxel based on the enhanced data, and generate an image for meniscus identification based on the water content percentage of each voxel. The plain scan data of each voxel is generated by scanning the knee joint region with a dual-energy CT scanner using the first and second energy levels. The three basic substances in the dual-energy tomography three-substance separation mathematical model are water, dense collagen fiber tissue, and calcium.

[0055] In an illustrative embodiment, the computational processing unit 10 is configured to generate an attenuation feature map. The attenuation feature map includes voxel points corresponding to each voxel, a first matrix point corresponding to water, a second matrix point corresponding to dense collagen fiber tissue, and a third matrix point corresponding to calcium, and a scale formed by connecting the first and second matrix points. The computational processing unit 10 is further configured to project each voxel point onto the scale based on the third matrix point to obtain enhanced data.

[0056] In the illustrative embodiment, refer to Figure 3 The calculation and processing unit 10 calculates the water content percentage according to the following formula:

[0057] ;

[0058] in, This indicates the percentage of water content in each voxel. This represents the distance from the projection of each voxel point onto the scale to the first fundamental particle point. This represents the distance from the first matrix point to the second matrix point. The water content percentage of each voxel is calculated using a normalized algorithm to quantify the voxels of the meniscus.

[0059] In an illustrative embodiment, the computational processing unit 10 is configured to generate a grayscale image based on the water content percentage of each voxel, and then overlay an atomic number chromatogram on the grayscale image to generate an enhanced image. This maximizes the visual capture effect of the meniscus and aids in the diagnosis of meniscus injuries of grade II and above.

[0060] The present invention also provides a tomographic scanning system. Figure 6 This is a schematic diagram illustrating one embodiment of a tomographic scanning system. (Refer to...) Figure 6 The computed tomography (CT) system includes a dual-energy CT scanner 20, the aforementioned computational processing unit 10, and a display device 30. The dual-energy CT scanner 20 is configured to scan the knee joint region at a first energy level and a second energy level, generating plain scan data for each voxel. The computational processing unit 10 is configured to acquire the plain scan data for each voxel and generate an image for identifying the meniscus. The display device 30 can display the image generated by the computational processing unit 10.

[0061] The present invention also provides a storage medium including a computer program, which, when executed by a processor, implements the above-described meniscus identification method.

[0062] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0063] The detailed descriptions listed above are merely specific descriptions of feasible embodiments of the present invention and are not intended to limit the scope of protection of the present invention. All equivalent implementation schemes or modifications made without departing from the spirit of the present invention, such as combinations, divisions or repetitions of features, should be included within the scope of protection of the present invention.

Claims

1. A method for identifying the meniscus, characterized in that, include: The knee joint area is scanned using a dual-energy CT scanner with the first and second energy levels to generate plain scan data for each voxel. The three basic substances in the mathematical model of three-substance separation by dual-energy tomography were set as water, dense collagen fiber tissue, and calcium. The plain scan data of each voxel are processed using the dual-energy tomography three-substance separation mathematical model to obtain the enhanced data of each voxel. Calculate the water content percentage of each voxel based on the enhancement data described for each voxel; and An image for identifying the meniscus is generated based on the stated water content percentage of each voxel.

2. The method for identifying the meniscus as described in claim 1, characterized in that, The dual-energy tomography three-substance separation mathematical model includes the CT value of water, the CT value of dense collagen fiber tissue, and the slope of calcium.

3. The method for identifying the meniscus as described in claim 1, characterized in that, The plain scan data of each voxel are processed using the aforementioned dual-energy tomography three-substance separation mathematical model to obtain the enhanced data of each voxel, including: A decay feature map is generated, comprising voxel points corresponding to each voxel, a first matrix point corresponding to water, a second matrix point corresponding to dense collagen fiber tissue, and a third matrix point corresponding to calcium, and a scale formed by the lines connecting the first and second matrix points; and The enhanced data is obtained by projecting each voxel point onto the scale based on the third matrix point.

4. The meniscus identification method as described in claim 3, characterized in that, The enhanced data is obtained by projecting each voxel point onto the scale. Specifically, a ray is emitted from the third matrix point, and the ray intersects the scale after passing through each voxel point to obtain the enhanced data.

5. The method for identifying the meniscus as described in claim 3, characterized in that, The formula for calculating the percentage of water content is: ; in, This indicates the percentage of water content in each voxel. This represents the distance from the projection of each voxel point onto the scale to the first matrix point. This represents the distance from the first matrix point to the second matrix point.

6. The method for identifying the meniscus as described in claim 1, characterized in that, An image for identifying the meniscus is generated based on the stated water content percentage of each voxel, including: A grayscale image is generated based on the stated water content percentage of each voxel; and An atomic number chromatogram is overlaid on the grayscale image to generate an enhanced image.

7. A computing processing unit, characterized in that, The computational processing unit is configured to acquire plain scan data of each voxel, process the plain scan data of each voxel based on a dual-energy tomography three-substance separation mathematical model to obtain enhanced data of each voxel, calculate the water content percentage of each voxel based on the enhanced data of each voxel, and generate an image for identifying the meniscus based on the water content percentage of each voxel; wherein, the plain scan data of each voxel is generated by scanning the knee joint area with a dual-energy CT device using a first energy and a second energy, and the three basic substances of the dual-energy tomography three-substance separation mathematical model are water, dense collagen fiber tissue, and calcium.

8. The computing processing unit as described in claim 7, characterized in that, The computational processing unit is configured to generate an attenuation feature map, which includes voxel points corresponding to each voxel, a first matrix point corresponding to water, a second matrix point corresponding to dense collagen fiber tissue, and a third matrix point corresponding to calcium. The line connecting the first matrix point and the second matrix point forms a scale. The computational processing unit is further configured to project each voxel point onto the scale based on the third matrix point to obtain the enhancement data.

9. The computing processing unit as described in claim 8, characterized in that, The calculation unit calculates the water content percentage according to the following formula: ; in, This indicates the percentage of water content in each voxel. This represents the distance from the projection of each voxel point onto the scale to the first matrix point. This represents the distance from the first matrix point to the second matrix point.

10. The computing processing unit as described in claim 7, characterized in that, The computational processing unit is configured to generate a grayscale image based on the water content percentage of each voxel, and to overlay an atomic number chromatogram on the grayscale image to generate an enhanced image.

11. A tomographic scanning system, characterized in that, include: A dual-energy CT device (20) is configured to scan the knee joint region with a first energy and a second energy to generate plain scan data of each voxel; The computing processing unit (10) as described in any one of claims 7 to 10; The display device (30) is configured to display the generated image of the identified meniscus.

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