Medical imaging method, device, electronic device and storage medium

Abstract

The embodiment of the present invention discloses a medical imaging method, device, electronic device and storage medium. The method includes: obtaining an original scanned image of a target object, the original scanned image is obtained by reconstructing original data in a data domain, and the original data is obtained by performing a medical scan on a target object containing metal; according to the original scanned image, obtaining a first set of data corresponding to the metal in the data domain and a second set of data corresponding to the metal removed from the original scanned image in the data domain; performing weighted processing and reconstruction on the first set of data, the second set of data and the second set of data processed by interpolation to obtain a first corrected image; determining the boundary information of the metal in the image domain in the original scanned image according to the first corrected image, and correcting the original image according to the boundary information of the metal in the image domain to obtain a second corrected image; determining a medical image according to the original scanned image and the second corrected image. The method of the present invention can remove metal artifacts in an image, which is convenient for doctors to diagnose.

Description

Technical Field

[0001] The embodiments of the present invention relate to image processing technology, and in particular to a medical imaging method, device, electronic device and storage medium. Background Art

[0002] Due to the high density of metal materials, when CT scanning rays pass through, various metal artifacts including noise, ray hardening and scattering will be generated, which appear as strip artifacts, as well as bright or dark band artifacts on the image.

[0003] In the prior art, metal implant materials and structures are usually selected before metal implant surgery, or metal is removed as much as possible during scanning to reduce the impact of metal implants on images.

[0004] However, this method can only avoid artifacts before scanning. However, due to surgeries such as joint replacements and heart bypass surgery, it is impossible to completely prevent metal from entering the scanning field of view. This will cause the tissue information in the image to be missing and difficult to identify, affecting the doctor's diagnosis. Summary of the invention

[0005] The embodiments of the present invention provide a medical imaging method, device, electronic device and storage medium to achieve high-quality removal of metal artifacts in images and obtain high-quality images that are convenient for doctors to diagnose.

[0006] In a first aspect, an embodiment of the present invention provides a medical imaging method, the method comprising:

[0007] Acquire an original scanned image of a target object, wherein the original scanned image is obtained by reconstructing original data in a data domain, wherein the original data is obtained by performing a medical scan on the target object, wherein a part of the target object contains metal;

[0008] According to the original scanned image, a first set of data corresponding to the metal in the data domain is obtained, and a second set of data corresponding to the metal in the data domain is removed from the original scanned image;

[0009] Combining the first set of data, the second set of data, and the interpolated second set of data to perform weighted processing and reconstruction to obtain a first corrected image;

[0010] Determine the boundary information of the metal in the image domain in the original scanned image according to the first corrected image, and correct the original image according to the boundary information of the metal in the image domain to obtain a second corrected image;

[0011] A medical image of the target object is determined based on the original scanned image and the second corrected image.

[0012] In a second aspect, an embodiment of the present invention further provides a medical imaging device, the device comprising:

[0013] An original scan image acquisition module, used to acquire an original scan image of a target object, wherein the original scan image is obtained by reconstructing original data in a data domain, wherein the original data is obtained by performing a medical scan on the target object, wherein a part of the target object contains metal;

[0014] A data acquisition module, used for acquiring, according to the original scanned image, a first set of data corresponding to the metal in the data domain, and a second set of data corresponding to the metal in the data domain after the original scanned image is removed;

[0015] A first corrected image acquisition module, used for performing weighted processing and reconstruction on the first set of data, the second set of data and the interpolated second set of data to acquire a first corrected image;

[0016] A second corrected image acquisition module, used to determine the boundary information of the metal in the image domain in the original scanned image according to the first corrected image, and correct the original image according to the boundary information of the metal in the image domain to acquire a second corrected image;

[0017] The medical image acquisition module is used to determine the medical image of the target object according to the original scanned image and the second corrected image.

[0018] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0019] one or more processors;

[0020] A storage device for storing one or more programs;

[0021] When the one or more programs are executed by the one or more processors, the one or more processors implement any medical imaging method described in the embodiments of the present invention.

[0022] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute any medical imaging method described in the embodiments of the present invention.

[0023] The technical solution of the embodiment of the present invention is to obtain an original scanned image of the target object, obtain a first set of data corresponding to the metal in the data domain and a second set of data corresponding to the metal removed from the original scanned image based on the original scanned image, perform weighted processing and reconstruction on the first set of data, the second set of data and the second set of data processed by interpolation, obtain a first corrected image, determine the boundary information of the metal in the image domain based on the first corrected image in the original scanned image, correct the original image based on the boundary information of the metal in the image domain, obtain a second corrected image, and determine the medical image of the target object based on the original scanned image and the second corrected image. In this way, metal artifacts can be removed by combining the image domain and the data domain, thereby achieving high-quality removal of metal artifacts in the image and obtaining a high-quality image that is convenient for doctors to diagnose. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of a medical imaging method in Embodiment 1 of the present invention;

[0025] Figure 2 is a schematic diagram of a medical image in Embodiment 1 of the present invention;

[0026] Figure 3 is a flow chart of a medical imaging method according to a second embodiment of the present invention;

[0027] Figure 4 is a schematic diagram of a target medical image in Embodiment 2 of the present invention;

[0028] Figure 5 is a schematic structural diagram of a medical imaging device in Embodiment 3 of the present invention;

[0029] Figure 6 It is a structural schematic diagram of an electronic device in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0031] Embodiment 1

[0032] Figure 1 This is a flowchart of a medical imaging method provided in Embodiment 1 of the present invention. This embodiment is applicable to the case of effectively removing metal artifacts in medical images. The method can be performed by a medical imaging device, which can be implemented by software and / or hardware. The medical imaging device can be configured on a computing device, and specifically includes the following steps:

[0033] S110, obtaining an original scanned image of the target object, the original scanned image being obtained by reconstructing original data in a data domain, the original data being obtained by performing a medical scan on the target object, and a part of the target object contains metal.

[0034] Exemplarily, the target object here may be an object whose metal artifacts need to be removed in the original scanned image, for example, a person or an animal.

[0035] It should be noted that the target object includes metal. Taking the target object as a human as an example, the metal here may be, for example, an implant material implanted in the target object, such as a heart stent or a steel plate implanted in the target object.

[0036] The original scanned image may be obtained by reconstructing the original data in the data domain, where the original data is obtained by performing a medical scan on the target object.

[0037] The original data here may be data of a medical scan of a target object. For example, the original data may be CT data of multiple layers obtained by performing CT scanning on the target object, and the CT data may be obtained by one or more combinations of multiple reconstruction methods such as multi-layer reconstruction, maximum density projection, volume roaming, curved surface reconstruction, etc. to obtain the original scan image.

[0038] S120, acquiring, according to the original scanned image, a first set of data corresponding to metal in the data domain, and a second set of data corresponding to the original scanned image without metal in the data domain.

[0039] Exemplarily, the first set of data may be metal data corresponding to the metal in the original scanned image in the data domain.

[0040] The second set of data may be data in the data domain of the remaining image after metal is removed from the original scanned image.

[0041] The original scanned image is forward projected, for example, by using a filter convolution calculation method, to obtain a first set of data corresponding to the metal in the data domain, and a second set of data corresponding to the original scanned image after the metal is removed from the data domain.

[0042] It should be noted that the forward projection calculation here belongs to the existing technology and will not be described in detail here.

[0043] S130 , performing weighted processing and reconstruction on the first set of data, the second set of data, and the interpolated second set of data to obtain a first corrected image.

[0044] Exemplarily, the first corrected image may be an image obtained by weighting and reconstructing the first set of data, the second set of data, and the second set of data after interpolation processing.

[0045] Optionally, the first group of data, the second group of data and the interpolated second group of data are weighted and reconstructed to obtain a first corrected image. Specifically, the method may include: determining the weight of the first group of data based on the first group of data and the original data; weighting the first group of data, the weight of the first group of data and the interpolated second group of data to obtain a first weighted result; weighting the first weighted result, the second group of data and the weight of the first weighted result to obtain a second weighted result; and filtering and back-projecting the second weighted result to obtain the first corrected image.

[0046] Exemplarily, based on the first set of data and the original data, the weight of the first set of data can be obtained according to the following formula (1):

[0047]

[0048] Wherein, weight is the weight of the first set of data, 1W0<weight<1W0, W0 is the coefficient of the first set of data determined based on the original data, W0 is a constant value, Pmetal is each data unit in the first set of data, and in this embodiment, the value of Pmetal is the CT value of the projection data corresponding to each pixel of the metal image; max is the maximum value operation, and max(Pmetal) is the maximum value of the data unit in the first set of data; (1-W0)) means taking the maximum of the two.

[0049] The weight of the first set of data and the interpolated second set of data are weighted, specifically, according to the following formula (2), to obtain a first weighted result:

[0050] Pcorr1=Pmetal*weight+(1-weight)*Pdoff (2)

[0051] Among them, Pcorr1 is the data corresponding to the first weighted result, weight is the weight of the first group of data, and Pdoff is each data unit in the second group of data subjected to interpolation processing.

[0052] The data corresponding to the first weighted result, the second set of data and the weight of the first weighted result are weighted according to the following formula (3) to obtain a second weighted result:

[0053] Pcorr2=Pcorr1*weight1+(1-weight1)*Pdiff (3)

[0054] Among them, Pcorr2 is the data corresponding to the second weighted result, Pcorr1 is the data corresponding to the first weighted result, weight1 is the weight of the data corresponding to the first weighted result, and Pdiff is each data unit in the second set of data.

[0055] It should be noted that the method for calculating the weight of the first weighted result here is the same as the method for calculating the weight of the first data described above, the only difference being that Pmetal in formula (1) is replaced by Pcorr1. No further description will be given here.

[0056] After obtaining the second weighted result, filtering back-projection is performed on the second weighted result to obtain the first corrected image. It should be noted that the filtering back-projection here belongs to the prior art and will not be described in detail here.

[0057] S140, determining the boundary information of the metal in the image domain in the original scanned image according to the first corrected image, and correcting the original image according to the boundary information of the metal in the image domain to obtain a second corrected image.

[0058] Exemplarily, the second corrected image may be an image obtained by correcting the original image based on the boundary information of the metal in the image domain determined by the first corrected image in the original scanned image, using the boundary information of the metal in the image domain.

[0059] Optionally, the original image is corrected according to the boundary information of the metal in the image domain to obtain a second corrected image, which may specifically be: determining the boundary data of the metal in the data domain according to the boundary information of the metal in the image domain; determining the weight of the boundary data of the metal in the data domain based on the boundary data of the metal in the data domain and the original data; weighting the boundary data of the metal in the data domain, the weight of the boundary data of the metal in the data domain, and the boundary data of the metal in the data domain after interpolation to obtain a third weighted result; weighting the third weighted result, the boundary data of the metal in the data domain, and the weight of the third weighted result to obtain a fourth weighted result; and filtering and back-projecting the fourth weighted result to obtain the second corrected image.

[0060] Exemplarily, according to the boundary information of the metal in the image domain, the boundary data of the metal in the data domain can be obtained by forward projection calculation.

[0061] According to the boundary data and original data of metal in the data domain, the weight of the boundary data of metal in the data domain is determined using the following formula (4):

[0062]

[0063] Among them, weight2 is the weight of the boundary data of the metal in the data domain, W1 is the coefficient of the boundary data of the metal in the data domain determined based on the original data, W1 is a constant value, P is the boundary data of the metal in the data domain, the boundary data of the metal in the data domain may contain multiple values ​​and the corresponding boundary data contains multiple values, max(P) is the maximum value of the boundary data of the metal in the data domain; (1-W1)) means taking the maximum of the two.

[0064] The boundary data of the metal in the data domain, the weight of the boundary data of the metal in the data domain, and the boundary data of the metal in the data domain after interpolation processing are weighted according to the following formula (5) to obtain a third weighted result:

[0065] Pcorr3=P*weight2+(1-weight)*P1 (5)

[0066] Among them, Pcorr3 is the data corresponding to the third weighted result, P is the boundary data of the metal in the data domain, weight2 is the weight of the boundary data of the metal in the data domain, and P1 is the boundary data of the metal in the data domain after interpolation processing. The interpolation processing of the boundary data of the metal in the data domain is specifically linear interpolation.

[0067] The third weighted result, the boundary data of the metal in the data domain and the weight of the third weighted result are weighted according to the following formula (6) to obtain a fourth weighted result:

[0068] Pcorr4=Pcorr3*weight3+(1-weight3)*P (6)

[0069] Among them, Pcorr4 is the data corresponding to the fourth weighted result, Pcorr3 is the data corresponding to the third weighted result, weight3 is the weight of the data corresponding to the third weighted result, and P is the boundary data of the metal in the data domain.

[0070] It should be noted that the method for calculating the weight of the third weighted result here is the same as the method for calculating the weight of the first data described above, which will not be described in detail here.

[0071] It should be noted that the calculation method for weighted processing here can be uniformly expressed by the same formula: a*Coeff+b*(1-Coeff), where Coeff is the weight of a.

[0072] After the above calculation, the metal artifacts in the original scanned image can be removed, and the following is obtained: Figure 2The calculation of the first weighted result, the second weighted result, the third weighted result and the fourth weighted result adopts multi-core GPU pixel-by-pixel and data point-by-data point parallel calculation, which can significantly improve the efficiency of data processing and facilitate the interactive calculation of the data domain and the image domain involved in the metal artifact process.

[0073] S150: Determine a medical image of the target object according to the original scanned image and the second corrected image.

[0074] Exemplarily, after obtaining the second corrected image, the original scanned image and the second corrected image are subjected to Gaussian filtering and weighted calculation to obtain a medical image of the target object.

[0075] The second set of data processed by interpolation can be obtained by linear interpolation of the second set of data, frequency domain interpolation, etc. In this embodiment, the second set of data processed by interpolation is obtained by the following method:

[0076] First, I x,y represents the image after the metal is removed from the original scanned image, P x,y For I x,y The frequency domain data obtained by two-dimensional Fourier transform, and P x,y is the data in the rectangular coordinate system; let P x,y The polar coordinate frequency domain expression of P' x,y :

[0077] Secondly, the frequency domain interval between two adjacent data points in the polar coordinate frequency domain is set, that is, the number of interpolation points is determined. In this embodiment, the frequency domain interval of the rectangular coordinate is:

[0078]

[0079] The frequency domain interval of polar coordinates is:

[0080]

[0081] Among them, F max represents the maximum field of view of CT data reconstruction, d is the physical spacing of equally spaced parallel beam projections, and N FFT is the length of the convolution in the filtered back-projection reconstruction, and the value P' for each point (p, n) in polar coordinates x,y The values ​​are interpolated linearly.

[0082] The projection angle for each interpolation point is:

[0083]

[0084] Where p represents the frequency of the current interpolation point, p = 0, 1, ..., N pπ -1, is the starting angle of the first projection, Np represents the angle according to Δω p The determined matrix size, n represents the frequency domain index, n = 0, 1, ..., N FFT-1 .

[0085] Through the above interpolation process, for each projection angle p, a sequence value of multiple interpolation points is obtained;

[0086] Secondly, deconvolution operation and inverse Fourier transform calculation are performed on the obtained multiple interpolated sequence values ​​to obtain an image of the second set of interpolated data in the image domain. It should be noted that the Gaussian filtering and weighted calculation here belong to the existing technology and will not be introduced in detail here.

[0087] In this way, metal artifacts can be removed by combining the above-mentioned image domain and data domain, thereby achieving high-quality removal of metal artifacts in images and obtaining high-quality images that are convenient for doctors to diagnose.

[0088] The technical solution of the embodiment of the present invention is to obtain an original scanned image of the target object, obtain a first set of data corresponding to the metal in the data domain according to the original scanned image, and obtain a second set of data corresponding to the metal in the data domain after the original scanned image is removed, and perform weighted processing and reconstruction on the first set of data, the second set of data and the second set of data processed by interpolation to obtain a first corrected image, determine the boundary information of the metal in the image domain according to the first corrected image in the original scanned image, and correct the original image according to the boundary information of the metal in the image domain to obtain a second corrected image, and determine the medical image of the target object based on the original scanned image and the second corrected image, so that the metal artifacts can be removed by combining the image domain and the data domain, and the metal artifacts in the image can be removed with high quality, so as to obtain a high-quality image that is convenient for doctors to diagnose; in the early stage of metal artifact removal, the first set of data corresponding to the metal in the data domain is extracted, and the first corrected image is obtained according to the first set of data, which can effectively reduce the interference of other non-metal artifacts such as motion artifacts and system artifacts on the final image.

[0089] Embodiment 2

[0090] Figure 3 The flowchart of the medical imaging method provided for the second embodiment of the present invention, the embodiment of the present invention can be combined with each optional scheme in the above embodiment. In the embodiment of the present invention, optionally, the method further includes: performing weighted merging calculation on the original scanned image and the second corrected image to obtain a third corrected image; performing Gaussian filtering and weighted calculation on the third corrected image and the original scanned image to obtain a target medical image of the target object.

[0091] like Figure 3 As shown, the method of the embodiment of the present invention specifically includes the following steps:

[0092] S210, obtaining an original scanned image of the target object, wherein the original scanned image is obtained by reconstructing original data in a data domain, wherein the original data is obtained by performing a medical scan on the target object, and a part of the target object contains metal.

[0093] S220, acquiring, according to the original scanned image, a first set of data corresponding to metal in the data domain, and a second set of data corresponding to the original scanned image without metal in the data domain.

[0094] S230 , performing weighted processing and reconstruction on the first set of data, the second set of data, and the interpolated second set of data to obtain a first corrected image.

[0095] S240: Determine the boundary information of the metal in the image domain in the original scanned image according to the first corrected image, and correct the original image according to the boundary information of the metal in the image domain to obtain a second corrected image.

[0096] S250 , performing weighted merging calculation on the original scanned image and the second corrected image to obtain a third corrected image.

[0097] Exemplarily, the third corrected image may be an image obtained by weighted merging calculation of the original scanned image and the second corrected image.

[0098] Optionally, the weighted merging calculation of the original scanned image and the second corrected image to obtain the third corrected image may specifically be: sampling and segmenting calculation are performed on each pixel point in the second corrected image, and a first target pixel point of a preset pixel value is selected from each pixel point in the segmented second corrected image; sampling and segmenting calculation are performed on each pixel point in the original scanned image, and based on the first target pixel point, a second target pixel point is selected from each pixel point in the segmented original scanned image; based on a preset step value, a first reference image corresponding to each first target pixel point and a second reference image corresponding to each second target pixel point, a weighted result of the first target pixel point and the second target pixel point is obtained; based on the weighted result, a statistical result corresponding to the weighted result is determined; based on the statistical result, a third corrected image is determined.

[0099] Exemplarily, the preset pixel value may be a preset pixel value that needs to be selected. For example, the preset pixel value here may be 255.

[0100] The first target pixel point may be a pixel point with a preset pixel value selected from the pixel points in the second corrected image after sampling and segmentation.

[0101] The second target pixel point may be a pixel point corresponding to the first target pixel point and having a preset pixel value selected from the pixel points in the original scanned image after sampling and segmentation.

[0102] Sampling and segmenting are performed on each pixel point in the second corrected image. Specifically, downsampling and segmentation calculation may be performed, and a first target pixel point with a pixel value of 255 is selected from each pixel point in the segmented second corrected image.

[0103] The same sampling segmentation calculation method as that for each pixel in the second corrected image is used to perform sampling segmentation calculation on each pixel in the original scanned image. A second target pixel with a pixel value of 255 and the same position as the first target pixel is selected from the original scanned image. For example, a first target pixel is selected in the second corrected image, and in the original scanned image, a pixel with the same position as the first target pixel in the second corrected image is selected as the second target pixel.

[0104] The preset step value may be a preset step, for example, the preset step value here may be 0.01.

[0105] The first reference image may be an image of a preset size selected from the second corrected image based on a preset step value and a first target pixel point.

[0106] The second reference image may be an image of a preset size selected from the original scanned image based on a preset step value and a second target pixel point.

[0107] It should be noted that the size of the first reference image corresponding to each first target pixel point is equal to the size of the second reference image corresponding to the second target pixel point corresponding to the first target pixel point.

[0108] Optionally, the weighted result of the first target pixel and the second target pixel is obtained based on the preset step value, the first reference image corresponding to each first target pixel, and the second reference image corresponding to each second target pixel. Specifically, it can be: for any first target pixel, and the second target pixel corresponding to the first target pixel, perform the following steps to determine the weighted result of the first target pixel and the second target pixel: determine the current first reference image corresponding to the current first target pixel based on the preset step value and the current first target pixel; determine the current second reference image corresponding to the current second target pixel based on the preset step data and the current second target pixel corresponding to the current first target pixel; determine the weighted result based on the current first reference image and the current second reference image.

[0109] Exemplarily, based on the preset step value and the first target pixel point, an image of a preset size is selected from the second corrected image as the first reference image. The preset size here can be set according to the computing performance and is not limited here.

[0110] An image of a preset size is selected from the original scanned image based on the preset step data and the current second target pixel corresponding to the current first target pixel. The preset size here must be equal to the size of the first reference image.

[0111] Taking the calculation of the weighted result of any first reference image as an example, the preset step value is used as the weight coefficient of each first target pixel point, and each weight coefficient is multiplied by its corresponding first reference image to obtain the weighted result corresponding to each first reference image.

[0112] The calculation of the weighted result of any second reference image is the same as the calculation of the weighted result of the first reference image, which will not be repeated here.

[0113] After obtaining the weighted result of any first reference image and the weighted result of any second reference image, the corresponding weighted result of the first reference image and the weighted result of the second reference image are combined and calculated to obtain a combined weighted result of the first reference image and the second reference image.

[0114] Specifically, taking the preset step value of 0.01 in the range of 0-1 as an example, there will be 101 weight coefficients. For a first reference image, there will be a second reference image corresponding to the first reference image, because the first reference image is obtained by the first target pixel point, the second reference image is obtained by the second target pixel point, and each first target pixel point corresponds to a second target pixel point. Therefore, each first reference image here will also correspond to a second reference image.

[0115] For a certain first reference image, by multiplying the first reference image by 101 weight coefficients respectively, 101 weighted results of the first reference image will be obtained.

[0116] Similarly, the weighted calculation of the first reference image is performed on each second reference image, and 101 weighted results are obtained for each second reference image.

[0117] The 101 weighted results obtained from the first reference image and the 101 weighted results obtained from the second reference image corresponding to the first reference image are combined and calculated to obtain the weighted results corresponding to the first target pixel points corresponding to the first reference image. The 101 weighted results obtained from each first reference image and the 101 weighted results obtained from each second reference image corresponding to each first reference image are combined and calculated to obtain the 101 weighted results corresponding to each first target pixel points.

[0118] Based on the obtained weighted results corresponding to the first target pixel points, statistical results corresponding to the weighted results are determined.

[0119] Optionally, based on the weighted result corresponding to any first target pixel point, a statistical result corresponding to the weighted result is determined. Specifically, for any first target pixel point, and the second target pixel point corresponding to the first target pixel point, the following steps are performed to determine the statistical result: based on the current weighted result, a histogram of the current weighted result and a logarithm of the histogram of the current weighted result are determined; and weighted calculations are performed on the histogram of the current weighted result and the logarithm of the histogram of the current weighted result to obtain the statistical result.

[0120] Exemplarily, continuing the above example, according to the 101 weighted results corresponding to the current first target pixel, the histogram of the current 101 weighted results and the logarithm of the histogram of the current 101 weighted results are calculated. The specific calculation of the histogram of the current 101 weighted results and the logarithm of the histogram of the current 101 weighted results belong to the prior art and will not be described in detail here.

[0121] After obtaining the histogram of the current 101 weighted results and the logarithm of the histogram of the current 101 weighted results, the histogram of the current 101 weighted results and the logarithm of the histogram of the current 101 weighted results are correspondingly multiplied to obtain a statistical result. Of course, the statistical results here are also 101.

[0122] According to the statistical results, a third corrected image can be obtained, which can be specifically: based on each statistical result, determining the entropy of the histogram of each current weighted result, and the image weight corresponding to the entropy of the histogram of each current weighted result; based on each image weight and a preset step value, determining the third corrected image.

[0123] Exemplarily, based on the 101 statistical results, the entropy of the histogram of the current 101 weighted results and the image weight corresponding to the entropy of the histogram of each current weighted result can be determined.

[0124] Here, the entropy of the histogram of the current 101 weighted results may be determined by taking the histogram result with the largest histogram result among the histograms of the current 101 weighted results as the entropy of the histogram of the weighted result corresponding to the current first target pixel.

[0125] Here, the image weight corresponding to the entropy of the histogram of each current weighted result can be determined by taking the position of the histogram with the largest determined histogram result as the image weight corresponding to the entropy of the histogram of the current weighted result. For example, among the histograms of 101 weighted results, the result of the histogram of the 5th weighted result is the largest, and the image weight here is 5.

[0126] After the image weight of each pixel is determined, the third corrected image can be determined according to the image weight and the preset step value. Specifically, the image weight and the preset step value can be multiplied to obtain the third corrected image. In this embodiment, the entropy of the histogram of multiple weighted results is calculated by multi-core GPU parallel processing to improve the removal speed of metal artifacts.

[0127] S260 , performing Gaussian filtering and weighted calculation on the third corrected image and the original scanned image to obtain a target medical image of the target object.

[0128] Exemplarily, the target medical image may be an image obtained after detail enhancement processing is performed on the original image and the third corrected image.

[0129] After obtaining the third corrected image, the original scanned image and the third corrected image are subjected to Gaussian filtering and weighted calculation to obtain the following: Figure 4 The target medical image of the target object. The Gaussian filtering and weighted calculation here belong to the prior art and will not be described in detail here.

[0130] It should be noted that the metal artifacts are removed by the technical solution in the above-mentioned embodiment 1, but after the above-mentioned removal of the metal artifacts, the tissue details around the metal artifacts may also be removed, and the metal artifact removal effect is not very optimistic. At this time, after the second corrected image is obtained in the above-mentioned embodiment 1, steps S250 and S260 of the embodiment of the present invention can be implemented, that is, the third corrected image is obtained according to the second corrected image, and then the detail enhancement processing is performed according to the original scanned image and the third corrected image, so as to obtain an image with better quality that is convenient for doctors to diagnose.

[0131] It should be noted that if the image after removing metal artifacts obtained by using the technical solution in the above embodiment 1 has a good effect, the user does not need to perform further detail enhancement, and only the technical solution in the above embodiment 1 can be executed. If the image after removing metal artifacts obtained by using the technical solution in the above embodiment 1 does not meet the user's requirements, the technical solution of the second embodiment of the present invention can be executed to perform subsequent calculations of S250 and S260 on the second corrected image obtained in the first embodiment to obtain an image that meets the requirements and has better quality for doctors to diagnose. Whether to use the technical solution of the first embodiment or the technical solution of the second embodiment can be selected according to the user's needs, and is not limited here.

[0132] from Figure 2 and Figure 4 It can be seen intuitively that Figure 2 and Figure 4 The regions H in are all metal artifact regions. Figure 2After removing the metal artifacts, the tissue details around the metal artifacts are also removed, so some data is lost, such as Figure 2 In the circled area Q, it can be seen that the tissue details around the metal artifacts are also removed. Figure 4 After removing metal artifacts in , the tissue details around the metal artifacts are not removed, such as Figure 4 In the circled area K, it can be seen that in this area K, the tissue details around the metal artifact are not Figure 2 In this area K, many tissue details are retained, and no artifacts of other states are introduced. The image is easier for doctors to diagnose and the integrity of the image is maintained.

[0133] The technical solution of the embodiment of the present invention performs weighted merging calculation on the original scanned image and the second corrected image to obtain a third corrected image, performs Gaussian filtering and weighted calculation on the third corrected image and the original scanned image to obtain a target medical image of the target object, so that the second corrected image can be processed for detail enhancement to obtain an image of better quality that is easier for doctors to diagnose.

[0134] Embodiment 3

[0135] Figure 5 The schematic diagram of the structure of the medical imaging device provided in the third embodiment of the present invention is as follows: Figure 5 As shown, the device includes: an original scan image acquisition module 31, a data acquisition module 32, a first corrected image acquisition module 33, a second corrected image acquisition module 34 and a medical image acquisition module 35.

[0136] An original scan image acquisition module 31 is used to acquire an original scan image of a target object, wherein the original scan image is obtained by reconstructing original data in a data domain, wherein the original data is obtained by performing a medical scan on the target object, wherein a part of the target object contains metal;

[0137] A data acquisition module 32, configured to acquire, based on the original scanned image, a first set of data corresponding to the metal in the data domain, and a second set of data corresponding to the metal in the data domain after the original scanned image is removed;

[0138] A first corrected image acquisition module 33, used to perform weighted processing and reconstruction on the first set of data, the second set of data and the interpolated second set of data to acquire a first corrected image;

[0139] A second corrected image acquisition module 34 is used to determine the boundary information of the metal in the image domain in the original scanned image according to the first corrected image, and correct the original image according to the boundary information of the metal in the image domain to acquire a second corrected image;

[0140] The medical image acquisition module 35 is used to determine the medical image of the target object according to the original scanned image and the second corrected image.

[0141] Based on the technical solution of the above embodiment, the second corrected image acquisition module 34 is specifically used for:

[0142] Determine the boundary information of the metal in the image domain in the original scanned image according to the first corrected image;

[0143] Determining boundary data of the metal in the data domain according to boundary information of the metal in the image domain;

[0144] Determining the weight of the boundary data of the metal in the data domain based on the boundary data of the metal in the data domain and the original data;

[0145] Performing weighted processing on the boundary data of the metal in the data domain, the weight of the boundary data of the metal in the data domain, and the boundary data of the metal in the data domain after interpolation processing to obtain a third weighted result;

[0146] Performing weighted processing on the third weighted result, the boundary data of the metal in the data domain, and the weight of the third weighted result to obtain a fourth weighted result;

[0147] The fourth weighted result is subjected to filtered back-projection to obtain a second corrected image.

[0148] Based on the technical solution of the above embodiment, the device further includes:

[0149] A third corrected image acquisition module, used for performing a weighted merging calculation on the original scanned image and the second corrected image to obtain a third corrected image;

[0150] The target medical image acquisition module is used to perform Gaussian filtering and weighted calculation on the third corrected image and the original scanned image to obtain a target medical image of the target object.

[0151] Based on the technical solution of the above embodiment, the third corrected image acquisition module includes:

[0152] A first target pixel point selection unit is used to perform sampling segmentation calculation on each pixel point in the second corrected image, and select a first target pixel point with a preset pixel value from each pixel point in the segmented second corrected image;

[0153] A second target pixel point selection unit is used to perform sampling and segmentation calculation on each pixel point in the original scanned image, and select a second target pixel point from each pixel point in the segmented original scanned image based on the first target pixel point;

[0154] a weighted result determination unit, configured to obtain a weighted result of the first target pixel and the second target pixel based on a preset step value, a first reference image corresponding to each of the first target pixels, and a second reference image corresponding to each of the second target pixels;

[0155] A statistical result determination unit, configured to determine a statistical result corresponding to the weighted result based on the weighted result;

[0156] The third corrected image acquisition unit is used to determine the third corrected image based on the statistical result.

[0157] Based on the technical solution of the above embodiment, the second target pixel point selection unit is specifically used for:

[0158] Sampling and segmenting calculations are performed on each pixel point in the original scanned image, and based on the first target pixel point, a pixel point at the same position as the first target pixel point is selected from the segmented original scanned image as a second target pixel point.

[0159] Based on the technical solution of the above embodiment, the statistical result determination unit is specifically used for:

[0160] For any of the first target pixel points and the second target pixel points corresponding to the first target pixel point, based on the current weighted result, determine a histogram of the current weighted result and a logarithm of the histogram of the current weighted result;

[0161] The statistical result is obtained by performing weighted calculation on the histogram of the current weighted result and the logarithm of the histogram of the current weighted result.

[0162] Based on the technical solution of the above embodiment, the third corrected image acquisition unit is specifically used for:

[0163] Based on the statistical results, determining the entropy of the histogram of each current weighted result and the image weight corresponding to the entropy of the histogram of each current weighted result;

[0164] The third corrected image is determined based on each of the image weights and the preset step value.

[0165] The image imaging device provided in the embodiment of the present invention can execute the image imaging method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0166] Embodiment 4

[0167] Figure 6 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention is shown in FIG. Figure 6 As shown, the electronic device includes a processor 70, a memory 71, an input device 72 and an output device 73; the number of processors 70 in the electronic device can be one or more. Figure 6 A processor 70 is taken as an example; the processor 70, the memory 71, the input device 72 and the output device 73 in the electronic device can be connected by a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0168] The memory 71, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the medical imaging method in the embodiment of the present invention (for example, the original scanned image acquisition module 31, the data acquisition module 32, the first corrected image acquisition module 33, the second corrected image acquisition module 34 and the medical image acquisition module 35). The processor 70 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 71, that is, realizes the above-mentioned medical imaging method.

[0169] The memory 71 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 71 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 71 may further include a memory remotely arranged relative to the processor 70, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0170] The input device 72 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the electronic device. The output device 73 may include a display device such as a display screen.

[0171] Embodiment 5

[0172] Embodiment 5 of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions are used to execute a medical imaging method when executed by a computer processor.

[0173] Of course, the computer executable instructions of a storage medium containing computer executable instructions provided by an embodiment of the present invention are not limited to the method operations described above, and can also execute related operations in the medical imaging method provided by any embodiment of the present invention.

[0174] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0175] It is worth noting that in the above-mentioned embodiment of the medical imaging device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0176] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A medical imaging method, characterized in that: include: Acquire an original scanned image of a target object, wherein the original scanned image is obtained by reconstructing original data in a data domain, wherein the original data is obtained by performing a medical scan on the target object, wherein a part of the target object contains metal; According to the original scanned image, a first set of data corresponding to the metal in the data domain and a second set of data corresponding to the original scanned image after the metal is removed in the data domain are obtained; wherein the second set of data is the data in the data domain of the remaining image after the metal is removed from the original scanned image; Combining the first set of data, the second set of data, and the interpolated second set of data to perform weighted processing and reconstruction to obtain a first corrected image; Determine the boundary information of the metal in the image domain in the original scanned image according to the first corrected image, and correct the original scanned image according to the boundary information of the metal in the image domain to obtain a second corrected image; Determine a medical image of the target object based on the original scanned image and the second corrected image; The step of combining the first set of data, the second set of data, and the interpolated second set of data for weighted processing and reconstruction to obtain a first corrected image includes: Based on the first group of data and the original data, the weight of the first group of data is determined; the first group of data, the weight of the first group of data and the interpolated second group of data are weighted to obtain a first weighted result; the first weighted result, the second group of data and the weight of the first weighted result are weighted to obtain a second weighted result; the second weighted result is filtered and back-projected to obtain a first corrected image.

2. The method according to claim 1, characterized in that: The step of correcting the original scanned image according to the boundary information of the metal in the image domain to obtain a second corrected image includes: Determining boundary data of the metal in the data domain according to boundary information of the metal in the image domain; Determining the weight of the boundary data of the metal in the data domain based on the boundary data of the metal in the data domain and the original data; Performing weighted processing on the boundary data of the metal in the data domain, the weight of the boundary data of the metal in the data domain, and the boundary data of the metal in the data domain after interpolation processing to obtain a third weighted result; Performing weighted processing on the third weighted result, the boundary data of the metal in the data domain, and the weight of the third weighted result to obtain a fourth weighted result; The fourth weighted result is subjected to filtered back-projection to obtain a second corrected image.

3. The method according to claim 1, characterized in that The method further comprises: Performing a weighted merging calculation on the original scanned image and the second corrected image to obtain a third corrected image; Gaussian filtering and weighted calculation are performed on the third corrected image and the original scanned image to obtain a target medical image of the target object.

4. The method according to claim 3, characterized in that The step of performing weighted merging calculation on the original scanned image and the second corrected image to obtain a third corrected image includes: Sampling and segmenting each pixel point in the second corrected image, and selecting a first target pixel point with a preset pixel value from each pixel point in the segmented second corrected image; Sampling and segmenting each pixel point in the original scanned image, and selecting a second target pixel point from each pixel point in the segmented original scanned image based on the first target pixel point; Obtaining weighted results of the first target pixel and the second target pixel based on a preset step value, a first reference image corresponding to each of the first target pixels, and a second reference image corresponding to each of the second target pixels; Based on the weighted result, determining a statistical result corresponding to the weighted result; Based on the statistical results, the third corrected image is determined.

5. The method according to claim 4, characterized in that The selecting, based on the first target pixel point, a second target pixel point from each pixel point in the segmented original scanned image comprises: Based on the first target pixel point, a pixel point at the same position as the first target pixel point is selected from the segmented original scanned image as a second target pixel point.

6. The method according to claim 4, characterized in that The determining, based on the weighted result, a statistical result corresponding to the weighted result includes: For any of the first target pixel points and the second target pixel points corresponding to the first target pixel point, the following steps are performed to determine the statistical result: Based on the current weighted result, determine a histogram of the current weighted result and a logarithm of the histogram of the current weighted result; The statistical result is obtained by performing weighted calculation on the histogram of the current weighted result and the logarithm of the histogram of the current weighted result.

7. The method according to claim 6, characterized in that The step of determining the third corrected image based on the statistical result includes: Based on the statistical results, determining the entropy of the histogram of each current weighted result and the image weight corresponding to the entropy of the histogram of each current weighted result; The third corrected image is determined based on each of the image weights and the preset step value.

8. An image forming device, characterized in that: include: An original scan image acquisition module, used to acquire an original scan image of a target object, wherein the original scan image is obtained by reconstructing original data in a data domain, wherein the original data is obtained by performing a medical scan on the target object, wherein a part of the target object contains metal; A data acquisition module, used to acquire, according to the original scanned image, a first set of data corresponding to the metal in the data domain and a second set of data corresponding to the original scanned image after the metal is removed in the data domain; wherein the second set of data is the data in the data domain of the remaining image after the metal is removed from the original scanned image; A first corrected image acquisition module, used for performing weighted processing and reconstruction on the first set of data, the second set of data and the interpolated second set of data to acquire a first corrected image; A second corrected image acquisition module, configured to determine the boundary information of the metal in the image domain in the original scanned image according to the first corrected image, and to correct the original scanned image according to the boundary information of the metal in the image domain to acquire a second corrected image; A medical image acquisition module, used to determine the medical image of the target object according to the original scanned image and the second corrected image; The first corrected image acquisition module is specifically used for: Based on the first set of data and the original data, determine the weight of the first set of data; perform weighted processing on the first set of data, the weight of the first set of data, and the interpolated second set of data to obtain a first weighted result; The first weighted result, the second group of data and the weight of the first weighted result are weighted to obtain a second weighted result; and the second weighted result is filtered and back-projected to obtain a first corrected image.

9. An electronic device, characterized in that: The device comprises: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the medical imaging method according to any one of claims 1 to 7.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to perform the medical imaging method according to any one of claims 1 to 7 when executed by a computer processor.

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