Energy spectrum image processing method and apparatus, device, and storage medium

Abstract

An energy spectrum image processing method and apparatus, a device, and a storage medium. The method comprises: determining an initial substance base image (S110); determining a current iteration result corresponding to a current number of diffusion steps to obtain current noise data corresponding to the current iteration result, and on the basis of the current noise data, performing noise reduction processing on the current iteration result to update the current iteration result (S120); and taking an updated current iteration result as a target substance base image (S130).

Description

Energy spectrum image processing method, device, equipment and storage medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on December 8, 2023, with application number 202311689521.8. The entire contents of the above application are incorporated by reference into this application. Technical Field

[0002] The present application relates to the technical field of medical image processing, for example, to a method, apparatus, device and storage medium for energy spectrum image processing. Background Art

[0003] Energy spectrum image reconstruction is typically performed by modeling the physical process of data acquisition. This approach improves the quantitative accuracy of material decomposition by incorporating a model of the data acquisition physical process. However, the material decomposition problem associated with existing nonlinear multi-energy X-ray measurement models is highly pathological.

[0004] Therefore, it is necessary to provide a method for reconstructing energy spectrum images to improve the quantitative accuracy of material decomposition of energy spectrum images.

[0005] Summary of the Invention

[0006] The present application provides a method, apparatus, device and storage medium for energy spectrum image processing.

[0007] According to one aspect of the present application, a method for processing an energy spectrum image is provided, comprising:

[0008] determining an initial material-based image that meets initial quantitative accuracy conditions;

[0009] During iterative reconstruction of a target image performed on the initial material-based image, a current iteration result corresponding to a current number of diffusion steps is determined, current noise data corresponding to the current iteration result is obtained based on a pre-trained diffusion noise model, and noise reduction processing is performed on the current iteration result based on the current noise data to update the current iteration result, wherein the starting number of diffusion steps in the iterative reconstruction of the target image is determined based on a reverse diffusion starting point, and the reverse diffusion starting point is determined based on data acquisition conditions corresponding to the initial material-based image;

[0010] When the iterative reconstruction of the target image is completed, the updated current iteration result is used as the target material basis image.

[0011] According to another aspect of the present application, there is provided an energy spectrum image processing device, comprising:

[0012] an image determination module configured to determine an initial material-based image that meets an initial quantitative accuracy condition;

[0013] a reconstruction denoising module configured to, during a process of iteratively reconstructing a target image on the initial material-based image, determine a current iteration result corresponding to a current number of diffusion steps, obtain current noise data corresponding to the current iteration result based on a pre-trained diffusion noise model, and perform denoising on the current iteration result based on the current noise data to update the current iteration result, wherein the starting number of diffusion steps in the iterative reconstruction of the target image is determined based on a reverse diffusion starting point, and the reverse diffusion starting point is determined based on data acquisition conditions corresponding to the initial material-based image;

[0014] The result module is configured to use the updated current iteration result as the target material basis image when the iterative reconstruction of the target image is completed.

[0015] According to another aspect of the present application, an electronic device is provided, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy spectrum image processing method described in any embodiment of the present application.

[0019] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the energy spectrum image processing method described in any embodiment of the present application when executed.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] FIG1 is a flow chart of a method for processing an energy spectrum image according to an embodiment of the present application;

[0022] FIG2 is a schematic diagram of a technology for acquiring energy spectrum images according to an embodiment of the present application;

[0023] FIG3 is a schematic diagram showing the results of different energy spectrum image processing methods provided in an embodiment of the present application;

[0024] FIG4 is another flow chart of the energy spectrum image processing method provided according to an embodiment of the present application;

[0025] FIG5A is a schematic diagram of a first base image provided according to an embodiment of the present application;

[0026] FIG5B is a schematic diagram of a second base image provided according to an embodiment of the present application;

[0027] FIG6 is a flow chart of a noise reduction method according to an embodiment of the present application;

[0028] FIG7A is a schematic diagram of the structure of a diffusion noise model provided according to an embodiment of the present application;

[0029] FIG7B is a schematic diagram of the structure of a residual network provided according to an embodiment of the present application;

[0030] FIG8A is a schematic diagram of the structure of an energy spectrum image processing device provided according to an embodiment of the present application;

[0031] FIG8B is another structural diagram of an energy spectrum image processing device provided according to an embodiment of the present application;

[0032] FIG9 is a schematic structural diagram of a C-arm CT imaging system provided in an embodiment of the present application;

[0033] FIG10A is a schematic diagram of the structure of a diagnostic CT imaging system provided in an embodiment of the present application;

[0034] FIG10B is a schematic structural diagram of another diagnostic CT imaging system provided in an embodiment of the present application;

[0035] FIG11 is a schematic structural diagram of an electronic device for implementing the energy spectrum image processing method according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] FIG1 is a flow chart of a spectrum image processing method provided by an embodiment of the present application. In this embodiment, a pre-trained diffusion noise model is used during the iterative image reconstruction process to implement constrained noise reduction on the iterative results, thereby improving the quantitative accuracy of material decomposition of material-based images. The method can be performed by a spectrum image processing device, which can be implemented in the form of hardware and / or software and can be configured in a processor of an electronic device. As shown in FIG1 , the method includes:

[0038] S110: Determine an initial material-based image that meets initial quantitative accuracy conditions.

[0039] The initial material-based image that meets the initial quantitative accuracy conditions refers to a material-based image that can meet the preliminary quantitative accuracy requirements.

[0040] For example, a material-based image to be processed corresponding to the projection data is determined based on image domain decomposition; and an initial image iterative reconstruction algorithm based on a model is used to perform iterative reconstruction on the material-based image to be processed to obtain an initial material-based image that meets the initial quantitative accuracy conditions.

[0041] The projection data can be acquired by using any scanning method including the dual-source dual-detector method, the fast tube voltage switching method, the line beam filtering method, the photon counting method and the double-layer detector method shown in FIG2 .

[0042] For example, after the material-based image to be processed is determined, the following formula (1) is used to perform iterative reconstruction of the initial image on the material-based image to be processed. The number of iterations can be set to 50, 60, or 70 times, etc., to obtain an initial material-based image that meets the initial quantitative accuracy condition. It can be understood that the number of iterative reconstructions can be determined based on the initial quantitative accuracy condition.

[0043] Wherein, formula (1) is as follows:

[0044] In the iterative reconstruction process of the initial image, t is the number of iterations. j,t Indicates the current iteration result or the material-based image to be processed with pixel identifier j and iteration number t. For example, if iteration number t is the initial iteration number, then C j,t is the material-based image to be processed, otherwise C j,t is the current iteration result corresponding to the iteration number t. Formula (1) is used to perform an image iterative reconstruction on the material-based image to be processed corresponding to the iteration number t or the current iteration result to obtain the corresponding iteration result. The iteration number corresponding to the iteration result is The iterative result is used as the image corresponding to the iteration number t-1, and is used to enter the next image iterative reconstruction process.

[0045] In formula (1), Q(Cj ; C j,t )For C j The first derivative is a vector that is j,t hour:

[0046] in, Represents the number of photons projected by C before iteration t.

[0047] In Q(C j ; C j,t ) corresponds to C j The second derivative is a Hessian matrix, which is j,t hour:

[0048] The derivation process of formula (1) is as follows: Based on the Bayesian formula, the following formula can be obtained:

[0049] Where C={c jk'} N×K Represents the equivalent decomposition coefficient image of K material bases, and each equivalent decomposition coefficient image has N pixels, N represents the collected projection data (the number of photons of each ray detected by the detector), Represents the projection data obtained by forward projection calculation of the material-based image. is the data fidelity item, is the regularization term.

[0050] The Poisson model is used to model the data fidelity term as follows:

[0051] Among them, i represents the index of X-ray, k represents the index of energy spectrum, and the maximum value is K. Then minimize Minimize According to the transfer optimization principle, it is converted into a quadratic objective function Q(C; C t ), the objective function is as follows:

[0052] Among them, c jk' Indicates the equivalent coefficient of the j-th pixel and the k'th material basis; ω ikε represents the number of “empty scan” photons at the i-th ray, the k-th energy spectrum, and the ε-th energy level; b k'ε represents the attenuation coefficient of the k'th material basis at the εth energy level; {a ij} represents the system matrix; γ ij Indicates a ij / ∑ j aij ;q ikε,t (·) is a quadratic scalar function whose argument is a vector; t iε (C t )=exp(-∑ k' b k'ε ∑ j a ij c jk' ).

[0053] The above objective function for each pixel is solved using the Newton iteration method to obtain the above formula (1).

[0054] S120. During the iterative reconstruction of the target image on the initial material-based image, a current iteration result corresponding to the current number of diffusion steps is determined, current noise data corresponding to the current iteration result is obtained based on a pre-trained diffusion noise model, and noise reduction processing is performed on the current iteration result based on the current noise data to update the current iteration result, wherein the starting number of diffusion steps in the iterative reconstruction of the target image is determined based on the inverse diffusion starting point, and the inverse diffusion starting point is determined based on the data acquisition conditions corresponding to the initial material-based image.

[0055] The diffusion noise model can be understood as a diffusion model based on noise control. For example, the denoising process of the model can be controlled by controlling the starting point of the reverse diffusion during the iterative reconstruction of the target image.

[0056] Formula (1) is used to perform iterative reconstruction of the target image on the initial material-based image. At this time, C j,t Represents the current iteration result or initial material basis image with pixel ID j and diffusion step number t. For example, if diffusion step number t is the starting diffusion step number, then C j,t is the initial material-based image that meets the initial quantitative accuracy conditions, otherwise C j,t is the current iteration result corresponding to the diffusion step number t. It can be understood that the iterative result corresponding to the image iterative reconstruction of the initial material-based image corresponding to the diffusion step number t or the current iteration result is obtained by using formula (1). The diffusion step number corresponding to the iterative result is

[0057] In one embodiment, the target image iterative reconstruction process may be:

[0058] Step a1: Determine the initial diffusion step number corresponding to the initial material-based image, perform image reconstruction corresponding to the initial diffusion step number on the initial material-based image, and obtain the current iteration result.

[0059] Step a2: input the current iteration result and the number of diffusion steps corresponding to the current iteration result into a pre-trained diffusion noise model to obtain current noise data corresponding to the current iteration result.

[0060] Step a3: performing noise reduction processing on the current iteration result based on the current noise data, and using the noise reduction processing result as the updated current iteration result.

[0061] Step a4: Determine the current diffusion step number by subtracting one from the number of steps. If the updated current diffusion step number does not meet the set termination condition, perform image reconstruction corresponding to the current diffusion step number on the current iteration result to obtain the current iteration result, and return to step a2. If the updated current diffusion step number meets the set termination condition, stop the image iterative reconstruction.

[0062] For example, if the current diffusion step number corresponding to the current iteration result is t, then formula (1) is used to iteratively reconstruct it to update the current iteration result. The diffusion step number corresponding to the updated iterative reconstruction result is The updated current iteration result is compared with the Input the trained diffuse noise model and control the model based on the The corresponding model parameters are used to determine the current noise data corresponding to the updated current iteration result; the current noise data is then used to perform denoising on the updated current iteration result to update the current iteration result again, and then the current diffusion step number is set to t-1, and the above process is repeated.

[0063] The termination condition is set to be that the current diffusion step number is 0, that is, when it is detected that the updated current diffusion step number is zero, the above-mentioned iterative reconstruction process of the target image is terminated.

[0064] The output of the pre-trained diffuse noise model in this embodiment is denoted as g Θ* (C t ,t), Θ* is the network parameter of the pre-trained diffusion noise model, which corresponds to the number of diffusion steps. The correspondence between the network parameters and the number of diffusion steps achieves the effect of performing targeted regularization denoising on each iteration result of image iterative reconstruction using the pre-trained diffusion noise model, improving the accuracy of denoising the current iteration result, that is, the accuracy of the updated current iteration result. The improved accuracy of the updated current iteration result helps improve the accuracy of the next image reconstruction, thereby improving the accuracy of the entire iterative image reconstruction.

[0065] In one embodiment, the current iteration result and the number of diffusion steps corresponding to the current iteration result are input into a pre-trained diffusion noise model to obtain current noise data. Because the number of diffusion steps corresponding to the current iteration result is input along with the current iteration result, the model can determine the current noise data corresponding to the current iteration result based on the number of diffusion steps corresponding to the current iteration result, thereby improving the accuracy of noise data determination.

[0066] To further enhance the denoising effect, this embodiment determines the reverse diffusion starting point during the iterative reconstruction of the target image based on the data acquisition conditions corresponding to the initial material-based image, and then determines the starting diffusion step number based on this reverse diffusion starting point. By establishing a correspondence between the starting diffusion step number and the data acquisition conditions corresponding to the initial material-based image, the accuracy of determining the starting diffusion step number is improved, enabling the energy spectrum image processing method provided in this embodiment of the application to flexibly process energy spectrum data acquired based on a variety of data acquisition conditions.

[0067] S130 . When the iterative reconstruction of the target image is completed, the updated current iteration result is used as the target material-based image.

[0068] After the iterative reconstruction of the image is completed, the latest updated current iteration result is used as the target material image.

[0069] Figure 3 is a schematic diagram of the results of different energy spectrum image processing methods provided in an embodiment of the present application. As can be seen from Figure 3, compared to the true values ​​of the water-based image and the iodine-based image, the error images of the water-based image and the iodine-based image determined using the image-domain material decomposition method are the largest; the error images of the water-based image and the iodine-based image determined using the model-based iterative reconstruction method are second largest; and the error images of the water-based image and the iodine-based image determined using the energy spectrum image processing method described in an embodiment of the present application are the smallest. The true values ​​of the water-based image and the iodine-based image are determined based on first projection data acquired under high scanning dose conditions. The image-domain material decomposition method, the model-based iterative reconstruction method, and the energy spectrum image processing method described in an embodiment of the present application process the same second projection data acquired under low scanning dose conditions. The acquisition conditions of the first and second projection data differ only in the scanning dose.

[0070] In one embodiment, the system matrix is ​​stored in a pre-sparse storage mode. ij} represents the length of the i-th X-ray passing through the j-th pixel. Each X-ray ray only passes through a portion of the pixels in the entire image, that is, it only corresponds to a portion of the pixels in the entire image, so the entire system matrix is ​​very sparse. Moreover, for the same scanning system, the system matrix remains unchanged during each scan. Therefore, using pre-sparse storage of the system matrix can increase the iteration speed of formula (1), thereby improving the speed of the entire energy spectrum image processing.

[0071] In one embodiment, CUDA (Compute Unified Device Architecture) programming is used to increase the speed of energy spectrum image processing. Graphics Processing Units (GPUs) can significantly increase image processing speed, and CUDA programming allows developers to use NVIDIA GPUs for general-purpose computing. Therefore, using CUDA programming can significantly improve the speed and accuracy of energy spectrum image processing.

[0072] In one embodiment, parallel computing is used to increase the speed of spectral image processing. A three-dimensional image of a target object (patient) comprises at least two two-dimensional slices, each of which is independently reconstructed. Therefore, parallel image iterative reconstruction is used to increase the speed of spectral image processing.

[0073] In one embodiment, the speed of spectral image processing is increased by upgrading and expanding graphics cards. To support parallel image iterative reconstruction, a fast graphics card is selected for spectral image processing. For a 3D image consisting of 400 slices, reconstructing each slice (with a resolution of 512×512) requires approximately 1GB of video memory. In this case, six Nvidia H100 (80GB) graphics cards can be configured.

[0074] Using this speed-enhancing optimization strategy, for projection data acquired under conventional data acquisition conditions, if the initial diffusion step number is set to 250 and the corresponding slice reconstruction time is 400 slices, it can be reduced to 95 seconds. It should be noted that, while keeping other scanning conditions unchanged, the lower the scan dose and the larger the initial diffusion step number, the longer the corresponding image processing time.

[0075] The energy spectrum image processing method provided in the embodiment of the present application determines the current iteration result corresponding to the current diffusion step number during the iterative reconstruction of the target image performed on the initial material-based image, inputs the current iteration result into a pre-trained diffusion noise model to obtain the current noise data corresponding to the current diffusion step number, and performs denoising on the current iteration result based on the current noise data to update the current iteration result, thereby achieving regularized constrained denoising of the iterative reconstruction of the image based on the pre-trained diffusion noise model, so that the final target material-based image has high quantitative accuracy and very natural noise texture, which can fully meet the clinical requirements for energy spectrum image quality; moreover, since the starting diffusion step number in the iterative reconstruction of the target image is determined based on the inverse diffusion starting point, and the inverse diffusion starting point is determined based on the data acquisition conditions corresponding to the initial material-based image, the pre-trained diffusion noise model can process the initial material-based images corresponding to various data acquisition conditions, thereby meeting the energy spectrum image processing requirements of different scenarios.

[0076] FIG4 is another flow chart of the energy spectrum image processing method provided in an embodiment of the present application. This embodiment adds a step of determining the number of initial diffusion steps based on the previous embodiment. As shown in FIG4 , the method includes:

[0077] S2101. Determine an initial material-based image that meets initial quantitative accuracy conditions.

[0078] S2102: Determine target data acquisition conditions corresponding to the initial material-based image.

[0079] The target acquisition conditions include but are not limited to at least one of scanning dose, tube current, tube voltage, X-ray filter material, and butterfly filter.

[0080] S2103 : Determine the noise covariance matrix corresponding to the target data acquisition condition according to the correspondence between the pre-created data acquisition condition and the noise covariance matrix.

[0081] A pre-created first correspondence file is read, wherein the first correspondence file records at least two data acquisition conditions and a noise covariance matrix corresponding to each data acquisition condition, and a noise covariance matrix corresponding to a target data acquisition condition is read from the first correspondence file.

[0082] In one embodiment, the noise covariance matrix corresponding to each data acquisition condition is determined by the following steps:

[0083] Step b1: For each data acquisition condition in the data acquisition condition directory, normalize the first base image and the second base image corresponding to the current data acquisition condition, respectively. The first base image and the second base image are two base images corresponding to the projection data of the calibration phantom, and the two base images are determined based on image domain decomposition.

[0084] The first and second base images of the calibration phantom are acquired by controlling the spectral imaging system to scan the calibration phantom based on the current scan protocol for each scan protocol in the scan protocol catalog, obtaining projection data. The first and second base images are then determined based on this projection data. The calibration phantom is the largest phantom scannable by the spectral imaging system, such as a 40 cm diameter water phantom. Different scan protocols correspond to different data scanning conditions.

[0085] Exemplarily, the first base image of the calibration phantom is a water-based image (see FIG5A ), and the second base image of the calibration phantom is an iodine-based image (see FIG5B ); the first base image is normalized using a window of [800, 1200] mg / m; and the second base image is normalized using a window of [0, 15] mg / m.

[0086] Step b2: Select at least two uniform image blocks from the first base image and the second base image, respectively, and positions of the at least two uniform image blocks on the first base image on the first base image correspond to positions of the at least two uniform image blocks on the second base image on the second base image.

[0087] Exemplarily, according to the same uniform image block selection rule, the same selection box is used to select n uniform image blocks from the first base image and the second base image respectively; the position of the uniform image block marked as 1 on the first base image on the first base image is the same as the position of the uniform image block marked as 1 on the second base image on the second base image; the position of the uniform image block marked as 2 on the first base image on the first base image is the same as the position of the uniform image block marked as 2 on the second base image on the second base image, and so on, the position of the uniform image block marked as n on the first base image on the first base image is the same as the position of the uniform image block marked as n on the second base image on the second base image.

[0088] Step b3: Calculate a first noise covariance matrix between two corresponding uniform image blocks on the first base image and the second base image to obtain at least two first noise covariance matrices.

[0089] Calculate the first noise covariance matrix between the uniform image block identified as 1 on the first base image and the uniform image block identified as 1 on the second base image, calculate the first noise covariance matrix between the uniform image block identified as 2 on the first base image and the uniform image block identified as 2 on the second base image, and so on, calculate the first noise covariance matrix between the uniform image block identified as n on the first base image and the uniform image block identified as 2 on the second base image.

[0090] Step b4: For each first noise covariance matrix in the at least two first noise covariance matrices, use a ratio of a current first noise covariance matrix to a maximum value of the current first noise covariance matrix as a second noise covariance matrix.

[0091] After each first noise covariance matrix is ​​determined, the ratio of each data in each first noise covariance matrix to the maximum value in the first noise covariance matrix is ​​determined, and the ratio is used as the second noise covariance matrix. The calculation formula is as follows:

[0092] Where S is the second noise covariance matrix, is the mean of the first noise covariance matrix.

[0093] Step b5: taking the mean of all the second noise covariance matrices as the noise covariance matrix corresponding to the current data acquisition condition.

[0094] In one embodiment, the mean of all second noise covariance matrices is calculated and used as the noise covariance matrix corresponding to the current data acquisition condition. This covariance determination method is simple and direct.

[0095] In one embodiment, all outliers in the second noise covariance matrices are removed, and the mean of all remaining second noise covariance matrices is determined, and the mean is used as the noise covariance matrix corresponding to the current data acquisition conditions. This embodiment can ensure that the noise covariance matrix corresponding to the current data acquisition conditions has high accuracy.

[0096] S2104 , determining an inverse diffusion starting point estimate according to the noise covariance matrix, and determining a starting diffusion step number in an iterative reconstruction process of a target image according to the inverse diffusion starting point estimate.

[0097] The reverse starting point (RSP) of the reverse diffusion denoising process using a trained diffusion noise model is also related to the noise covariance matrix. For example, a smaller scan dose results in higher noise and a larger noise covariance matrix. A larger noise covariance matrix, on the other hand, results in a larger reverse diffusion starting point, and vice versa.

[0098] The starting point of the reverse diffusion should be selected so that the signal-to-noise ratio of the image is approximately To this end, this embodiment determines the inverse diffusion starting point estimate based on the noise covariance matrix corresponding to the target data acquisition conditions, which can be calculated using the following formula:

[0099] Since the data fidelity term will introduce noise during the iterative reconstruction of the target image, the amount of noise to be removed is greater than that in the original image. Therefore, the starting point of the reverse diffusion should be appropriately increased based on the estimated amount of the reverse diffusion starting point, such as increasing by 30 steps or 20 steps depending on the scanning dose. If 30 steps are increased, then Among them, RSP is the reverse diffusion starting point, is the estimated value of the starting point of reverse diffusion, 30 is the correction value of the starting point of reverse diffusion, is the mean of the first noise covariance matrix.

[0100] In one embodiment, after the inverse diffusion starting point estimate is determined, the number of diffusion steps corresponding to the inverse diffusion starting point estimate is directly used as the starting diffusion step number. The method for determining the starting diffusion step number is simple and direct.

[0101] In one embodiment, after the backdiffusion starting point estimate is determined, the starting number of diffusion steps in the iterative reconstruction of the target image is determined based on the backdiffusion starting point estimate and the target correction value. In this embodiment, the target correction value remains unchanged for different backdiffusion starting point estimates or data acquisition conditions. The method for determining the starting number of diffusion steps is also relatively simple.

[0102] In one embodiment, a reverse diffusion starting point correction value corresponding to the target data acquisition conditions is determined based on a pre-established correspondence between the data acquisition conditions and the reverse diffusion starting point correction value; a reverse diffusion starting point is determined based on the reverse diffusion starting point estimate and the reverse diffusion starting point correction value; and the number of initial diffusion steps in the iterative reconstruction of the target image is determined based on the reverse diffusion starting point. This embodiment allows for matching different reverse diffusion starting point correction values ​​to different data acquisition conditions, resulting in better correction effects for the reverse diffusion starting point and improved denoising effects for the trained diffusion noise model.

[0103] For example, a pre-created second correspondence file is read, the second correspondence file recording at least two data acquisition conditions and the inverse diffusion starting point correction value corresponding to each data acquisition condition. The inverse diffusion starting point correction value corresponding to the target data acquisition condition is determined from the second correspondence file. For example, the first correspondence file and the second correspondence file form a single correspondence file, i.e., the single correspondence file records at least two data acquisition conditions and the noise covariance matrix and inverse diffusion starting point correction value corresponding to each data acquisition condition.

[0104] In one embodiment, a correspondence between data acquisition conditions and the correction amount of the reverse diffusion starting point is determined based on a large amount of experimental data. The establishment of this correspondence greatly improves the accuracy of determining the reverse diffusion starting point and the accuracy of the starting diffusion steps determined based on the reverse diffusion starting point.

[0105] S220. During the iterative reconstruction of the target image on the initial material-based image, a current iteration result corresponding to the current number of diffusion steps is determined, current noise data corresponding to the current iteration result is obtained based on a pre-trained diffusion noise model, and noise reduction processing is performed on the current iteration result based on the current noise data to update the current iteration result.

[0106] S230 . When the iterative reconstruction of the target image is completed, the updated current iteration result is used as the target material-based image.

[0107] The embodiment of the present application determines a reverse diffusion starting point based on the data acquisition conditions of the initial material-based image, and determines the starting diffusion steps for iterative reconstruction of the target image based on the reverse diffusion starting point, thereby achieving accurate matching of the starting diffusion steps for the initial material-based image, improving the accuracy of determining the starting diffusion steps, and thus improving the image quality of the denoised material-based image.

[0108] FIG6 is a flow chart of a noise reduction method provided in an embodiment of the present application, which is used to refine the noise reduction processing flow in the aforementioned embodiment. As shown in FIG6 , the method includes:

[0109] S3201. Determine a noise weighting coefficient corresponding to the current noise data, where the noise weighting coefficient is determined based on a diffusion step number estimate, and the diffusion step number estimate is determined based on a set diffusion parameter.

[0110] Determining the noise weighting coefficient corresponding to the current noise data is essentially determining the noise weighting coefficient of the diffusion step number corresponding to the current noise data. In one embodiment, the noise weighting coefficient is determined by the following steps:

[0111] in, is an estimator of the number of diffusion steps, which can be determined by the following formula:

[0112] β max and β min is to set the diffusion parameters, which are 0.02 and 0.0001 respectively, and T is to set the total number of diffusion steps, which can be set to 1000.

[0113] S3202: Perform noise reduction processing on the current iteration result based on the current noise data and the noise weighting coefficient to update the current iteration result.

[0114] The product of the current noise data and the noise weighting coefficient is determined, and the current noise data is updated using the product; the difference between the current iteration result and the updated current noise data is determined, and the difference is used as the updated current iteration result.

[0115] In one embodiment, the denoising process is implemented by the following formula:

[0116] It can be seen from the above embodiments that The current iteration result is processed by the formula. The denoising process is performed on the current iteration result. The diffusion step number of the denoised current iteration result, that is, the updated current iteration result, is t-1. T-1 is exactly the diffusion step number corresponding to the next image iteration process.

[0117] The embodiment of the present application improves the accuracy of the noise data corresponding to the current iteration result by using the noise weighting coefficient corresponding to the current noise data, thereby improving the accuracy of denoising the current iteration result and the accuracy of image iterative reconstruction, that is, improving the image quality of the target material-based image.

[0118] FIG7A is a schematic diagram of the structure of a diffusion noise model provided in an embodiment of the present application. This embodiment illustrates the diffusion noise model in the aforementioned embodiment.

[0119] As shown in FIG7A , the diffuse noise model has a symmetrical structure, including an encoder on the left, a decoder on the right, and a bottleneck layer for connecting the encoder and the decoder.

[0120] The encoder consists of four residual networks and a pooling layer connected to the end of each residual network. The residual network is used to determine the target residual result of the received feature map, and the pooling layer is used to downsample the received feature map. The kernel size of each pooling layer is K = 2 and the stride is S = 2. After four rounds of residual calculation and pooling, the current iteration result of 32×32 becomes a 256×256 image.

[0121] The decoder includes an upsampling layer and a residual network connected to the end of each upsampling layer. The upsampling layer is used to perform upsampling operations on the received feature map with a step size of 2, 3×3. The upsampling result and the final encoding result of the corresponding layer are spliced ​​together to obtain a splicing result, which is sent to the corresponding residual network. Each residual network performs two convolution operations on the received splicing result. The last residual network sends the final convolution result to the normalization network, which includes a GN (Group Normalization) layer, a GELU (Gaussian Error Linear Unit) activation function and a 3×3 convolution kernel.

[0122] The bottleneck layer connects the encoder and decoder through two residual networks.

[0123] As shown in Figure 7B, the residual network includes two inputs: one is the embedded time information, such as the number of diffusion steps, and the other is the received feature map. The embedded time information is processed using the GELU activation function and the linear layer to obtain a time result. The received feature map is processed using the residual network to obtain a residual result; a combination of the time result and the residual result is determined; the combined result is processed using the residual network to obtain an intermediate residual result; and a target residual result is determined based on the intermediate residual result and the received feature map. This target residual result is the output data of the residual network.

[0124] The diffuse noise model in this embodiment uses the following loss function for model training:

[0125] in,

[0126] C s,t is the initial material basis image in the sth group of samples in the sample set, is the diffusion step estimator, Δ is random noise sampled using the Monte Carlo method and obeys a Gaussian distribution with mean 0 and covariance S. The method for determining the covariance S can be found in the noise covariance matrix determination method described in the previous embodiment, and is not further described in this embodiment.

[0127] The embodiments of the present application describe the structure and loss function of a diffuse noise model. The coordinated use of the model with this structure and the loss function improves the noise prediction accuracy and generalizability of a pre-trained diffuse noise model.

[0128] FIG8A is a schematic diagram of the structure of the energy spectrum image processing device provided in an embodiment of the present application. As shown in FIG8A , the device includes:

[0129] An image determination module 51 is configured to determine an initial substance-based image that meets an initial quantitative accuracy condition;

[0130] a reconstruction denoising module 52 configured to, during a process of iteratively reconstructing a target image on the initial material-based image, determine a current iteration result corresponding to a current number of diffusion steps, obtain current noise data corresponding to the current iteration result based on a pre-trained diffusion noise model, and perform denoising on the current iteration result based on the current noise data to update the current iteration result, wherein the starting number of diffusion steps in the iterative reconstruction of the target image is determined by a reverse diffusion starting point, and the reverse diffusion starting point is determined based on data acquisition conditions corresponding to the initial material-based image;

[0131] The result module 53 is configured to use the updated current iteration result as the target material-based image when the iterative reconstruction of the target image is completed.

[0132] In one embodiment, the reconstruction denoising module 52 is configured as follows:

[0133] The current iteration result and the number of diffusion steps corresponding to the current iteration result are input into a pre-trained diffusion noise model to obtain current noise data.

[0134] In one embodiment, the reconstruction denoising module 52 is configured as follows:

[0135] Determining a noise weighting coefficient corresponding to the current noise data, wherein the noise weighting coefficient is determined based on a diffusion step number estimate, and the diffusion step number estimate is determined based on a set diffusion parameter;

[0136] Noise reduction processing is performed on the current iteration result based on the current noise data and the noise weighting coefficient to update the current iteration result.

[0137] In one embodiment, the loss function used to train the noise model is:

[0138] Among them, C s,t is the material-based image at time t in the s-th group of samples, is the estimated value of the diffusion step number, Δ is the random noise sampled by the Monte Carlo method, and the random noise obeys the Gaussian distribution with mean 0 and covariance S, where S is the noise covariance matrix, which is determined based on the data acquisition conditions corresponding to the corresponding samples.

[0139] In one embodiment, as shown in FIG8B , the apparatus further includes an initial diffusion step number module 54 , and the initial diffusion step number module 54 includes:

[0140] A data acquisition condition unit, configured to determine a target data acquisition condition corresponding to the initial material-based image;

[0141] a noise covariance matrix unit configured to determine a noise covariance matrix corresponding to the target data acquisition condition based on a correspondence between a pre-created data acquisition condition and a noise covariance matrix;

[0142] The starting diffusion step number unit is configured to determine an inverse diffusion starting point estimate according to the noise covariance matrix, and determine an initial diffusion step number in the iterative reconstruction process of the target image according to the inverse diffusion starting point estimate.

[0143] In one embodiment, the noise covariance matrix unit is further configured as:

[0144] Determining the inverse diffusion starting point correction amount corresponding to the target data acquisition condition according to the pre-established correspondence between the data acquisition condition and the inverse diffusion starting point correction amount;

[0145] The starting diffusion step number unit is set to:

[0146] Determining a reverse diffusion starting point according to the reverse diffusion starting point estimation amount and the reverse diffusion starting point correction amount;

[0147] The number of initial diffusion steps in the iterative reconstruction process of the target image is determined according to the inverse diffusion starting point.

[0148] In one embodiment, the noise covariance matrix unit is set as:

[0149] For each data acquisition condition in the data acquisition condition directory, the first base image and the second base image corresponding to the current data acquisition condition are normalized respectively, where the first base image and the second base image are two base images corresponding to the projection data of the calibration phantom, and the two base images are determined based on image domain decomposition.

[0150] selecting at least two uniform image blocks from the first base image and the second base image, respectively, wherein positions of the at least two uniform image blocks on the first base image on the first base image correspond to positions of the at least two uniform image blocks on the second base image on the second base image;

[0151] Calculating a first noise covariance matrix between two uniform image blocks having a corresponding relationship on the first base image and the second base image to obtain at least two first noise covariance matrices;

[0152] For each of the at least two first noise covariance matrices, determining a ratio of a current first noise covariance matrix to a maximum value of the current first noise covariance matrix as a second noise covariance matrix;

[0153] The mean value of all the second noise covariance matrices is used as the noise covariance matrix corresponding to the current data acquisition condition.

[0154] In one embodiment, the image determination module is configured to:

[0155] Determining a material-based image to be processed corresponding to the projection data based on image domain decomposition;

[0156] A model-based iterative reconstruction algorithm is used to perform iterative reconstruction for a set number of times on the material-based image to be processed, so as to obtain an initial material-based image that meets the initial quantitative accuracy conditions.

[0157] The energy spectrum image processing device provided in the embodiment of the present application determines the current iteration result corresponding to the current diffusion step number during the process of iterative reconstruction of the target image on the initial material-based image, inputs the current iteration result into a pre-trained diffusion noise model to obtain current noise data corresponding to the current diffusion step number, and performs denoising on the current iteration result based on the current noise data to update the current iteration result, thereby achieving regularized constrained denoising of the iterative reconstruction of the image based on the pre-trained diffusion noise model, so that the final material-based image has high quantitative accuracy and very natural noise texture, which can fully meet the clinical requirements for energy spectrum image quality; moreover, since the starting diffusion step number in the iterative reconstruction of the target image is determined based on the inverse diffusion starting point, and the inverse diffusion starting point is determined based on the data acquisition conditions corresponding to the initial material-based image, the pre-trained diffusion noise model can process the initial material-based images corresponding to various data acquisition conditions, thereby meeting the energy spectrum image processing requirements of different scenarios.

[0158] The energy spectrum image processing device provided in the embodiments of the present application can execute the energy spectrum image processing method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method.

[0159] FIG9 is a schematic diagram of the structure of a C-arm CT (C-arm Computed Tomography) imaging system provided in another embodiment of the present application. The system includes a gantry 1211, a detector 1212, a bed 1214, an X-ray tube 1215, a C-arm drive shaft 1216, a rotating shaft 1217, and a base 1219. The X-ray tube 1215 and detector 1212 are mounted at either end of the C-arm gantry 1211, with the centerline connecting them perpendicular to the central axis of rotation 1218. The C-arm gantry 1211 rotates about the central axis of rotation 1218, capturing image data of a patient 1213 on the bed at different projection angles. The X-ray generator 123 controls the current, voltage, and exposure time of the X-ray tube 1215. The projection data collected by the detector 1212 is transmitted to a computer via a communication system 126. The gantry 1211 is connected to the C-arm drive shaft 1216, which is powered by the rotating shaft 1217. The base 1219 is responsible for bearing the weight. The C-arm control unit 121 controls the rotation speed, angle, position, etc. of the gantry 1211. The spindle control unit 122 is connected to the base 1219 and provides power support for the entire C-arm system. The X-ray generator 123 controls the current, voltage and exposure time of the X-ray tube 1215. The data acquisition system 124 coordinates the gantry 1211, the detector 1212 and the X-ray generator 123, and collects the collected data. The bed control system 125 controls the position and movement speed of the bed 1214 to achieve different scanning trajectories for the patient 1213. The communication system 126 connects the C-arm control unit 121, the spindle control unit 122, the X-ray generator 123, the data acquisition system 124 and the bed control system 125, and transmits the collected projection data to the memory of the computer device 2.

[0160] Figures 10A and 10B show the schematic structure of another CT (Computed Tomography) imaging system. This CT imaging system is a diagnostic CT system. Compared to a C-arm CT, its gantry 1211 is annular, with detectors 1212 and X-ray tubes 1215 mounted on the gantry and positioned relatively to each other. A bedplate 1214 moves in and out of the gantry aperture under the control of a bedplate control system 125, which drives the detectors 1212 and X-ray tubes 1215 around the bedplate 1214.

[0161] FIG11 shows a block diagram of an electronic device 10 that can be used to implement embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present application as described and / or claimed herein.

[0162] As shown in Figure 11, the electronic device 10 includes at least one processor 11 and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12 and a random access memory (RAM) 13. The memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0163] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0164] The processor 11 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the energy spectrum image processing method.

[0165] In some embodiments, the energy spectral image processing method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the energy spectral image processing method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the energy spectral image processing method in any other appropriate manner (e.g., via firmware).

[0166] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0167] Computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0168] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. Examples of machine-readable storage media include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs) or flash memories, optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0169] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0170] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0171] A computing system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. The client-server relationship arises through computer programs running on the respective computers and establishing a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within a cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and virtual private server (VPS) services.

[0172] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the embodiments of this application can be achieved, and this document is not limited here.

Claims

1. An energy spectrum image processing method, comprising: determining an initial material basis image that meets the initial quantitative accuracy conditions; during the process of performing target image iterative reconstruction on the initial material basis image, determining a current iteration result corresponding to the current diffusion step, obtaining current noise data corresponding to the current iteration result based on a pre-trained diffusion noise model, and performing noise reduction processing on the current iteration result based on the current noise data to update the current iteration result, wherein the starting diffusion step in the target image iterative reconstruction process is determined based on the inverse diffusion starting point, and the inverse diffusion starting point is determined based on the data acquisition conditions corresponding to the initial material basis image; when the target image iterative reconstruction ends, using the updated current iteration result as the target material basis image.

2. The method according to claim 1, wherein, the obtaining of the current noise data corresponding to the current iteration result based on a pre-trained diffusion noise model includes: inputting the current iteration result and the diffusion step corresponding to the current iteration result into a pre-trained diffusion noise model to obtain current noise data.

3. The method according to claim 1, wherein, the performing of noise reduction processing on the current iteration result based on the current noise data to update the current iteration result includes: determining a noise weighting coefficient corresponding to the current noise data, the noise weighting coefficient being determined based on a diffusion step estimator, and the diffusion step estimator being determined based on set diffusion parameters; performing noise reduction processing on the current iteration result based on the current noise data and the noise weighting coefficient to update the current iteration result.

4. The method according to claim 1, wherein, The loss function for training the diffusion noise model is as follows: Among them, C s,t is the material base image at time t in the s-th group of samples, is the diffusion step estimator, Δ is a random noise sampled by the Monte Carlo method, and this random noise follows a Gaussian distribution with a mean of 0 and a covariance of S, where S is the noise covariance matrix, and the noise covariance matrix is determined based on the data acquisition conditions corresponding to the corresponding samples.

5. The method according to claim 1, wherein, the starting diffusion step in the target image iterative reconstruction process is determined through the following steps: determining the target data acquisition conditions corresponding to the initial material basis image; according to the corresponding relationship between the pre-created data acquisition conditions and the noise covariance matrix, determining the noise covariance matrix corresponding to the target data acquisition conditions; determining an inverse diffusion starting point estimator based on the noise covariance matrix, and determining the starting diffusion step in the target image iterative reconstruction process based on the inverse diffusion starting point estimator.

6. The method according to claim 5, wherein, when determining the noise covariance matrix corresponding to the target data acquisition conditions according to the corresponding relationship between the pre-created data acquisition conditions and the noise covariance matrix, it further includes: determining an inverse diffusion starting point correction amount corresponding to the target data acquisition conditions according to the corresponding relationship between the pre-created data acquisition conditions and the inverse diffusion starting point correction amount; the determining of the starting diffusion step in the target image iterative reconstruction process based on the inverse diffusion starting point estimator includes: Determine the inverse diffusion starting point according to the inverse diffusion starting point estimator and the inverse diffusion starting point correction amount; Determine the starting diffusion step number in the iterative reconstruction process of the target image according to the inverse diffusion starting point.

7. The method according to claim 5, wherein, Determine the noise covariance matrix corresponding to the data acquisition condition through the following steps: For each data acquisition condition in the data acquisition condition catalog, normalize the first basis image and the second basis image corresponding to the current data acquisition condition respectively. The first basis image and the second basis image are two basis images corresponding to the projection data of the calibration phantom, and the two basis images are determined based on image domain decomposition; Select at least two uniform image patches from the first basis image and the second basis image respectively, and the positions of the at least two uniform image patches on the first basis image correspond to the positions of the at least two uniform image patches on the second basis image respectively; Calculate the first noise covariance matrix between two uniform image patches with corresponding relationships on the first basis image and the second basis image to obtain at least two first noise covariance matrices; For each first noise covariance matrix among the at least two first noise covariance matrices, use the ratio of the current first noise covariance matrix to the maximum value of the current first noise covariance matrix as the second noise covariance matrix; Take the mean of all the second noise covariance matrices as the noise covariance matrix corresponding to the current data acquisition condition.

8. The method according to claim 1, wherein, The determination of the initial substance basis image that meets the initial quantitative accuracy condition includes: Determine the substance basis image to be processed corresponding to the projection data based on image domain decomposition; Perform initial image iterative reconstruction on the substance basis image to be processed using a model-based iterative reconstruction algorithm to obtain an initial substance basis image that meets the initial quantitative accuracy condition.

9. An energy spectrum image processing device, comprising: An image determination module configured to determine an initial substance basis image that meets the initial quantitative accuracy condition; A reconstruction denoising module configured to, during the iterative reconstruction of the target image on the initial substance basis image, determine the current iteration result corresponding to the current diffusion step number, obtain the current noise data corresponding to the current iteration result based on a pre-trained diffusion noise model, and perform denoising processing on the current iteration result based on the current noise data to update the current iteration result. Among them, the starting diffusion step number in the iterative reconstruction process of the target image is determined based on the inverse diffusion starting point, and the inverse diffusion starting point is determined based on the data acquisition condition corresponding to the initial substance basis image; A result module configured to, when the iterative reconstruction of the target image ends, use the updated current iteration result as the target substance basis image.

10. An electronic device, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, enables the at least one processor to execute the energy spectrum image processing method according to any one of claims 1-8.

11. A computer-readable storage medium storing computer instructions for implementing the energy spectrum image processing method according to any one of claims 1-8 when executed by a processor.

Images