Method for realizing energy spectrum CT beam hardening artifact correction based on image restoration

Abstract

The invention discloses a method for realizing energy spectrum CT beam hardening artifact correction based on image restoration. The method comprises the following steps: scanning an object to be measured to obtain original projection data; using an image restoration algorithm to eliminate a high-attenuation material track in the original projection data as input data; re-projecting data through a template image obtained through threshold segmentation and an initial model spectrum, and iteratively updating model spectrum parameters with input data; re-projecting the segmented image of the original projection data using the estimated energy spectrum, re-projecting monochromatic projection data by presetting a single energy spectrum and subtracting the monochromatic projection data from the re-projected multi-energy projection data to obtain an artifact portion for correcting the original projection data; and finally, carrying out filtering back projection reconstruction on the corrected projection data to obtain a corrected CT image. The problem that the correction effect of a beam hardening artifact correction software method on the CT image of the to-be-measured object containing the high-attenuation material is poor is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of X-ray CT imaging, and in particular to a method for correcting energy spectrum CT beam hardening artifacts based on image restoration. Background Art

[0002] Spectral CT technology uses computed tomography (CT) technology to scan the fault plane of the object to be tested with X-rays. After the flat-panel detector receives the X-rays passing through the fault plane of the object, it converts the signal into a digital signal, and finally obtains the absorption value of the X-rays through computer processing. This technology can detect the fault image of the internal structure of the object to be tested without touching or destroying the object. It has been widely used in the medical and industrial fields.

[0003] Normally, the X-rays emitted by the X-ray source have a continuous spectrum. When the multi-energy X-rays pass through the object to be tested, the low-energy X-rays are more easily absorbed by high-attenuation materials, such as iron and other metals, which leads to beam hardening and beam hardening artifacts. Artifacts mainly take the form of cupping artifacts and streak artifacts, which lead to the loss of the object's structural information and seriously reduce the diagnostic value.

[0004] There are an increasing number of methods for beam hardening artifact correction, which can be divided into hardware and software methods.

[0005] The hardware method is to add correction tools to the CT system, such as filters, water bag correction method, etc. However, the hardware method will reduce the signal-to-noise ratio, and the suppression effect is general, and it cannot completely solve the beam hardening problem.

[0006] The software method corrects the projection data or reconstruction data based on the mechanism of beam hardening, and mainly includes polynomial fitting method, iterative correction method, dual energy method, deep learning method, etc. However, the current software methods only have a good correction effect on the objects that have been penetrated. When the object to be measured contains high attenuation materials, the commonly used software methods often have poor effects.

[0007] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0008] In order to overcome the defects of the prior art, a method for correcting beam hardening artifacts in spectral CT based on image restoration is provided to solve the problem that the software method of beam hardening artifact correction has poor correction effect on CT images of objects to be measured containing high attenuation materials.

[0009] To achieve the above object, a method for correcting beam hardening artifacts in spectral CT based on image restoration is provided, comprising the following steps:

[0010] Acquire original projection data of a tomographic plane of an object to be measured containing high attenuation materials;

[0011] Using an image restoration algorithm on the high attenuation material track in the original projection data to eliminate the high attenuation material portion in the original projection data to obtain preprocessed projection data;

[0012] Reconstructing the preprocessed projection data using a reconstruction algorithm to obtain a first reconstructed image;

[0013] Segmenting the first reconstructed image to obtain a template image;

[0014] Using energy spectrum generation software to initialize a set of continuous energy spectra as initial model spectra, and calculating and obtaining multi-energy reprojection with the template image;

[0015] Optimizing the parameters of the initial model spectrum by minimizing the square of the L2 norm of the multi-energy reprojection and the pre-processed projection data through an iterative algorithm to obtain an estimated energy spectrum of the multi-energy reprojection that is closest to the pre-processed projection data;

[0016] Reconstructing the original projection data using the reconstruction algorithm to obtain a second reconstructed image;

[0017] Segmenting the second reconstructed image to obtain a segmented image, and using the estimated energy spectrum and the segmented image to calculate and obtain reprojection data containing high attenuation materials;

[0018] Acquire the mono-energy projection data of the object to be measured, and calculate and obtain the beam hardening artifact part data based on the mono-energy projection data and the re-projection data;

[0019] Based on the original projection data and the beam hardening artifact part data, beam hardening artifact correction data is calculated and obtained;

[0020] The beam hardening artifact correction data is reconstructed using the reconstruction algorithm to obtain a beam hardening artifact correction CT image.

[0021] Furthermore, the image restoration algorithm is an image linear interpolation algorithm.

[0022] Furthermore, the reconstruction algorithm is a filtered back-projection reconstruction algorithm.

[0023] Furthermore, the initial model spectrum and the template image are calculated by a first formula to obtain the multi-energy reprojection, and the first formula is:

[0024]

[0025] Among them, S(E) is the continuous energy spectrum, τ m (E) is the mass attenuation coefficient of the material corresponding to the pixel in the template image, A m (u) is the line integral of the mass density of the corresponding material.

[0026] Furthermore, the square of the L2 norm is the objective function of the iterative algorithm, and the objective function is:

[0027]

[0028] The objective function is optimized using the Downhill Simplex Method.

[0029] The beneficial effect of the present invention is that the method for correcting spectral CT beam hardening artifacts based on image restoration does not require a complicated experimental environment or experimental process, nor does it require a large amount of laboratory data. It can not only accurately estimate the energy spectrum with only one set of CT system projection data, but also use the estimated energy spectrum to correct spectral CT beam hardening artifacts. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0031] Figure 1 The present invention is a flowchart of a method for correcting beam hardening artifacts in spectral CT based on image restoration according to an embodiment of the present invention.

[0032] Figure 2 Schematic diagram of original projection data according to an embodiment of the present invention.

[0033] Figure 3 A set of energy spectrum schematic diagrams generated by using SpekCalc software according to an embodiment of the present invention.

[0034] Figure 4 Schematic diagram of estimated energy spectrum and reference energy spectrum according to an embodiment of the present invention.

[0035] Figure 5a It is the original CT image of the embodiment of the present invention.

[0036] Figure 5b This is a corrected CT image according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The present application is 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 relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0038] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] Reference Figure 1 As shown, the present invention provides a method for correcting beam hardening artifacts of spectral CT based on image restoration, comprising the following steps:

[0040] S1. Acquire original projection data of a tomographic plane of an object to be measured containing high attenuation materials.

[0041] Perform a 360° CT scan on the object to be tested to obtain the original projection data R of the tomographic plane of the object to be tested containing high attenuation materials. a ,like Figure 2 shown.

[0042] Original projection data R a It is a sinusoidal image, and the horizontal axis of the image represents the number of scanning angles of the CT system.

[0043] In this embodiment, a GE Maxiray 125CT system is used to perform CT scanning on the object to be measured under 80Kvp conditions to obtain original projection data.

[0044] In this embodiment, the actual mold body uses organic glass as the base material and iron rods as the embedded material.

[0045] S2. Using an image restoration algorithm on the high attenuation material track in the original projection data to eliminate the high attenuation material portion in the original projection data to obtain preprocessed projection data.

[0046] Preprocessed projection data as input data R of the iterative algorithm u .

[0047] As a preferred implementation, the image restoration algorithm is an image linear interpolation algorithm.

[0048] Specifically, a linear interpolation image restoration algorithm is used to eliminate high attenuation material tracks in the original projection data.

[0049] Specifically, high attenuation materials, such as iron and other metals, appear as highlights in the original projection data, such as Figure 2 Highlight track.

[0050] Specifically, the linear interpolation image restoration algorithm predicts the pixel value of the high attenuation material trajectory through the surrounding known pixels. It is necessary to interpolate in two directions at the same time, that is, first horizontal interpolation and then vertical interpolation.

[0051] S3. Reconstruct the preprocessed projection data using a reconstruction algorithm to obtain a first reconstructed image.

[0052] The reconstruction algorithm is a FBP reconstruction algorithm, and the specific FBP reconstruction algorithm is a filtered back projection reconstruction algorithm.

[0053] For the input data R u The FBP reconstruction algorithm is used to reconstruct the first reconstructed image, and the threshold segmentation technique is used to segment the material by pixel to obtain a template image for the iterative algorithm.

[0054] S4. Segment the first reconstructed image to obtain a template image.

[0055] The first reconstructed image pixels are segmented using a threshold segmentation technique to obtain a template image for an iterative algorithm.

[0056] In this embodiment, the threshold segmentation adopts adaptive threshold segmentation to distinguish the pixel areas corresponding to each material according to the image pixel value.

[0057] S5. Use the spectrum generation software to initialize a set of continuous energy spectra as the initial model spectra, and calculate and obtain multi-energy reprojection with the template image.

[0058] Use the spectrum generation software SpekCalc to initialize a set of continuous energy spectra as the initial model spectrum S es (E) Figure 3 shown.

[0059] In this example, the spectrum generation software SpekCalc initializes a set of energy spectra by setting a voltage value of 80Kvp and different aluminum filter thicknesses, and uses the sum normalization method to obtain the normalized energy spectrum.

[0060] Specifically, sum normalization refers to summing the number of photons corresponding to each energy level of an energy spectrum and dividing the number of photons at each energy level by the sum.

[0061] In this example, the initial model spectrum is obtained by weighting and summing the set of initialized energy spectra. The weighted sum formula used for the initial model spectrum is:

[0062]

[0063] And initialize c1 = 1 to set a set of energy spectrum S i (E) Initialized to the initial model spectrum.

[0064] The initial model spectrum and the template image are calculated by the first formula to obtain the multi-energy reprojection R p , the first formula is:

[0065]

[0066] Among them, S(E) is the continuous energy spectrum, τ m (E) is the mass attenuation coefficient of the material corresponding to the pixel in the template image, A m (u) is the line integral of the mass density of the corresponding material.

[0067] The mass attenuation coefficient and mass density were obtained from the National Institute of Standards and Technology (NIST).

[0068] S6. Optimize the parameters of the initial model spectrum by minimizing the square of the L2 norm of the multi-energy reprojection and the pre-processed projection data through an iterative algorithm to obtain the estimated energy spectrum of the multi-energy reprojection that is closest to the pre-processed projection data.

[0069] Specifically, the multi-energy reprojection R is minimized through an iterative algorithm. p and input data R u The square of the L2 norm is used to optimize the energy spectrum model S es (E) parameter c, and finally obtain the multi-energy reprojection R p Closest to the input data R u The estimated energy spectrum.

[0070] The square of the L2 norm is the objective function of the iterative algorithm, and the objective function is:

[0071]

[0072] The objective function is optimized using the downhill simplex method.

[0073] Specifically, the square of the L2 norm is the sum of the squares of the errors, which represents the predicted value R p and the true value R u The gap between.

[0074] Specifically, the Downhill Simplex Method is an optimization algorithm for unconstrained optimization problems, which finds the minimum value of the objective function through reflection, expansion, contraction and other operations. The Downhill Simplex Method does not rely on the derivative of the objective function, and the implementation process is relatively simple. The Downhill Simplex Method combined with the square objective function of the L2 norm can effectively solve the minimization problem.

[0075] In this example, the estimated energy spectrum obtained by the optimization algorithm uses the MSE evaluation indicator. The estimated energy spectrum uses the MSE evaluation indicator to evaluate the accuracy of the estimated energy spectrum. The MSE calculation formula is:

[0076]

[0077] The estimated energy spectrum, reference energy spectrum and MSE calculated value are as follows Figure 4 shown.

[0078] S7. Reconstruct the original projection data using a reconstruction algorithm to obtain a second reconstructed image.

[0079] The original projection data R a The FBP reconstruction algorithm is used to reconstruct the second reconstructed image.

[0080] S8. Segment the second reconstructed image to obtain a segmented image, and use the estimated energy spectrum and the segmented image to calculate and obtain reprojection data containing high attenuation materials.

[0081] Perform threshold segmentation on the second reconstructed image, and use the estimated energy spectrum and segmented image to calculate the reprojection data R containing high attenuation materials ′ p .

[0082] The first formula is also used to calculate the reprojected data containing high attenuation materials:

[0083]

[0084] Calculated.

[0085] S9. Obtaining mono-energy projection data of the object to be measured, and calculating and obtaining beam hardening artifact data based on the mono-energy projection data and the re-projection data.

[0086] Calculate the single energy projection data R by using the preset single energy constant m The beam hardening artifact data δ is obtained by subtracting the monoenergetic projection data and the reprojection data containing high attenuation materials. BHC .

[0087] The formula used for monoenergetic projection data is:

[0088]

[0089] Calculated, where S and τ are preset constants.

[0090] In this example, the estimated energy spectrum is normalized by summing, so the preset monoenergetic constant is 1, and the mass attenuation coefficient of the corresponding material is the attenuation coefficient value at the energy level of 0.08 MeV.

[0091] S10, calculating and obtaining beam hardening artifact correction data based on the original projection data and the beam hardening artifact partial data.

[0092] Using the original projection data R a Subtract the beam hardening artifact data δ BHC Thus, the beam hardening artifact correction data R is obtained. c .

[0093] S11 . Reconstruct the beam hardening artifact correction data using a reconstruction algorithm to obtain a beam hardening artifact correction CT image.

[0094] In this example, the corrected CT image is compared with the original CT image, such as Figure 5a is the original CT image, Figure 5b is the CT image after correction.

[0095] Specifically, the corrected CT image is obtained by reconstructing the projection data after beam hardening artifact correction using FBP, and the original CT image is obtained by reconstructing the original projection data using FBP.

[0096] The method for correcting spectral CT beam hardening artifacts based on image restoration of the present invention does not require a complicated experimental environment or experimental process, nor does it require a large amount of laboratory data. It can not only accurately estimate the energy spectrum with only one set of CT system projection data, but also use the estimated energy spectrum to correct spectral CT beam hardening artifacts.

[0097] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the present application.

Claims

1. A method for correcting beam hardening artifacts in spectral CT based on image restoration, characterized in that: The following steps are involved: Acquire original projection data of a tomographic plane of an object to be measured containing high attenuation materials; Using an image restoration algorithm on the high attenuation material track in the original projection data to eliminate the high attenuation material portion in the original projection data to obtain preprocessed projection data; Reconstructing the preprocessed projection data using a reconstruction algorithm to obtain a first reconstructed image; Segmenting the first reconstructed image to obtain a template image; Using energy spectrum generation software to initialize a set of continuous energy spectra as initial model spectra, and calculating and obtaining multi-energy reprojection with the template image; Optimizing the parameters of the initial model spectrum by minimizing the square of the L2 norm of the multi-energy reprojection and the pre-processed projection data through an iterative algorithm to obtain an estimated energy spectrum of the multi-energy reprojection that is closest to the pre-processed projection data; Reconstructing the original projection data using the reconstruction algorithm to obtain a second reconstructed image; Segmenting the second reconstructed image to obtain a segmented image, and using the estimated energy spectrum and the segmented image to calculate and obtain reprojection data containing high attenuation materials; Acquire the mono-energy projection data of the object to be measured, and calculate and obtain the beam hardening artifact part data based on the mono-energy projection data and the re-projection data; Based on the original projection data and the beam hardening artifact part data, beam hardening artifact correction data is calculated and obtained; The beam hardening artifact correction data is reconstructed using the reconstruction algorithm to obtain a beam hardening artifact correction CT image.

2. The method for correcting beam hardening artifacts in spectral CT based on image restoration according to claim 1, characterized in that: The image restoration algorithm is an image linear interpolation algorithm.

3. The method for correcting beam hardening artifacts in spectral CT based on image restoration according to claim 1, characterized in that: The reconstruction algorithm is a filtered back-projection reconstruction algorithm.

4. The method for correcting beam hardening artifacts in spectral CT based on image restoration according to claim 1, characterized in that: The initial model spectrum and the template image are calculated by a first formula to obtain the multi-energy reprojection, and the first formula is: Among them, S(E) is the continuous energy spectrum, τ m (E) is the mass attenuation coefficient of the material corresponding to the pixel in the template image, A m (u) is the line integral of the mass density of the corresponding material.

5. The method for correcting beam hardening artifacts of spectral CT based on image restoration according to claim 4, characterized in that: The square of the L2 norm is the objective function of the iterative algorithm, and the objective function is: The objective function is optimized using the Downhill Simplex Method.

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