A dual-energy imaging method and system based on sparse acquisition optimization

Abstract

The application discloses a kind of dual-energy imaging method and system based on sparse acquisition optimization, belong to X ray imaging technical field.The method includes: the complete projection data of non-sparse in low-energy or high-energy mode is collected as the first circle data, and the sparse angle projection data in high-energy or low-energy mode is collected as the second circle data;Using projection domain base material decomposition algorithm, the base substance projection decomposition result under sparse angle is obtained;The base substance decomposition result under sparse angle is interpolated and completed, the error between the calculated value and the true value of low-energy projection data under non-sparse acquisition is optimized by grid search, to obtain the corrected non-sparse angle base substance decomposition result;The required functional image is output.The application introduces corresponding algorithm process while realizing sparse acquisition, reduces the error in angle completion process, while guaranteeing the advantage of sparse acquisition, realizes the improvement of material decomposition precision.

Description

Technical Field

[0001] This invention belongs to the field of X-ray imaging technology, specifically relating to a dual-energy imaging method and system based on sparse acquisition optimization. Background Technology

[0002] Traditional mixed-energy CT imaging technology faces the unavoidable beam hardening effect, resulting in problems such as metal artifacts and poor CT value accuracy due to the hardening effect. Furthermore, mixed-energy CT cannot identify or quantitatively analyze substances, limiting the information available for clinical diagnosis. Dual-energy CT imaging technology, in principle, eliminates the limitations of beam hardening in mixed-energy imaging. Based on dual-energy imaging algorithms, it can reconstruct the type of matter (atomic number image) and density, providing more information about the tissue being analyzed.

[0003] However, dual-energy CT imaging technology places high demands on system design. Existing techniques include: spectral CBCT imaging based on spectral detectors such as dual-layer flat panel detectors or photon counting detectors. However, the manufacturing process of new spectral detectors is demanding and costly, and due to the size limitations of photon counting detectors, they cannot yet be directly applied to cone-beam CT imaging; spectral CBCT imaging based on rapid kV switching technology. However, this method usually selects a smaller projection angle interval to supplement the high and low energy projection values ​​at the same projection angle. Therefore, it requires an X-ray source capable of rapid kV switching or an increased exposure time. The former brings significant challenges to core device technology and increases costs, while the latter significantly increases the patient dose level; spectral CBCT imaging with a dual-source dual-detector system design. This technology requires two sets of X-ray generators and detectors, which are costly. To prevent interference, scattering, and other effects, it places high demands on system design and correction algorithms.

[0004] Sparse angle imaging can reduce the amount of data, but using it alone can lead to stripe artifacts in the reconstructed image. Existing methods have attempted to supplement the sparse data in the dual-energy acquisition process through interpolation or deep learning, but this introduces errors. The matrix decomposition algorithm is sensitive to projection completeness, and the direct decomposition of sparse data has poor results, which in turn introduces errors into the dual-energy algorithm. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a dual-energy imaging method and system based on sparse acquisition optimization. While achieving sparse acquisition, a corresponding algorithm process is introduced to reduce errors in the angle completion process. This ensures the advantages of sparse acquisition (reduced scanning time or significantly reduced dose) while improving the accuracy of material decomposition.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A dual-energy imaging method based on sparse acquisition optimization, the method comprising:

[0008] Step 1, Scanning Phase: Collect complete non-sparse projection data of one ring under low-energy or high-energy mode as the first ring data, and sparse angular projection data under high-energy or low-energy mode as the second ring data.

[0009] Step 2, Matrix Material Decomposition Stage: Based on the first and second ring data, the projection domain matrix material decomposition algorithm is used to obtain the matrix material projection decomposition results under sparse angles;

[0010] Step 3, Correction Stage: Interpolate and complete the matrix material decomposition results under sparse angles. Optimize the error between the calculated and true values ​​of the low-energy projection data under non-sparse acquisition through grid search to obtain the corrected non-sparse angle matrix material decomposition results.

[0011] Step 4, Output Stage: Based on the corrected non-sparse angle-based material decomposition results, output the required functional image.

[0012] On the other hand, the present invention provides a dual-energy imaging system based on sparse acquisition optimization, comprising:

[0013] The scanning module is used to collect non-sparse complete projection data of one ring as the first ring data in low-energy or high-energy modes, and sparse angular projection data as the second ring data in high-energy or low-energy modes.

[0014] The matrix material decomposition module is used to obtain the matrix material projection decomposition results under sparse angles based on the first and second ring data and the projection domain matrix material decomposition algorithm.

[0015] The correction module is used to interpolate and complete the matrix material decomposition results under sparse angles. It optimizes the error between the calculated and true values ​​of low-energy projection data under non-sparse acquisition by grid search, and obtains the corrected non-sparse angle matrix material decomposition results.

[0016] The output module is used to output the required functional image based on the corrected non-sparse angle-based material decomposition results.

[0017] Thirdly, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned dual-energy imaging method based on sparse acquisition optimization.

[0018] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned dual-energy imaging method based on sparse acquisition optimization.

[0019] The beneficial effects of this invention are as follows:

[0020] Dose and efficiency advantages: Sparse acquisition of high-energy data can effectively and significantly reduce radiation dose while retaining dual-energy decomposition capability.

[0021] Image quality assurance: Low-energy, full-angle data provides structural priors and suppresses sparse reconstruction artifacts.

[0022] Hardware compatibility: Suitable for conventional dual-source CT, fast kVp switching, or photon counting detector systems. Attached Figure Description

[0023] Figure 1 This is a flowchart of a dual-energy imaging method based on sparse acquisition optimization according to the present invention. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] This invention proposes a CT scanning method combining single-energy sparse acquisition and another single-energy complete acquisition. The algorithm optimizes the matrix material decomposition results under the angle of interpolation completion. The optimization method calculates the projection value (also called the calculated projection value) under the non-sparse acquisition energy based on the completed matrix material decomposition results, and expresses the error between the calculated and actual projection values ​​using a formula. By selecting appropriate step sizes for the two error source parameters, a grid search is established to find the parameter combination with the minimum error and update the original interpolated matrix material decomposition results, thereby obtaining the optimized projection domain matrix material decomposition results under non-sparse acquisition. Finally, through reconstruction, metal-free images, images of the substance of interest (e.g., hydroxyapatite), virtual single-energy images, and fused images with CT images are obtained. Specifically, for example... Figure 1 As shown, it includes the following steps:

[0026] Step 1, Scanning Phase: Collect complete non-sparse projection data of one ring under low-energy or high-energy mode as the first ring data, and sparse angular projection data under high-energy or low-energy mode as the second ring data.

[0027] Scan to acquire a set of non-sparse complete projection data (360° uniform sampling) in low-energy (or high-energy) mode; and a set of sparse angular projection data (e.g., sampling once every 30°) in high-energy (or low-energy) mode.

[0028] Both sets of data are non-truncated CT scan data (the scanned object is within the imaging field of view). This can be achieved through dual-circle scanning or single-circle dynamic pulse width modulation.

[0029] For example, dual-circle scanning mode:

[0030] First rotation: 80kV high voltage, 360° continuous rotation, one frame is collected every 0.5°, for a total of 720 frames of low-energy projection data P. L (θ).

[0031] Second cycle: 140kV high voltage, one frame is collected every 4°, for a total of 90 frames of high-energy sparse data P H (θi).

[0032] For example, single-turn pulse width modulation mode:

[0033] Employing a pulse sequence design, the output alternates within a single rotation of the rack: high-energy pulses (140kV, wide pulse width) cover all angles; low-energy pulses (80kV, narrow pulse width) are triggered only at sparse angles (e.g., every 30°).

[0034] For example, single-turn kV switching mode:

[0035] It also employs a pulse sequence design and sets up a high-low kV switching control mode to control the exposure conditions to achieve unequal interval high-low voltage sequences, such as high, high, low, high, high, low, high, high, low...

[0036] Achieve high-energy complete acquisition and low-energy sparse acquisition.

[0037] For example, dual-source dual-probe acquisition mode:

[0038] The system design employs two ray generators paired with two detectors. One generator collects data at a high frequency during one rotation, achieving non-sparse acquisition; the other generator collects data at a low trigger frequency during one rotation, achieving sparse acquisition.

[0039] Step 2, Matrix Material Decomposition Stage: Based on the first and second ring data, a projection domain matrix material decomposition algorithm is used to obtain the matrix material projection decomposition results at sparse angles; including:

[0040] Step 2.1: Extract the first-round data with the same projection angle as the second-round data to obtain a round of high- and low-energy projection data with the same sparse angle; if the angles of the high- and low-energy projection data are not aligned, use the projection data interpolation method under non-sparse acquisition energy to complete the projection data that is completely consistent with the sparse acquisition projection angle.

[0041] Step 2.2: Using the projection domain basis material decomposition algorithm, based on the combination of "material of interest + low-attenuation material", such as "hydroxyapatite + water" or "titanium + water", etc., the bi-energy basis material decomposition is performed; the projection decomposition result of the material of interest Proj_basis1 under the sparse angle and the projection decomposition result of another basis material (low-attenuation material) Proj_basis2 are obtained.

[0042] Step 3, Correction Stage: Interpolate and complete the matrix material decomposition results under sparse angles. Optimize the error between the calculated and true values ​​of the low-energy projection data under non-sparse acquisition through grid search to obtain the corrected non-sparse angle matrix material decomposition results.

[0043] Assume that sparsely collected data is high-energy data, and non-sparsely collected data is low-energy data:

[0044] Step 3.1: Perform interpolation to complete the projection decomposition results Proj_basis1 and Proj_basis2, forming new projection decomposition results new_Proj_basis1 and new_Proj_basis2 under non-sparse angles.

[0045] Step 3.2: Based on known low-energy spectral data And, using the completed new projection decomposition results new_Proj_basis1 and new_Proj_basis2, calculate the low-energy projection data after one rotation under non-sparse acquisition:

[0046] ,

[0047] in, For the projection angle, The coordinates for the projection data collected on the detector. For low-energy spectral data, and The attenuation coefficient values ​​at each monoenergetic level in the energy spectrum of the new projection decomposition results corresponding to the material of interest and the low-attenuation material;

[0048] From the perspective of completion, the actual low-energy projection data obtained. The deviation from the low-energy projection data calculated in the previous step can be expressed as:

[0049] ,

[0050] Deviation of projection data The deviation in decomposition results for different base materials (material of interest and low-attenuation material) caused by angle interpolation and ;

[0051] Step 3.3: Establish a grid search. Centered on the new projection decomposition result, perform a grid search traversal within a certain range (such as ±n steps) to find the optimal parameter combination that minimizes the error between the calculated projection value and the true projection value.

[0052] If the completed new projection decomposition result , If the values ​​are b1 and b2, then iterate through the data and select:

[0053]

[0054]

[0055] in, The value of determines the range of traversal. and This refers to the steps taken by the two base materials when establishing the lookup table in the base material decomposition algorithm. A new series of calculations is obtained by traversing the above b1 and b2. Establish search judgment logic: Obtain b1 and b2 that are closest to the actual collected values;

[0056] Step 3.4: All matrix material decomposition results under interpolated angles are corrected using steps 3.1 and 3.3, finally yielding the corrected matrix material projection decomposition results under non-sparse angle acquisition. and .

[0057] Step 4: Based on the corrected non-sparse angle-based material decomposition results, output the required functional image;

[0058] Based on the corrected non-sparse angle acquisition of the base material decomposition results, the required functional images are output, such as those for... If the substance of interest in the selected base material combination is a metal, adaptive threshold interpolation (such as cubic spline interpolation) can be used to replace the metal region data in the original projection data. Then, a reconstructed CT image filled with the metal region is obtained. If the substance of interest in the selected base material combination is another substance requiring quantitative analysis, such as calcium, then a method can be used to obtain a complete CT image. (Calcium) projection image reconstruction obtains images of the matrix material (calcium). It can also be combined with... Virtual monoenergetic images and fused images of virtual monoenergetic images and CT images are obtained by conventional dual-energy CT processing methods.

[0059] On the other hand, the present invention provides a dual-energy imaging system based on sparse acquisition optimization, wherein each module of the system can implement each step of the aforementioned method, specifically including:

[0060] The scanning module is used to collect non-sparse complete projection data of one ring as the first ring data in low-energy or high-energy modes, and sparse angular projection data as the second ring data in high-energy or low-energy modes.

[0061] The matrix material decomposition module is used to obtain the matrix material projection decomposition results under sparse angles based on the first and second ring data and the projection domain matrix material decomposition algorithm.

[0062] The correction module is used to interpolate and complete the matrix material decomposition results under sparse angles. It optimizes the error between the calculated and true values ​​of low-energy projection data under non-sparse acquisition by grid search, and obtains the corrected non-sparse angle matrix material decomposition results.

[0063] The output module is used to output the required functional image based on the corrected non-sparse angle-based material decomposition results.

[0064] Thirdly, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned dual-energy imaging method based on sparse acquisition optimization.

[0065] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned dual-energy imaging method based on sparse acquisition optimization.

[0066] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dual-energy imaging method based on sparse acquisition optimization, characterized in that, The method includes: Step 1, Scanning Phase: Collect complete non-sparse projection data of one ring under low-energy or high-energy mode as the first ring data, and sparse angular projection data under high-energy or low-energy mode as the second ring data. Step 2, Matrix Material Decomposition Stage: Based on the first and second ring data, the projection domain matrix material decomposition algorithm is used to obtain the matrix material projection decomposition results under sparse angles; Step 3, Correction Stage: Interpolation is performed on the matrix material decomposition results under sparse angles to complete the calculations. The error between the calculated and actual values ​​of the low-energy projection data under non-sparse acquisition is optimized through grid search to obtain the corrected non-sparse angle matrix material decomposition results; including: Step 3.1: Interpolate and complete the projection decomposition results of the material of interest and the low-attenuation material under sparse angles to form new projection decomposition results under non-sparse angles respectively. Step 3.2: Based on the known low-energy spectrum data and the new projection decomposition results, calculate the low-energy projection data after one rotation under non-sparse acquisition, and compare the deviation between the actual acquired low-energy projection data and the calculated low-energy projection data after one rotation under non-sparse acquisition. Step 3.3: Using the new projection decomposition result as the center, perform a grid search traversal within a certain range to find the optimal parameter combination that minimizes the deviation; Step 3.4: Perform steps 3.1-3.3 on all interpolated angle-based matrix material decomposition results to obtain the corrected non-sparse angle-based matrix material decomposition results. Step 4, Output Stage: Based on the corrected non-sparse angle-based material decomposition results, output the required functional image.

2. The dual-energy imaging method based on sparse acquisition optimization according to claim 1, characterized in that, In step 1, both the first and second loop data are untruncated CT scan data, which are achieved through dual-loop scanning or single-loop dynamic pulse width modulation.

3. The dual-energy imaging method based on sparse acquisition optimization according to claim 1, characterized in that, Step 2 includes: Step 2.1: Extract the first-round data under the same projection angle as the second-round data to obtain a round of high- and low-energy projection data under the same sparse angle; Step 2.2: Using the projection domain basis material decomposition algorithm, based on the basis material combination of "material of interest + low-attenuation material", bi-energy basis material decomposition is performed; the projection decomposition results of the material of interest and the projection decomposition results of the low-attenuation material under the sparse angle are obtained.

4. The dual-energy imaging method based on sparse acquisition optimization according to claim 3, characterized in that, In step 2.1, if the angles of the high-energy and low-energy projection data are not aligned, the projection data interpolation method under non-sparse acquisition energy is used to complete the projection data that is completely consistent with the sparse angle projection acquisition.

5. The dual-energy imaging method based on sparse acquisition optimization according to claim 1, characterized in that, In step 3.2, the expression for the deviation is: ， in, For the projection angle, The coordinates for the projection data collected on the detector. For low-energy spectral data, and The attenuation coefficient values ​​at each monoenergetic level in the energy spectrum of the new projection decomposition results corresponding to the material of interest and the low-attenuation material; This represents the actual low-energy projection data acquired. This represents the low-energy projection data that has been rotated once under non-sparse acquisition conditions. This indicates the deviation in the decomposition results of the material of interest caused by angular interpolation. This indicates the deviation in the decomposition results of low-attenuation materials caused by angular interpolation.

6. The dual-energy imaging method based on sparse acquisition optimization according to claim 1, characterized in that, In step 4, the output functional images include CT images after region of interest repair, quantitative images of low-attenuation materials, or fusion images of virtual monoenergetic images and traditional CT.

7. A dual-energy imaging system based on sparse acquisition optimization, applied to the method described in any one of claims 1-6, characterized in that, include: The scanning module is used to collect non-sparse complete projection data of one ring as the first ring data in low-energy or high-energy modes, and sparse angular projection data as the second ring data in high-energy or low-energy modes. The matrix material decomposition module is used to obtain the matrix material projection decomposition results under sparse angles based on the first and second ring data and the projection domain matrix material decomposition algorithm. The correction module is used to interpolate and complete the matrix material decomposition results under sparse angles. It optimizes the error between the calculated and true values ​​of low-energy projection data under non-sparse acquisition by grid search, and obtains the corrected non-sparse angle matrix material decomposition results. The output module is used to output the required functional image based on the corrected non-sparse angle-based material decomposition results.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the dual-energy imaging method based on sparse acquisition optimization as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, enable the processor to implement the dual-energy imaging method based on sparse acquisition optimization as described in any one of claims 1-6.

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