Dual-energy imaging method and system based on sparse acquisition optimization

Abstract

The invention discloses a dual-energy imaging method and system based on sparse acquisition optimization, and belongs to the technical field of X-ray imaging. The method comprises the steps that non-sparse complete circle of projection data in a low-energy or high-energy mode is collected to serve as first circle data, and sparse angle projection data in the high-energy or low-energy mode is collected to serve as second circle data; adopting a projection domain base material decomposition algorithm to obtain a base material projection decomposition result under the sparse angle; performing interpolation completion on the base material decomposition result under the sparse angle, optimizing an error between a calculated value and a true value of the low-energy projection data under non-sparse acquisition through grid search, and obtaining a corrected non-sparse angle base material decomposition result; and outputting the required functional image. According to the method, a corresponding algorithm process is introduced while sparse acquisition is realized, errors in the whole angle compensation process are reduced, and improvement of substance decomposition precision is realized while sparse acquisition advantages are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of X-ray imaging, and in particular relates to a dual-energy imaging method and system based on sparse acquisition optimization. Background Art

[0002] Conventional mixed-energy CT imaging technology faces the inevitable beam hardening effect, resulting in issues such as metal artifacts and poor CT value accuracy caused by the hardening effect. Furthermore, mixed-energy CT cannot identify or quantitatively analyze materials, providing limited information for clinical diagnosis and treatment. Dual-energy CT imaging technology, however, fundamentally eliminates the beam hardening limitations of mixed-energy imaging. Furthermore, its dual-energy imaging algorithm enables material classification (atomic number images) and density reconstruction, providing more information about the tissue being examined.

[0003] However, dual-energy CT imaging technology has high requirements for system design. The existing technical methods include: energy spectrum CBCT imaging based on energy spectrum detectors such as double-layer flat-panel detectors or photon counting detectors, but the new energy spectrum detectors have high process requirements and high costs, and due to the size limitations of photon counting detectors, they cannot be directly applied to the field of cone-beam CT imaging for the time being; energy spectrum CBCT imaging based on fast kV switching technology, but in order to supplement the high and low energy projection values under the same projection angle, this method usually selects a smaller projection angle interval, so it requires an X-ray source that can quickly switch kV or increase the exposure time. The former will bring great core device technology challenges and increased costs, and the latter will greatly increase the patient dose level; energy spectrum CBCT imaging under the dual-source dual-detector system design, this technology requires two sets of X-ray generators and detectors, which are expensive and have high requirements for system design and correction algorithms to prevent interference, scattering and other influences.

[0004] Sparse angle imaging can reduce the amount of data, but using it alone will cause streak artifacts in the reconstructed image. Existing methods have attempted to complete the sparse data in the dual-energy acquisition process through interpolation or deep learning, but this will introduce errors; the basis material decomposition algorithm is sensitive to the completeness of the projection, and the direct decomposition of sparse data is poor, which in turn brings errors to the dual-energy algorithm. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a dual-energy imaging method and system based on sparse acquisition optimization. While achieving sparse acquisition, the corresponding algorithm process is introduced to reduce the error in the angle completion process. While ensuring the advantages of sparse acquisition (reduced scanning time or significantly reduced dose), the accuracy of material decomposition is improved.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

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

[0008] Step 1, scanning phase: collecting a complete circle of non-sparse projection data in low-energy or high-energy mode as the first circle of data, and sparse angle projection data in high-energy or low-energy mode as the second circle of data;

[0009] Step 2, base material decomposition stage: Based on the first and second circle data, the projection domain base material decomposition algorithm is used to obtain the base material projection decomposition results under sparse angles;

[0010] Step 3, correction stage: interpolate and complete the basis material decomposition results under sparse angles, and optimize the error between the calculated value and the true value of the low-energy projection data under non-sparse acquisition through grid search to obtain the corrected basis material decomposition results under non-sparse angles;

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

[0012] In another aspect, the present invention provides a dual-energy imaging system based on sparse acquisition optimization, comprising:

[0013] A scanning module is used to collect non-sparse complete circle projection data in low energy or high energy mode as the first circle data, and sparse angle projection data in high energy or low energy mode as the second circle data;

[0014] The base material decomposition module is used to obtain the base material projection decomposition result under the sparse angle based on the first circle data and the second circle data by using the projection domain base material decomposition algorithm;

[0015] The correction module is used to interpolate and complete the basis material decomposition results under sparse angles. By optimizing the error between the calculated value and the true value of the low-energy projection data under non-sparse acquisition through grid search, the corrected basis material decomposition results under non-sparse angles are obtained.

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

[0017] In a third aspect, the present invention provides an electronic device comprising: one or more processors; and 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] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned dual-energy imaging method based on sparse acquisition optimization.

[0019] The beneficial effects of the present invention are:

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

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

[0022] Hardware compatibility: Suitable for conventional dual-source CT, fast kVp switching or photon counting detector systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a dual-energy imaging method based on sparse acquisition optimization according to the present invention. DETAILED DESCRIPTION

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

[0025] The present invention proposes a CT scanning method that combines single-energy sparse acquisition with another single-energy complete acquisition, and proposes to optimize the basis material decomposition results under the angle completed by interpolation in the algorithm process. The optimization method is to calculate the projection value (also called projection calculation value) under the non-sparse acquisition energy based on the completed basis material decomposition result, and express the error between it and the real projection value by formula. By selecting appropriate steps for the two error source parameters to establish a grid search, find the parameter combination with the minimum error and update the basis material decomposition result obtained by the original interpolation, so as to obtain the optimized projection domain basis material decomposition result under non-sparse acquisition. Finally, the demetallized image, the image of the substance of interest (such as hydroxyapatite), the virtual single-energy image and the fused image with the CT image are obtained through reconstruction. Specifically, Figure 1 As shown, the following steps are included:

[0026] Step 1, scanning phase: collecting a complete circle of non-sparse projection data in low-energy or high-energy mode as the first circle of data, and sparse angle projection data in high-energy or low-energy mode as the second circle of data;

[0027] The scan acquires a set of non-sparse full-circle projection data (360° uniform sampling) in low-energy (or high-energy) mode, and a set of sparse angle projection data (e.g., sampling 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-loop scanning or single-loop dynamic pulse width modulation.

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

[0030] The first circle: 80kV high voltage, 360° continuous rotation, collecting one frame every 0.5°, 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°, a total of 90 frames of high-energy sparse data P H (θi).

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

[0033] The pulse sequence design is adopted. Within a single rotation of the gantry, the output is alternating. High-energy pulses (140kV, wide pulse width) cover all angles; low-energy pulses (80kV, narrow pulse width) are triggered only at sparse angles (such as every 30°).

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

[0035] It also uses a pulse sequence design and sets a high and low kV switching control method to control the exposure conditions to achieve unequal intervals of high and low voltage sequences, such as high, high, low, high, high, low, high, high, low...

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

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

[0038] The system design adopts two ray generators and two detectors. One set has a high acquisition frequency during one rotation, realizing non-sparse acquisition; the other set has a low acquisition trigger frequency during one rotation, realizing sparse acquisition.

[0039] Step 2, base material decomposition stage: Based on the first and second circle data, the projection domain base material decomposition algorithm is used to obtain the base material projection decomposition results under sparse angles; including:

[0040] Step 2.1. Extract the first circle of data at the same projection angle as the second circle of data to obtain a circle of high-energy and low-energy projection data at the same sparse angle. If the angles of the high-energy and low-energy projection data are not aligned, interpolate the projection data at the non-sparse acquisition energy to complete the projection data with the same angle as the sparse acquisition projection data.

[0041] Step 2.2: Use the projection domain basis material decomposition algorithm to perform dual-energy basis material decomposition based on the basis material combination of "material of interest + low attenuation material", such as "hydroxyapatite + water" or "titanium + water". Obtain the projection decomposition result Proj_basis1 of the material of interest under the sparse angle and the projection decomposition result Proj_basis2 of another basis material (low attenuation material).

[0042] Step 3, correction stage: interpolate and complete the basis material decomposition results under sparse angles, and optimize the error between the calculated value and the true value of the low-energy projection data under non-sparse acquisition through grid search to obtain the corrected basis material decomposition results under non-sparse angles;

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

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

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

[0046] ,

[0047] in, is the projection angle, is the coordinate on the detector where the projection data is collected, is the low energy spectrum data, and The attenuation coefficient values at each single energy in the energy spectrum of the new projection decomposition result corresponding to the material of interest and the low attenuation material;

[0048] Low-energy projection data actually obtained at the completed angle The deviation from the low-energy projection data calculated in the previous step can be expressed as:

[0049] ,

[0050] Deviation of projection data Deviations in the decomposition results of 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 within a certain range (e.g., ±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 new projection decomposition result after completion 、 At this time, the values are b1 and b2, and the traversal takes:

[0053]

[0054]

[0055] in, The value of determines the range of traversal. and It is the step of the two base materials when establishing the lookup table in the base material decomposition algorithm. By traversing the above b1 and b2 calculations, a new series of , establish search judgment logic: , obtain b1 and b2 that are closest to the actual collected values;

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

[0057] Step 4: Output the required functional image according to the corrected non-sparse angle-based material decomposition result;

[0058] According to the base material decomposition results under the corrected non-sparse angle acquisition, the required functional images are output, such as If the material of interest in the selected base material combination is metal, adaptive threshold interpolation (such as cubic spline interpolation) can be used to replace the metal area data in the original projection data. Then reconstruct the CT image after the metal area is filled. If the material of interest in the selected base material combination is other substances that need to be quantitatively analyzed, such as calcium, a complete (Calcium) projection image reconstruction to obtain the basic material (calcium) image. It can also be combined with , virtual monoenergetic images, fusion images of virtual monoenergetic images and CT images, etc. 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 the modules included in the system can implement the steps of the aforementioned method, specifically including:

[0060] A scanning module is used to collect non-sparse complete circle projection data in low energy or high energy mode as the first circle data, and sparse angle projection data in high energy or low energy mode as the second circle data;

[0061] The base material decomposition module is used to obtain the base material projection decomposition result under the sparse angle based on the first circle data and the second circle data by using the projection domain base material decomposition algorithm;

[0062] The correction module is used to interpolate and complete the basis material decomposition results under sparse angles. By optimizing the error between the calculated value and the true value of the low-energy projection data under non-sparse acquisition through grid search, the corrected basis material decomposition results under non-sparse angles are obtained.

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

[0064] In a third aspect, the present invention provides an electronic device comprising: one or more processors; and 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] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned dual-energy imaging method based on sparse acquisition optimization.

[0066] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only 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 in the scope of protection of the present invention.

Claims

1. A dual-energy imaging method based on sparse acquisition optimization, characterized in that: The method comprises: Step 1, scanning phase: collecting a complete circle of non-sparse projection data in low-energy or high-energy mode as the first circle of data, and sparse angle projection data in high-energy or low-energy mode as the second circle of data; Step 2, base material decomposition stage: Based on the first and second circle data, the projection domain base material decomposition algorithm is used to obtain the base material projection decomposition results under sparse angles; Step 3, correction stage: interpolate and complete the basis material decomposition results under sparse angles, and optimize the error between the calculated value and the true value of the low-energy projection data under non-sparse acquisition through grid search to obtain the corrected basis material decomposition results under non-sparse angles; Step 4: Output stage: Output the required functional image based on the corrected non-sparse angle-based material decomposition results.

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

3. The dual-energy imaging method based on sparse acquisition optimization according to claim 1, characterized in that: The step 2 includes: Step 2.1, extract the first circle data at the same projection angle as the second circle data, and obtain a circle of high and low energy projection data at the same sparse angle; Step 2.2: Use the projection domain basis material decomposition algorithm to perform dual-energy basis material decomposition based on the basis material combination of "material of interest + low-attenuation material"; obtain the projection decomposition results of the material of interest and the low-attenuation material at sparse angles.

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, projection data under non-sparse acquisition energy are interpolated to complete the data into 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: The step 3 comprises: Step 3.1, interpolate and complete the projection decomposition results of the material of interest and the low-attenuation material at the sparse angle, respectively forming new projection decomposition results at the non-sparse angle; Step 3.2: Calculate the low-energy projection data for one rotation under non-sparse acquisition based on the known low-energy spectrum data and the new projection decomposition result, and compare the deviation between the actually acquired low-energy projection data and the calculated low-energy projection data for one rotation under non-sparse acquisition; Step 3.3, with the new projection decomposition result as the center, perform a grid search within a certain range to find the optimal parameter combination that minimizes the deviation; Step 3.4: Execute steps 3.1 and 3.3 for the basis material decomposition results at all interpolated and supplemented angles, and finally obtain the corrected non-sparse angle basis material decomposition results.

6. The dual-energy imaging method based on sparse acquisition optimization according to claim 5, characterized in that: In step 3.2, the expression of the deviation is: ， in, is the projection angle, is the coordinate on the detector where the projection data is collected, is the low energy spectrum data, and The attenuation coefficient values at each single energy in the energy spectrum of the new projection decomposition result corresponding to the material of interest and the low attenuation material; represents the low-energy projection data actually acquired, Represents the low-energy projection data of a rotation under non-sparse acquisition, represents the deviation of the decomposition result of the material of interest brought by the angle interpolation, Indicates the deviation of the decomposition result of low attenuation materials brought about by angular interpolation.

7. The dual-energy imaging method based on sparse acquisition optimization according to claim 1, characterized in that: In step 4, the output functional image includes a CT image after restoration of the region of interest, a quantitative image of a low-attenuation material, or a fusion image of a virtual monoenergetic image and a conventional CT image.

8. A dual-energy imaging system based on sparse acquisition optimization, characterized in that: include: A scanning module is used to collect non-sparse complete circle projection data in low energy or high energy mode as the first circle data, and sparse angle projection data in high energy or low energy mode as the second circle data; The base material decomposition module is used to obtain the base material projection decomposition result under the sparse angle based on the first circle data and the second circle data by using the projection domain base material decomposition algorithm; The correction module is used to interpolate and complete the basis material decomposition results under sparse angles. By optimizing the error between the calculated value and the true value of the low-energy projection data under non-sparse acquisition through grid search, the corrected basis material decomposition results under non-sparse angles are obtained. The output module is used to output the required functional image according to the corrected non-sparse angle-based material decomposition result.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; Wherein, 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 to 7.

10. A computer-readable storage medium, characterized in that Executable instructions are stored thereon, and when the instructions are executed by the processor, the processor can implement the dual-energy imaging method based on sparse acquisition optimization as described in any one of claims 1 to 7.

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