Spectrum correction method and system of photon counting CT detector
By adaptively weighting and fusing the spectral response model and the pulse stacking model, the correction error problem of photon counting CT detectors in different energy ranges was solved, achieving higher precision spectral correction and improving imaging quality and material identification accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SAINUO WEISHENG SCI & TECH BEIJING
- Filing Date
- 2026-01-16
- Publication Date
- 2026-05-15
AI Technical Summary
Existing spectral correction methods for photon-counting CT detectors fail to effectively consider the coupling effects of pulse stacking and response differences across different energy ranges, resulting in spectral distortion. This leads to significant correction errors, particularly in wide-energy-range detection scenarios, which negatively impacts imaging quality and the accuracy of substance identification.
An adaptive weighted fusion detector spectral response model and pulse stacking model are adopted. By acquiring measured viewing data, adaptive weights are assigned and the models are coupled to achieve synergistic correction of spectral response and pulse stacking effect.
It improves the calibration adaptability across the entire energy range, reduces spectral distortion, enhances imaging quality and the accuracy of substance identification, and provides reliable technical support for clinical diagnosis.
Smart Images
Figure CN122030997A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of medical imaging technology, and in particular to a spectral correction method and system for a photon counting CT detector. Background Technology
[0002] Photon-counting CT, with its precise spectral resolution, possesses unique advantages in medical imaging diagnosis. The accuracy of its detector's spectral response directly determines image quality and the precision of substance identification. However, photon-counting CT detectors are susceptible to pulse stacking effects and differences in response across different energy ranges, leading to spectral distortion, which requires calibration to ensure performance.
[0003] Existing spectral correction methods mostly rely solely on spectral response models or pulse stacking models, failing to consider the coupling effects of these two approaches across different energy ranges. Their fixed weighting leads to poor adaptability and difficulty in achieving correction accuracy across the entire energy range. This is particularly problematic in wide-energy-range detection scenarios, where correction errors are significant, hindering the clinical application of photon-counting CT.
[0004] Therefore, there is an urgent need for an adaptive fusion of multiple models for spectral correction of photon counting CT detectors. Summary of the Invention
[0005] In view of this, the present invention proposes a spectral correction method and system for a photon-counting CT detector, which can accurately correct the spectrum of the photon-counting CT detector.
[0006] To achieve the above objectives, the present invention provides the following technical solution: A spectral correction method for a photon-counting CT detector includes: Acquire measured observation data of the detector at the target energy range; Determine the energy segment type of the target energy segment, and assign adaptive weights to the pre-built detector spectral response model and pulse stacking model based on the energy segment type; The detector spectral response model and the pulse stacking model are fused by the adaptive weights to achieve coupling between the detector spectral response model and the pulse stacking model; The photon counting CT detector is spectrally corrected based on the coupled detector spectral response model and the pulse stacking model.
[0007] Based on the above technical solution, the present invention can be further improved as follows: Optionally, before acquiring the measured viewing data of the detector at the target energy range, the following steps are included: Pre-build the detector's spectral response model; The interaction process between monochromatic X-rays within a preset energy range and the detector crystal is simulated to obtain energy response curves; The energy response curve data were fitted using polynomial fitting to obtain the spectral response functions corresponding to different incident energies.
[0008] Optionally, the step of fitting the energy response curve data with a polynomial to obtain the spectral response function corresponding to different incident energies includes: The detector's response capability to particles of different energies is calculated using formula (1); Formula (1); In the formula, Let E represent the detector's response to particles of different energies, and E be the incident photon energy. The energy detected by the photon counting detector. The amplitude coefficient of the spectral response. The central energy coefficient of the spectral response, The width coefficient of the spectral response. This represents the background noise coefficient.
[0009] Optionally, the pulse stacking model integrates a paralyzable pulse stacking sub-model and a non-paralyzable pulse stacking sub-model.
[0010] Optionally, before acquiring the measured viewing data of the detector at the target energy range, the following steps are included: Pre-build a pulse stacking model; Dead time τ is adaptively configured in segments based on the differences in different energy ranges; Based on the correlation between incident count rate and recording count rate, and combined with the proportional relationship between incident count rate and X-ray tube current, the calculation formulas for the count rate of the paralyzable pulse accumulator model and the non-paralyzable pulse accumulator model are derived.
[0011] Optionally, the formula for calculating the count rate of the paralyzable pulse accumulation sub-model includes: The count rate of the paralyzable pulse stacking sub-model is calculated using formula (2); Formula (2); In the formula, The count rate is the count rate of the paralyzable pulse stacker model, I is the X-ray tube current, and k is the proportionality coefficient. Time for death.
[0012] Optionally, the formula for calculating the count rate of the non-paralyzable pulse accumulation sub-model includes: The count rate of the non-paralyzable pulse stacking sub-model is calculated using formula (3); Formula (3); In the formula, The count rate is for the non-paralyzable pulse stacker model, I is the X-ray tube current, and k is the proportionality coefficient. Time for death.
[0013] A spectral correction system for a photon-counting CT detector includes: The data acquisition module is used to acquire measured viewing data of the detector at the target energy range; The weight allocation module is used to determine the energy segment type of the target energy segment and assign adaptive weights to the pre-built detector spectral response model and pulse stacking model based on the energy segment type. The model coupling module is used to fuse the detector spectral response model and the pulse stacking model through the adaptive weights, thereby achieving coupling between the detector spectral response model and the pulse stacking model; The correction module is used to perform spectral correction on the photon-counting CT detector based on the coupled detector spectral response model and the pulse stacking model.
[0014] An electronic device includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the method described herein.
[0015] A non-transitory computer-readable storage medium having a computer program stored thereon, the computer program implementing the steps of the method when executed by a processor.
[0016] The present invention has the following advantages: The spectral correction method for photon-counting CT detectors in this invention achieves precise coupling between the spectral response model and the pulse stacking model by assigning adaptive weights according to energy band type. This overcomes the shortcomings of existing technologies that rely on fixed weights and separate modeling, which cannot adapt to the characteristics of different energy bands, significantly improving the full-energy-band correction adaptability. The coupled model can simultaneously consider the synergistic effect of two key effects, effectively reducing spectral distortion and improving correction accuracy. The corrected detector has superior spectral resolution, ensuring the imaging quality and material identification accuracy of photon-counting CT, providing reliable technical support for precise clinical diagnosis, and possessing significant engineering application value. Attached Figure Description
[0017] For illustrative purposes and not limiting, the present invention will now be described in conjunction with embodiments and accompanying drawings, wherein: Figure 1 This is a flowchart illustrating the spectral correction method for a photon-counting CT detector according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the data correction process under a certain energy range in an embodiment of the present invention. Figure 3 This is a schematic diagram of the main components of the spectral correction system of the photon counting CT detector in an embodiment of the present invention; Figure 4 This is a schematic diagram of the physical structure of the electronic device provided by the present invention. Detailed Implementation
[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0019] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0020] It should be noted that, where there is no conflict, the embodiments and features of the present invention can be combined with each other. The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0021] Figure 1 This is a flowchart illustrating the spectral correction method for a photon-counting CT detector according to an embodiment of the present invention, as shown below. Figure 1 As shown, the spectral correction method for a photon counting CT detector provided in this embodiment of the invention includes the following steps S101 to S104.
[0022] S101, acquire the measured viewing data of the detector at the target energy level.
[0023] S102, determine the energy segment type of the target energy segment, and assign adaptive weights to the pre-built detector spectral response model and pulse stacking model based on the energy segment type.
[0024] A detector spectral response model was constructed using the GEANT4 Monte Carlo simulation platform to simulate the interaction between monochromatic X-rays in the energy range of 30–180 keV and a CdTe crystal, obtaining energy response curves. The detector spectral response model considered the photoelectric effect, Compton scattering, charge sharing, electron noise, Rayleigh scattering, and characteristic X-rays. A polynomial fitting method was used to fit the model data, obtaining the spectral response functions for different incident energies. In the formula, Let E represent the detector's response to particles of different energies, and E be the incident photon energy. The energy detected by the photon counting detector. The amplitude coefficient of the spectral response. The central energy coefficient of the spectral response, The width coefficient of the spectral response. This represents the background noise coefficient.
[0025] A pulse stacking model is established, integrating paralyzable and non-paralyzable pulse stacking sub-models. Segmented adaptive dead time is implemented based on different energy gain segments. Incident count rate With the record count rate The relationship between them is satisfied, where This indicates the probability that the count will be recorded. In the non-paralyzable model: In the paralysis model: Due to the incident count rate It is proportional to the X-ray tube current I, where k is the proportionality constant: Combining the above formula, dead time It can be obtained through the following formula: Non-paralyzing count rate: Paralyzable count rate: The above parameters k and It can be obtained using the nonlinear least squares method.
[0026] S103 uses adaptive weights to fuse the detector spectral response model and the pulse accumulation model, thereby achieving coupling between the two models.
[0027] Different weights are set for different energy ranges in the model. The coupling of the two models addresses the difference in the dominance of effects across different energy ranges. The allocation rule is as follows: in the low energy range (<30 keV), due to significant electronic noise interference, [the following is omitted as the original text is incomplete and requires further context]. =0.45, mid-energy range (30~120keV), the effects of spectral response and pulse accumulation response on the detector are balanced, and the settings are... =0.75, high energy range (>120 keV), pulse accumulation is the dominant effect, set =0.95, enhancing the pulse accumulation model's correction of count rate deviation.
[0028] S104, based on the coupled detector spectral response model and pulse stacking model, performs spectral correction on the photon counting CT detector.
[0029] Figure 2 This is a flowchart illustrating the coupling process for data correction in a certain energy range according to an embodiment of the present invention, such as... Figure 2 As shown, the calibration table corresponding to the energy range is obtained through multi-step processing. The specific process steps are as follows: Start with "measured projection data (E) under a certain energy range" as the initial input for the entire process.
[0030] Import the projection data E into the "spectral response model". After processing by the model, you will get the "corrected spectral response data R(E,E')".
[0031] The spectral response model further outputs the "Cred matrix of photon counts after correcting the energy response". Based on the rules of the energy range, a "weighting coefficient w" is set, and the Cred matrix is multiplied by w to obtain the "weighted photon count matrix Cw".
[0032] Import the Cw matrix into the "pulse matrix model" and obtain two key parameters through model calculation: "proportional coefficient k" and "dead time t".
[0033] Finally, the "correction table (k,t) under this energy range" is saved. At the same time, this correction table will also form a closed loop and may participate in the iterative / correlation processing of subsequent processes.
[0034] Figure 3 This is a schematic diagram of the main components of the spectral correction system for a photon-counting CT detector according to an embodiment of the present invention. Figure 3 As shown, the spectral correction system 1 for a photon counting CT detector provided in this embodiment of the invention includes a data acquisition module 10, a weight allocation module 20, a model coupling module 30, and a correction module 40.
[0035] The data acquisition module 10 is used to acquire the measured viewing data of the detector under the target energy range; The weight allocation module 20 is used to determine the energy segment type of the target energy segment and to allocate adaptive weights to the pre-built detector spectral response model and pulse stacking model based on the energy segment type. The model coupling module 30 is used to fuse the detector spectral response model and the pulse stacking model through the adaptive weights, thereby achieving coupling between the detector spectral response model and the pulse stacking model. The correction module 40 is used to perform spectral correction on the photon counting CT detector based on the coupled detector spectral response model and the pulse stacking model.
[0036] Figure 4 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as... Figure 4 As shown, the electronic device 50 includes: a processor 501, a memory 502, and a bus 503; The processor 501 and the memory 502 communicate with each other via the bus 503. The processor 501 is used to call program instructions in the memory 502 to execute the methods provided in the above-described method embodiments, and to execute the methods provided in the embodiments of the present invention.
[0037] This embodiment provides a non-transitory computer-readable storage medium that stores computer instructions, which cause a computer to execute the method provided in this embodiment of the invention.
[0038] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various storage media capable of storing program code, such as ROM, RAM, magnetic disk, or optical disk.
[0039] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A spectral correction method for a photon-counting CT detector, characterized in that, include: Acquire measured observation data of the detector at the target energy range; Determine the energy segment type of the target energy segment, and assign adaptive weights to the pre-built detector spectral response model and pulse stacking model based on the energy segment type; The detector spectral response model and the pulse stacking model are fused by the adaptive weights to achieve coupling between the detector spectral response model and the pulse stacking model; The photon counting CT detector is spectrally corrected based on the coupled detector spectral response model and the pulse stacking model.
2. The spectral correction method for a photon-counting CT detector according to claim 1, characterized in that, Before acquiring the measured viewing data of the detector at the target energy range, the following steps are included: Pre-build the detector's spectral response model; The interaction process between monochromatic X-rays within a preset energy range and the detector crystal is simulated to obtain energy response curves; The energy response curve data were fitted using polynomial fitting to obtain the spectral response functions corresponding to different incident energies.
3. The spectral correction method for a photon-counting CT detector according to claim 2, characterized in that, The step of fitting the energy response curve data with a polynomial to obtain the spectral response function corresponding to different incident energies includes: The detector's response capability to particles of different energies is calculated using formula (1); Official (1); In the formula, Let E represent the detector's response to particles of different energies, and E be the incident photon energy. The energy detected by the photon counting detector. The amplitude coefficient of the spectral response. The central energy coefficient of the spectral response, The width coefficient of the spectral response. This represents the background noise coefficient.
4. The spectral correction method for a photon-counting CT detector according to claim 1, characterized in that, The pulse stacking model integrates a paralyzable pulse stacking sub-model and a non-paralyzable pulse stacking model.
5. The spectral correction method for a photon-counting CT detector according to claim 4, characterized in that, Before acquiring the measured viewing data of the detector at the target energy range, the following steps are included: Pre-build a pulse stacking model; Dead time τ is adaptively configured in segments based on the differences in different energy ranges; Based on the correlation between incident count rate and recording count rate, and combined with the proportional relationship between incident count rate and X-ray tube current, the calculation formulas for the count rate of the paralyzable pulse accumulator model and the non-paralyzable pulse accumulator model are derived.
6. The spectral correction method for a photon-counting CT detector according to claim 5, characterized in that, The formula for calculating the count rate of the paralyzable pulse accumulation sub-model includes: The count rate of the paralyzable pulse stacking sub-model is calculated using formula (2); Official (2); In the formula, The count rate is the count rate of the paralyzable pulse stacker model, I is the X-ray tube current, and k is the proportionality coefficient. Time for death.
7. The spectral correction method for a photon-counting CT detector according to claim 6, characterized in that, The formula for calculating the count rate of the non-paralyzable pulse accumulation sub-model includes: The count rate of the non-paralyzable pulse stacking sub-model is calculated using formula (3); Official (3); In the formula, The count rate is for the non-paralyzable pulse stacker model, I is the X-ray tube current, and k is the proportionality coefficient. Time for death.
8. A system for spectral correction of a photon-counting CT detector, characterized in that, include: The data acquisition module is used to acquire measured viewing data of the detector at the target energy range; The weight allocation module is used to determine the energy segment type of the target energy segment and assign adaptive weights to the pre-built detector spectral response model and pulse stacking model based on the energy segment type. The model coupling module is used to fuse the detector spectral response model and the pulse stacking model through the adaptive weights, thereby achieving coupling between the detector spectral response model and the pulse stacking model; The correction module is used to perform spectral correction on the photon-counting CT detector based on the coupled detector spectral response model and the pulse stacking model.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.
10. A non-transitory computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.