CN111707688B - Self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging and application thereof - Google Patents
Self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging and application thereof Download PDFInfo
- Publication number
- CN111707688B CN111707688B CN202010562558.4A CN202010562558A CN111707688B CN 111707688 B CN111707688 B CN 111707688B CN 202010562558 A CN202010562558 A CN 202010562558A CN 111707688 B CN111707688 B CN 111707688B
- Authority
- CN
- China
- Prior art keywords
- energy spectrum
- energy
- pixel
- imaging
- photon counting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
- G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
- G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
- G01N23/046—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material using tomography, e.g. computed tomography [CT]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
- G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
- G01T1/36—Measuring spectral distribution of X-rays or of nuclear radiation spectrometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
- G01T7/00—Details of radiation-measuring instruments
- G01T7/005—Details of radiation-measuring instruments calibration techniques
Abstract
The invention relates to a self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging and application thereof, wherein the method comprises the following steps: the method comprises the steps of obtaining the inconsistency difference between the energy spectrum of each pixel and a threshold value by utilizing air measurement data under different incident energy spectrum conditions and combining a photon counting energy spectrum CT imaging effective physical model; and establishing a hardening function relation between the multicolor projection value and the monochromatic projection value of the CT system, and performing pixel-by-pixel energy spectrum optimization correction according to the hardening function relation. The method can be used for correcting the energy spectrum CT inconsistency to the maximum extent by combining the CT physical model containing the inconsistency factor and the actual measurement data of the system. According to the system, the energy spectrum can be corrected quickly and accurately through the equipment of the system, the calibration of external equipment such as fluorescence is not relied on, the annular artifact is effectively reduced, the imaging quality is improved, the identification of material components is realized, the system has high practical value in the fields of medical imaging, safety inspection and the like, and powerful support is provided for the practicability of photon counting energy spectrum CT imaging.
Description
Technical Field
The disclosure belongs to the technical field of multi-energy-spectrum X-ray Computed Tomography (CT) imaging, and relates to a self-adaptive energy spectrum optimization method for photon counting CT imaging and a photon counting CT imaging method using the self-adaptive energy spectrum optimization method.
Background
X-ray CT imaging is an important nondestructive testing technique that reflects the internal structure of an object by reconstructing the attenuation coefficient distribution of the object, and is currently widely used in many important fields such as medical examination, industrial examination, and security inspection.
In recent years, the progress and development of photon counting detector technology provides a good hardware basis for multi-energy spectrum CT imaging, and the energy spectrum CT based on the photon counting detector develops rapidly. In the energy spectrum CT, a photon counting detector counts photons in different energy intervals by setting a plurality of threshold values to obtain projections obtained after X-rays with different energy spectrums penetrate through an object, so that image reconstruction and substance resolution are realized.
Compared with the traditional single-energy CT imaging, the energy spectrum CT has higher signal-to-noise ratio, better substance identification capability and lower dosage requirement, and can fundamentally break through the limitation of the existing CT equipment. The core of spectral CT quantitative imaging is material decomposition and accurate reconstruction based on spectral information. Due to the problems of charge sharing, accumulation effect, trap effect, consistency difference and the like in the detector, energy spectrum distortion and energy threshold inconsistency are caused, and a CT image generates ring artifacts and needs energy spectrum correction.
The current energy spectrum correction method needs to be corrected by means of external calibration methods such as fluorescence and the like, has insufficient portability and certain limitation, and urgently needs a new energy spectrum correction method with both accuracy and practicability. How to quickly and accurately realize energy spectrum estimation and correction and optimally utilize energy spectrum information to carry out reconstruction is one of the core technologies of current energy spectrum CT imaging and is a key problem which is urgently needed to be solved in the large-scale practical process.
Disclosure of Invention
Technical problem to be solved
In view of the above, it is an object of the present disclosure to provide an adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging to at least partially solve the above-mentioned technical problems.
Another object of the present disclosure is to provide a photon counting spectral CT imaging method applying the adaptive spectral optimization method to at least partially solve the technical problems set forth above.
(II) technical scheme
In order to achieve the above object, the present disclosure provides a method for adaptive energy spectrum optimization in photon counting energy spectrum CT imaging, the method comprising: acquiring the inconsistency difference between the energy spectrum of each pixel and the threshold value by utilizing air data under different incident energy spectrum conditions and combining with a photon counting energy spectrum CT imaging effective physical model; and establishing a hardening function relation between the multicolor projection value and the monochromatic projection value of the CT system according to the obtained inconsistent difference between the energy spectrum and the threshold value and the linear attenuation coefficient and thickness information of the object, and performing pixel-by-pixel energy spectrum optimization correction according to the hardening function relation.
In the above scheme, the obtaining of the disparity between the energy spectrum of each pixel and the threshold by using air data under different incident energy spectrum conditions in combination with a photon counting energy spectrum CT imaging effective physical model includes: obtaining air data under different incident energy spectrums through actual measurement; establishing a photon counting energy spectrum CT imaging effective physical model containing a threshold value and an energy spectrum related inconsistency factor; and establishing an equation set by utilizing a plurality of groups of air data measured under different incident energy spectrums and combining the CT imaging effective physical model, solving the energy spectrum and threshold value inconsistency difference of each pixel, and establishing an inconsistency parameter lookup table.
In the above scheme, the obtaining of the air data under different incident energy spectrums through actual measurement includes: measuring air data under different incident energy spectrums by adjusting the voltage of the X-ray bulb tube; or equivalent air data under different incident energy spectrums is measured by adding filter plates with different thicknesses.
In the above solution, the effective physical model for photon counting energy spectrum CT imaging including the threshold and the energy spectrum-related inconsistency factor includes: detector energy spectrum and threshold inconsistency factor; detector energy response relationship; an initial incident energy spectrum function; and the attenuation coefficient of the relevant material substance.
In the above scheme, the detector energy spectrum and threshold inconsistency factor includes: expressing the energy spectrum inconsistency factor of the detector by adopting a relational expression of incident energy spectrum difference of each pixel related to incident energy; and representing the detector threshold inconsistency factor as a deviation of the threshold difference of each pixel.
In the scheme, the energy response relation of the detector is obtained by Monte-Ka simulation or empirical model fitting; the initial incident energy spectrum is the energy spectrum of the surface of the incident detector and is obtained by adopting a software simulation or data recovery mode; and the material matter line attenuation coefficient, obtained by NIST standards.
In the above scheme, the performing pixel-by-pixel energy spectrum optimization and correction includes: and establishing a hardening function relation between the multi-energy projection value and the single-energy projection value of the CT system based on the established inconsistency parameter lookup table to obtain a hardening lookup table, and performing pixel-by-pixel energy spectrum optimization correction according to the hardening lookup table.
In the above solution, the establishing a hardening function relationship between the multi-color projection value and the single-color projection value of the CT system includes: selecting an object thickness value within a certain range; selecting equivalent energy; calculating a monochromatic projection value and a multicolor projection value under each object thickness value according to the hardening function relationship; establishing a mapping relation from the multicolor projection value to the single-color projection value, namely a hardening correction lookup table; the mapping relation from the multi-color projection value to the single-color projection value is realized by adopting polynomial fitting.
In the above solution, the energy spectrum optimization and correction at least includes: hardening correction and material decomposition.
In order to achieve the above object, the present disclosure further provides a photon counting energy spectrum CT imaging method, which applies the adaptive energy spectrum optimization method, including:
performing energy spectrum correction by adopting the self-adaptive energy spectrum optimization method, and performing pixel-by-pixel hardening correction on projection data;
and based on the corrected projection data, carrying out image reconstruction by an analytic or iterative method to obtain object tomography.
(III) advantageous effects
According to the technical scheme, the method has the following beneficial effects:
1. the self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging provided by the disclosure aims at physical problems of energy spectrum distortion, threshold value difference and the like among pixels in photon counting energy spectrum CT, and provides a self-adaptive energy spectrum correction solution based on an effective physical model of photon counting energy spectrum CT and combined with theoretical calculation and system experiments.
2. The photon counting energy spectrum CT imaging physical model containing the inconsistency factors and the photon counting energy spectrum CT energy spectrum optimization method based on the model are combined with system actual measurement data fitting solution, and the energy spectrum CT inconsistency can be corrected to the maximum extent.
3. According to the photon counting energy spectrum CT imaging method applying the energy spectrum optimization method, the energy spectrum can be corrected quickly and accurately through the self equipment of the system, the calibration of external equipment such as fluorescence is not relied on, the annular artifacts are effectively reduced, the imaging quality is improved, the identification of material components is realized, the method has high practical value in the fields of medical imaging, safety inspection and the like, and powerful support is provided for the practicability of photon counting energy spectrum CT imaging.
Drawings
Fig. 1 is a flow chart of a method of adaptive energy spectrum optimization in photon counting energy spectrum CT imaging in accordance with an embodiment of the present disclosure.
Fig. 2 is a flowchart of a method of photon counting spectroscopy CT imaging applying the adaptive spectroscopy optimization method according to an embodiment of the present disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
The self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging provided by the disclosure comprises a photon counting energy spectrum CT imaging effective model containing energy spectrum and threshold value related inconsistency factors, inconsistency parameter solving based on the model and system experiments, projection data correction based on inconsistency correction results, an image reconstruction algorithm and the like, and specifically comprises the following steps:
s1: acquiring the inconsistency difference between the energy spectrum of each pixel and the threshold value by utilizing air data under different incident energy spectrum conditions and combining with a photon counting energy spectrum CT imaging effective physical model; and
s2: and establishing a hardening function relation between the multicolor projection value and the monochromatic projection value of the CT system according to the obtained inconsistent difference between the energy spectrum and the threshold value and the linear attenuation coefficient and thickness information of the object, and performing pixel-by-pixel energy spectrum optimization correction according to the hardening function relation.
According to the embodiment of the present disclosure, the obtaining of the energy spectrum and threshold inconsistency difference of each pixel by using air data under different incident energy spectrum conditions in combination with the photon counting energy spectrum CT imaging effective physical model in step S1 includes:
step S11: obtaining air data under different incident energy spectrums through actual measurement;
in the embodiment of the disclosure, the air data under different incident energy spectrums can be measured by adjusting the voltage of the X-ray bulb tube, and equivalent air data under different incident energy spectrums can also be measured by adding filters with different thicknesses;
step S12: establishing a photon counting energy spectrum CT imaging effective physical model containing a threshold value and an energy spectrum related inconsistency factor;
in an embodiment of the present disclosure, a photon counting spectral CT imaging effective physics model containing a threshold and a spectral dependent inconsistency factor comprises: the detector energy spectrum and threshold inconsistency factor, the detector energy response relation, the initial incident energy spectrum function, the attenuation coefficient of related material substances and the like.
In an embodiment of the present disclosure, after establishing an effective physical model for photon counting spectral CT imaging, the effective physical model including a threshold and a spectrum-dependent inconsistency factor, the method further includes: parameterizing the threshold and the energy spectrum related inconsistency factor, namely parameterizing the detector energy spectrum inconsistency factor and the detector threshold inconsistency factor, wherein: and expressing the detector spectrum inconsistency factor by adopting a relational expression of incident energy spectrum difference of each pixel related to incident energy, and expressing the detector threshold inconsistency factor by adopting deviation of threshold difference of each pixel. In other words, the detector spectral disparity parameter may be expressed as a relational expression of incident spectral differences of respective pixels with respect to incident energy, and the detector threshold disparity parameter may be expressed as a deviation of threshold differences of respective pixels.
In the photon counting energy spectrum CT imaging effective physical model containing the threshold value and the energy spectrum related inconsistency factor, the detector energy response relation can be obtained through Monte card simulation or empirical model fitting; the initial incident energy spectrum is the energy spectrum of the surface of the incident detector and can be obtained by adopting a software simulation or data recovery mode; the material matter line attenuation coefficient can be obtained by NIST standard.
Step S13: and (3) establishing an equation set by utilizing a plurality of groups of measured data under different incident energy spectrums and combining the CT imaging effective physical model, solving the inconsistent difference between the energy spectrums of the pixels and the threshold value, and establishing an inconsistent parameter lookup table.
According to the embodiment of the present disclosure, the step S2 of establishing a hardening function relationship between the multi-color projection value and the single-color projection value of the CT system according to the obtained inconsistent difference between the energy spectrum and the threshold value and the line attenuation coefficient and thickness information of the object, and performing the pixel-by-pixel energy spectrum optimization correction according to the hardening function relationship includes: based on the established inconsistency parameter lookup table, establishing a hardening function relation between the multi-energy projection value and the single-energy projection value of the CT system to obtain a hardening lookup table, and performing pixel-by-pixel energy spectrum optimization correction according to the hardening lookup table, wherein the energy spectrum optimization correction comprises but is not limited to hardening correction, material decomposition and the like.
In an embodiment of the present disclosure, the establishing a hardening function relationship between a multi-color projection value and a single-color projection value of a CT system includes: selecting an object thickness value within a certain range; selecting equivalent energy; calculating a monochromatic projection value and a multicolor projection value under each object thickness value according to the hardening function relationship; establishing a mapping relation from the multicolor projection value to the single-color projection value, namely a hardening correction lookup table; the mapping relation from the multi-color projection value to the single-color projection value is realized by adopting polynomial fitting.
As shown in fig. 1, fig. 1 is a flowchart of a method for adaptive energy spectrum optimization in photon counting energy spectrum CT imaging according to an embodiment of the present disclosure, the method includes the following steps:
step 11: and (3) acquiring the inconsistency difference between the energy spectrum of each pixel and the threshold value by utilizing air measurement data under different incident energy spectrum conditions and combining with a photon counting energy spectrum CT imaging effective physical model.
In the embodiment of the disclosure, the photon counting energy spectrum CT imaging effective physical model satisfies the formula:
wherein λ isiRepresenting the detector measurement for each pixel after attenuation by the object, I representing the pixel number, I0As incident value of the detector, EHAnd ELRespectively representing the high and low thresholds of the detector, S0(E) The energy spectrum emitted by the ray source can be simulated by software such as SpekCalc; r (E'; E) is the average energy response function of the ray detector with the incident energy of E and can be obtained by Monte-Ka simulation or empirical model fitting; μ (E, x) represents the object line attenuation coefficient, obtained by NIST standards.
In the embodiment of the disclosure, the inconsistency factor in the photon counting energy spectrum CT imaging effective physical model is parameterized to reflect the difference between pixels;
wherein the energy spectrum inconsistency factor of the detector is gi(E) Indicating that the detector threshold disparity factor is Δ EijWhere j represents the threshold sequence number. Wherein g isi(E) One of a plurality of correlation functions can be selected to represent:
in the embodiment of the disclosure, after parameterizing the inconsistency factor in the CT imaging effective physical model of the photon counting energy spectrum, an equation set is established by using a plurality of groups of measured data under different incident energy spectrums and combining the CT imaging effective physical model, the inconsistency difference between the energy spectrum and the threshold of each pixel is solved, and an inconsistency parameter lookup table is established;
wherein, in order to eliminate the incident quantity I0Influence of, measuring data lambdaiNormalization is required. E.g. measured values lambda at different energy spectratiAir value lambda divided by a specific energy spectrum0iAnd t represents the type of the energy spectrum,
or, measurement of different energy spectra λtiDivided by the sum of the pixel measurements
The above equations can all be expressed as f (L)ki，ΔEiL，ΔEiH)＝0；
Solving: arg min | f (L)ki，ΔEiL，ΔEiH)|
Establishing an equation set through a plurality of groups of different measured values, and jointly fitting to solve the inconsistency parameter delta E of each pixelijAnd gi(E) And obtaining an inconsistency lookup table under each pixel, thereby correcting the spectrum inconsistency in the photon counting energy spectrum CT imaging.
Step 12: according to the obtained inconsistent difference between the energy spectrum and the threshold value, and the linear attenuation coefficient and thickness information of the object, establishing a hardening function relation between the multi-color projection value and the single-color projection value of the CT system, and performing pixel-by-pixel energy spectrum optimization correction according to the hardening function relation;
in an embodiment of the present disclosure, the establishing a hardening function relationship between a multi-color projection value and a single-color projection value of a CT system according to an obtained inconsistent difference between an energy spectrum and a threshold value and line attenuation coefficient and thickness information of an object, and performing pixel-by-pixel energy spectrum optimization and correction according to the hardening function relationship includes: based on the established inconsistency parameter lookup table, establishing a hardening function relation between the multi-energy projection value and the single-energy projection value of the CT system to obtain a hardening lookup table, and performing pixel-by-pixel energy spectrum optimization correction according to the hardening lookup table, wherein the energy spectrum optimization correction comprises but is not limited to hardening correction, material decomposition and the like;
in an embodiment of the present disclosure, the establishing a hardening function relationship between a multi-color projection value and a single-color projection value of a CT system includes: selecting an object thickness value within a certain range; selecting equivalent energy; calculating a monochromatic projection value and a multicolor projection value under each object thickness value according to the hardening function relationship; establishing a mapping relation from the multicolor projection value to the single-color projection value, namely a hardening correction lookup table; the mapping relation from the multi-color projection value to the single-color projection value is realized by adopting polynomial fitting.
Based on the method for adaptive energy spectrum optimization in photon counting energy spectrum CT imaging shown in fig. 1, the present disclosure further applies the method for adaptive energy spectrum optimization to CT imaging, fig. 2 shows a flowchart of a method for photon counting energy spectrum CT imaging applying the method for adaptive energy spectrum optimization according to an embodiment of the present disclosure, the method includes the following steps:
step 21: performing energy spectrum correction by adopting a self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging shown in FIG. 1, and performing pixel-by-pixel hardening correction on projection data;
step 22: and based on the corrected projection data, carrying out image reconstruction by an analytic or iterative method to obtain object tomography.
According to the embodiments, the self-adaptive energy spectrum optimization method in the photon counting energy spectrum CT imaging provided by the disclosure can be used for correcting the energy spectrum CT inconsistency to the maximum extent by combining the CT physical model containing the inconsistency factor and the actual measurement data of the system. The system can be used for quickly and accurately correcting the energy spectrum through the equipment of the system, does not depend on the calibration of external equipment such as fluorescence and the like, effectively reduces the ring-shaped artifact, improves the imaging quality, realizes the identification of material components, has high practical value in the fields of medical imaging, safety inspection and the like, and provides powerful support for the practicability of photon counting energy spectrum CT imaging.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging is characterized by comprising the following steps:
acquiring the inconsistency difference between the energy spectrum of each pixel and the threshold value by utilizing air data under different incident energy spectrum conditions and combining with a photon counting energy spectrum CT imaging effective physical model;
according to the obtained inconsistent difference between the energy spectrum and the threshold value, and the linear attenuation coefficient and thickness information of the object, establishing a hardening function relation between the multi-color projection value and the single-color projection value of the CT system, and performing pixel-by-pixel energy spectrum optimization correction according to the hardening function relation;
the photon counting energy spectrum CT imaging effective physical model meets the formula:
wherein λ isiRepresenting the detector measurement for each pixel after attenuation by the object, I representing the pixel number, I0As incident value of the detector, EHAnd ELRespectively representing the high and low thresholds of the detector, S0(E) An energy spectrum emitted by the ray source; r (E'; E) is the average energy response function of the ray detector with incident energy of E; μ (E, x) denotes the attenuation coefficient of the object line, gi(E) As a factor of spectral inconsistency, Δ E, of the detectorijIs a detector threshold inconsistency factor, where j represents a threshold sequence number;
the method for establishing a hardening function relationship between a CT system multicolor projection value and a monochromatic projection value according to the obtained inconsistent difference between the energy spectrum and the threshold value, and the line attenuation coefficient and thickness information of the object, and performing pixel-by-pixel energy spectrum optimization correction according to the hardening function relationship comprises the following steps: based on the established inconsistency parameter lookup table, establishing a hardening function relation between the multi-energy projection value and the single-energy projection value of the CT system to obtain a hardening lookup table, and performing pixel-by-pixel energy spectrum optimization correction according to the hardening lookup table; the energy spectrum optimization correction at least comprises the following steps: hardening correction and material decomposition.
2. The adaptive energy spectrum optimization method of claim 1, wherein the obtaining of the energy spectrum and threshold inconsistency difference of each pixel by using air data under different incident energy spectrum conditions in combination with a photon counting energy spectrum CT imaging effective physical model comprises:
obtaining air data under different incident energy spectrums through actual measurement;
establishing a photon counting energy spectrum CT imaging effective physical model containing a threshold value and an energy spectrum related inconsistency factor;
and (3) establishing an equation set by utilizing a plurality of groups of measured data under different incident energy spectrums and combining the CT imaging effective physical model, solving the inconsistent difference between the energy spectrums of the pixels and the threshold value, and establishing an inconsistent parameter lookup table.
3. The adaptive spectrum optimization method of claim 2, wherein the obtaining of the air data under different incident energy spectrums through actual measurement comprises:
measuring air data under different incident energy spectrums by adjusting the voltage of the X-ray bulb tube; or
And (3) measuring equivalent air data under different incident energy spectrums by adding filter plates with different thicknesses.
4. The adaptive power spectrum optimization method of claim 1,
the detector spectrum inconsistency factor gi(E) Expressing by using a relational expression of differences of incident energy spectrums of the pixels related to incident energy; and
the detector threshold inconsistency factor Δ EijAnd is represented by the deviation of the threshold difference of each pixel.
5. The adaptive power spectrum optimization method of claim 1,
the average energy response function R (E'; E) of the ray detector with the incident energy of E is obtained through Monte-Ka simulation or empirical model fitting; and
the object line attenuation coefficient μ (E, x), obtained by NIST standards.
6. The adaptive energy spectrum optimization method of claim 1, wherein the establishing a hardening function relationship between the CT system polychromatic projection value and the monochromatic projection value comprises:
selecting an object thickness value within a certain range;
selecting equivalent energy;
calculating a monochromatic projection value and a multicolor projection value under each object thickness value according to the hardening function relationship; and
establishing a mapping relation from a multicolor projection value to a monochromatic projection value, namely a hardening correction lookup table;
the mapping relation from the multi-color projection value to the single-color projection value is realized by adopting polynomial fitting.
7. A photon counting spectral CT imaging method applying the adaptive spectral optimization method of any one of claims 1 to 6, comprising:
performing energy spectrum correction by adopting the self-adaptive energy spectrum optimization method of any one of claims 1 to 6, and performing pixel-by-pixel hardening correction or material decomposition on projection data;
and based on the corrected projection data, carrying out image reconstruction by an analytic or iterative method to obtain object tomography.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010562558.4A CN111707688B (en) | 2020-06-18 | 2020-06-18 | Self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010562558.4A CN111707688B (en) | 2020-06-18 | 2020-06-18 | Self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging and application thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111707688A CN111707688A (en) | 2020-09-25 |
| CN111707688B true CN111707688B (en) | 2021-10-01 |
Family
ID=72542091
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010562558.4A Active CN111707688B (en) | 2020-06-18 | 2020-06-18 | Self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111707688B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112304987B (en) * | 2020-10-19 | 2024-04-12 | 西北工业大学 | Method for measuring equivalent atomic number of energetic material based on photon counting energy spectrum CT |
| CN116978495B (en) * | 2023-07-25 | 2024-03-12 | 上海交通大学 | Thin isotope irradiation production energy spectrum optimization method based on layered target |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101126722A (en) * | 2007-09-30 | 2008-02-20 | 西北工业大学 | Cone-beam CT beam hardening calibration method based on registration model emulation |
| CN103492906A (en) * | 2011-04-21 | 2014-01-01 | 株式会社电视系统 | Calibration device for photon counting radiation detector and calibration method thereof |
| CN105982683A (en) * | 2015-02-15 | 2016-10-05 | 北京纳米维景科技有限公司 | Comprehensive X-ray detector correction method achieving ray hardening influence elimination simultaneously |
| CN107356615A (en) * | 2016-05-10 | 2017-11-17 | 清华大学 | A kind of method and system for dual-energy x-ray CT |
| CN109363703B (en) * | 2018-10-18 | 2020-05-19 | 清华大学 | Method for correcting energy spectrum inconsistency of CT system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9020092B2 (en) * | 2013-02-19 | 2015-04-28 | Kabushiki Kaisha Toshiba | Apparatus and method for angular response calibration of photon-counting detectors in sparse spectral computed tomography imaging |
-
2020
- 2020-06-18 CN CN202010562558.4A patent/CN111707688B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101126722A (en) * | 2007-09-30 | 2008-02-20 | 西北工业大学 | Cone-beam CT beam hardening calibration method based on registration model emulation |
| CN103492906A (en) * | 2011-04-21 | 2014-01-01 | 株式会社电视系统 | Calibration device for photon counting radiation detector and calibration method thereof |
| CN105982683A (en) * | 2015-02-15 | 2016-10-05 | 北京纳米维景科技有限公司 | Comprehensive X-ray detector correction method achieving ray hardening influence elimination simultaneously |
| CN107356615A (en) * | 2016-05-10 | 2017-11-17 | 清华大学 | A kind of method and system for dual-energy x-ray CT |
| CN109363703B (en) * | 2018-10-18 | 2020-05-19 | 清华大学 | Method for correcting energy spectrum inconsistency of CT system |
Non-Patent Citations (6)
| Title |
|---|
| A hybrid Monte Carlo model for the energy response functions of X-ray;Dufan Wu;《Nuclear Instruments and Methods in》;20160911;397-406 * |
| Optimized energy thresholds in a spectral computed tomography scan for contrast agent imaging;Huang Kai Xin;《NUCLEAR SCIENCE AND TECHNIQUES》;20190212;第30卷(第3期);1-13 * |
| Systematic implementation of spectral CT with a photon counting detector;Xiaofei Xu;《Nuclear Inst. and Methods in Physics Research, A》;20180611;99-108 * |
| 光子计数能谱CT重建算法与系统优化研究;吴笃蕃;《中国博士学位论文全文数据库》;20160401;全文 * |
| 基于光子计数探测器的能谱CT的研究;徐品等;《CT理论与应用研究》;20151130;第24卷(第06期);819-825 * |
| 工业 CT 图像的伪影成因和校正方法综述;谷建伟等;《CT理论与应用研究》;20050831;第14卷(第3期);24-28 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111707688A (en) | 2020-09-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10869646B2 (en) | Method and apparatus for computed tomography (CT) and material decomposition with pile-up correction calibrated using real pulse pileup effect and detector response | |
| CN105188547B (en) | Photon counting detector calibration | |
| CN111707688B (en) | Self-adaptive energy spectrum optimization method in photon counting energy spectrum CT imaging and application thereof | |
| McKenney et al. | Experimental validation of a method characterizing bow tie filters in CT scanners using a real‐time dose probe | |
| Desai et al. | Practical evaluation of image quality in computed radiographic (CR) imaging systems | |
| CN110189389B (en) | Deep learning-based decomposition method and device for base material of dual-energy-spectrum CT projection domain | |
| CN103559699A (en) | Multi-energy-spectrum CT image reconstruction method based on projection estimation | |
| CN109363703B (en) | Method for correcting energy spectrum inconsistency of CT system | |
| Zhang et al. | Noise correlation in CBCT projection data and its application for noise reduction in low‐dose CBCT | |
| Gluckman et al. | Comparison of three high‐resolution digitizers for radiochromic film dosimetry | |
| Yao et al. | Multi-energy computed tomography reconstruction using a nonlocal spectral similarity model | |
| Kimoto et al. | Effective atomic number image determination with an energy-resolving photon-counting detector using polychromatic X-ray attenuation by correcting for the beam hardening effect and detector response | |
| CN108051458B (en) | X-ray energy spectrum estimation method based on rational fraction fitting multi-energy projection curve | |
| JP2006026412A (en) | Method for correcting detector signal of unit for reconstructing tomogram from projection data | |
| Atharifard et al. | Per-pixel energy calibration of photon counting detectors | |
| Sossin et al. | Influence of scattering on material quantification using multi-energy x-ray imaging | |
| Di Trapani et al. | Pre-and post-reconstruction digital image processing solutions for computed tomography with spectral photon counting detectors | |
| CN103405241A (en) | Detector afterglow correction method for ray imaging | |
| CN116183647A (en) | Substance identification method | |
| Yu et al. | Heel effect adaptive flat field correction of digital x‐ray detectors | |
| CN112304987B (en) | Method for measuring equivalent atomic number of energetic material based on photon counting energy spectrum CT | |
| Anjomrouz et al. | Beam profile assessment in spectral CT scanners | |
| CN109893148B (en) | Method for calibrating an X-ray measuring device and medical imaging apparatus | |
| WO2019231338A1 (en) | Modelling pileup effect for use in spectral imaging | |
| CN106373100B (en) | A kind of hardening artifact bearing calibration of CT image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |