CN104574416B - A kind of low dosage power spectrum CT image de-noising methods - Google Patents
A kind of low dosage power spectrum CT image de-noising methods Download PDFInfo
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Abstract
A kind of low dosage power spectrum CT image de-noising methods, including：（1）Low energy CT data for projection and high-energy CT data for projection of the imaging object under low dosage ray are obtained, and CT image reconstructions are carried out to low energy CT data for projection and high-energy CT data for projection respectively, low energy CT images are obtainedWith high-energy CT images, whereinHRepresent high energy,LRepresent low energy；（2）According to step（1）In the substratess matter decomposition model that is met of reconstruction data, build the mathematical modeling for power spectrum CT image denoisings；（3）By the use of the full variation of broad sense as regularization priori, with reference to step（2）Obtained mathematical modeling builds the object function for image denoising；（4）To step（3）The object function for power spectrum CT image denoisings of middle structure completes power spectrum CT image denoisings using division Bregman Algorithm for Solving.The substratess matter decomposition model that the present invention is met using high low energy image in power spectrum CT, Momentum profiles CT image informations and substratess matter image information, realize power spectrum CT image denoisings.
Description
Technical field
The present invention relates to a kind of image processing method of medical image, more particularly to a kind of low dosage power spectrum CT image denoisings
Method.
Background technology
With developing rapidly for CT technologies, dual intensity CT scan technology based on power spectrum integrating detector and based on energy resolution
The photon counting Detection Techniques of detector make it that power spectrum CT imagings are realized.Power spectrum CT is the development of following CT imaging techniques
One of direction, because power spectrum CT can not only obtain the information of material attenuated inside coefficient, used can also rebuild and obtain material
The information of composition.Power spectrum CT can be gone to from traditional form diagnosis in function assessment diagnosis, such as, and it can be found that conventional CT hairs
Existing focus not, it is possible to achieve the extreme early of tumour is detected, and can accomplish etiologic diagnosis and the quantitative analysis of tumour.Separately
Outside, power spectrum CT can solve many defects that conventional CT imagings are present, and such as remove beam hardening and metal artifacts.
Power spectrum CT imagings under the conditions of low dosage are only possible to clinically realize application, so needing to find efficient low dose
Measure imaging method.The existing method for realizing the imaging of low dosage power spectrum CT images mainly has two classes.Wherein in data acquisition
In reduce tube current as far as possible（mA）And tube voltage（kV）It is a kind of simplest method.The reduction of tube current can cause power spectrum
Photon noise intensity is increased considerably in data for projection and the influence of electronic noise is more prominent；Changing tube voltage can influence X to penetrate
Line is to the penetrability of tissue, so as to influence the picture quality of various tissues.Another is, using statistics method for reconstructing, to utilize
Its physical model is accurate, to insensitive for noise the advantages of, image can be reconstructed under irregular sampling and data deletion condition, changed
The noise of kind final image, improves the spatial resolution of reconstruction image.Because power spectrum CT data for projection amounts are huge, this method is deposited
Too big in amount of calculation, reconstruction time is very long, it is difficult to meet the requirement of real-time, interactive in clinic.
Therefore, in view of the shortcomings of the prior art, providing a kind of low dosage power spectrum CT image de-noising methods, it is possible to increase substratess matter
Density measure accuracy, it is possible to achieve the photo-quality imaging of power spectrum CT images under low-dose scanning agreement.
The content of the invention
A kind of low dosage power spectrum CT image denoisings are provided it is an object of the invention to avoid the deficiencies in the prior art part
Method, can improve the picture quality of substratess matter density image, can realize the excellent of power spectrum CT images under low-dose scanning agreement
Matter is imaged.
The above-mentioned purpose of the present invention is realized by following technological means.
A kind of low dosage power spectrum CT image de-noising methods are provided, comprised the following steps,
（1）Low energy CT data for projection and high-energy CT data for projection of the imaging object under low dosage ray are obtained, and
CT image reconstructions are carried out to low energy CT data for projection and high-energy CT data for projection respectively, low energy CT images are obtainedWith
High-energy CT images, whereinHRepresent high energy,LRepresent low energy；
（2）According to step（1）In the substratess matter decomposition model that is met of reconstruction data, build and gone for power spectrum CT images
The mathematical modeling made an uproar；
（3）By the use of the full variation of broad sense as regularization priori, with reference to step（2）Obtained mathematical modeling is built for image
The object function of denoising；
（4）To step（3）The object function for power spectrum CT image denoisings of middle structure is asked using division Bregman algorithms
Solution, completes power spectrum CT image denoisings.
It is preferred that, above-mentioned steps（2）In substratess matter decomposition model be：
Mass absorption function of the material to X-rayIt is the mass absorption letter that substratess are verified by any two material
Count to represent：, whereinWithIt is the mass absorption letter of two materials respectively
Number,It is the density of required substratess matter respectively, andValue it is unrelated with the energy of X-ray；
According to substratess matter decomposition model, for step（1）Power spectrum CT high-energy CT data for projection and low energy CT projection
Data, the expression formula of the mass absorption function of corresponding material is：,
Define material mass absorption function matrix, substratess matter mass absorption Jacobian matrix,
Substratess matter density matrix；
CCalculated and directly obtained by inverse matrix, formula is, define substratess matter matter
Amount absorbs matrixAInverse matrix form。
It is preferred that, above-mentioned steps（3）In it is specific using using the full variation of second order broad sense as priori, the full variation of second order broad sense
Definition is：
；
WhereinFor non-negative weight coefficient；The auxiliary parameter introduced for the full variation of broad sense, and takeSymmetric gradient operator is represented, whereinRepresent gradient operator,Representing matrix transposition computing；
The step（3）The object function for image denoising of middle structureSpecially：, wherein X represents to obtain after denoising
Power spectrum CT images, Y is the obtained power spectrum CT view data of measurement,WithIt is regularization parameter, becomes entirely for portraying broad sense
Divide regularization intensity.
It is preferred that, above-mentioned steps（4）The specific calculating process of middle division Bregman algorithms is：
Introduce formula A, formula B and formula C and be iterated solution,
, wherein
It is the vector value of an introducing,Represent residual error,nRepresent iterative steps；
Specific iterative process is carried out in accordance with the following steps：
（4.1）Ordern=0,
（4.2）According to formulaAWithB,Solved by primal dual algorithm；
（4.3）By step（4.1）ObtainSubstitute into formulaCSolve；
（4.4）Judge whether iteration ends
Judge n whether be equal to N, if n be equal to N, iteration ends, using current results as denoising after power spectrum CT figure
Picture；
If n is less than N, into step（4.5）；
（4.5）Ordern=n+ 1, return to step（4.2）.
It is preferred that, above-mentioned steps（1）Registration process step is additionally provided with, is specifically：
Low energy CT data for projection and high-energy CT data for projection obtained by judging are offset with the presence or absence of position, work as presence
Low energy CT data for projection and high-energy CT data for projection are carried out by registration process using the method for Registration of Measuring Data when position is offset.
The low dosage power spectrum CT image de-noising methods of the present invention, comprise the following steps,（1）Imaging object is obtained in low dosage
Low energy CT data for projection and high-energy CT data for projection under ray, and respectively to low energy CT data for projection and high-energy CT
Data for projection carries out CT image reconstructions, obtains low energy CT imagesWith high-energy CT images, whereinHRepresent high energy,L
Represent low energy；（2）According to step（1）In the substratess matter decomposition model that is met of reconstruction data, build for power spectrum CT images
The mathematical modeling of denoising；（3）By the use of the full variation of broad sense as regularization priori, with reference to step（2）Obtained mathematical modeling is built
Object function for image denoising；（4）To step（3）The object function for power spectrum CT image denoisings of middle structure, which is used, to be divided
Bregman Algorithm for Solving is split, power spectrum CT image denoisings are completed.The substratess that the present invention is met using high low energy image in power spectrum CT
Matter decomposition model, Momentum profiles CT image informations and substratess matter image information, realize power spectrum CT image denoisings.The present invention can be with
While transmitting using low dosage, the high-quality power spectrum CT denoising images of generation are still ensured that, the inventive method has good
Robustness, eliminates in noise and artifact suppresses two aspects and has good effect.
Brief description of the drawings
Using accompanying drawing, the present invention is further illustrated, but the content in accompanying drawing does not constitute any limit to the present invention
System.
Fig. 1 is the schematic flow sheet of low dosage power spectrum CT image de-noising methods of the present invention.
Fig. 2 is image schematic diagram of the ideal body mould without artifact and noise；Wherein, Fig. 2 (a) is preferable Clock bodies mould
Image schematic diagram without any artifact and noise under 80kVp；Fig. 2 (b) be preferable Clock bodies mould under 140kVp without any
The image schematic diagram of artifact and noise.
Fig. 3 be Clock body moulds low dosage data using FBP algorithms directly reconstruct after image schematic diagram；Wherein, Fig. 3
(a) the image schematic diagram after to be low dosage data of the Clock bodies mould under 80kVp directly reconstructed using FBP algorithms；Fig. 3 (b) points
It is not image schematic diagram of the Clock bodies mould after 140kVp low dosages data are directly reconstructed using FBP algorithms.
Fig. 4 is the image schematic diagram that preferable Clock bodies mould low dosage data are obtained using denoising method of the present invention；Wherein,
Fig. 4 (a) is the image that low dosage data of the Clock bodies mould under 80kVp are obtained using the denoising method of the present invention, and Fig. 4 (b) is
The image that low dosage data of the Clock bodies mould under 140kVp are obtained using the denoising method of the present invention.
Fig. 5 is that the water base figure that preferable Clock bodies mould is obtained based on image domain decomposition method decomposition method and bone base diagram are intended to；Its
In, Fig. 5 (a) is that water base diagram is intended to, and Fig. 5 (b) is bone base diagram intention.
Fig. 6 is that the water base figure that low dosage Clock bodies mould is obtained based on image domain decomposition method decomposition method and bone base diagram are intended to；
Wherein, Fig. 6 (a) is that water base diagram is intended to, and Fig. 6 (b) is bone base diagram intention.
Fig. 7 is to use denoising method of the present invention to obtain the water base figure and bone base figure obtained after result based on image domain decomposition method
Schematic diagram；Wherein, Fig. 7 (a) is that water base diagram is intended to, and Fig. 7 (b) is bone base diagram intention.
Fig. 8 is image section horizontal central line profile, and wherein Fig. 8 (a) is 80kVp image section horizontal central line profiles,
Fig. 8 (b) is 140kVp image section horizontal central line profiles.
Fig. 9 is water base figure and bone base figure part of horizontal center line profile, and wherein Fig. 9 (a) is water base figure part of horizontal center line
Profile, Fig. 9 (b) is bone base figure part of horizontal center line profile.
Embodiment
The invention will be further described with the following Examples.
Embodiment 1.
A kind of low dosage power spectrum CT image de-noising methods, as shown in figure 1, comprise the following steps,
（1）Low energy CT data for projection and high-energy CT data for projection of the imaging object under low dosage ray are obtained, and
CT image reconstructions are carried out to low energy CT data for projection and high-energy CT data for projection respectively, low energy CT images are obtainedWith
High-energy CT images, whereinHRepresent high energy,LRepresent low energy；
（2）According to step（1）In the substratess matter decomposition model that is met of reconstruction data, build and gone for power spectrum CT images
The mathematical modeling made an uproar；
（3）By the use of the full variation of broad sense as regularization priori, with reference to step（2）Obtained mathematical modeling is built for image
The object function of denoising；
（4）To step（3）The object function for power spectrum CT image denoisings of middle structure is asked using division Bregman algorithms
Solution, completes power spectrum CT image denoisings.
It is preferred that, above-mentioned steps（1）Registration process step is additionally provided with, is specifically：Low energy CT obtained by judging is thrown
Shadow data and high-energy CT data for projection are offset with the presence or absence of position, will using the method for Registration of Measuring Data when existence position is offset
Low energy CT data for projection and high-energy CT data for projection carry out registration process.
Wherein, step（2）In substratess matter decomposition model be：
Mass absorption function of the material to X-rayIt is the mass absorption letter that substratess are verified by any two material
Count to represent：, whereinWithIt is the quality suction of two materials respectively
Receive function,It is the density of required substratess matter respectively, andValue it is unrelated with the energy of X-ray；
According to substratess matter decomposition model, for step（1）Power spectrum CT high-energy CT data for projection and low energy CT projection
Data, the expression formula of the mass absorption function of corresponding material is：,
Define material mass absorption function matrix, substratess matter mass absorption Jacobian matrix,
Substratess matter density matrix；
CCalculated and directly obtained by inverse matrix, formula is, define substratess
Matter mass absorption matrixAInverse matrix form。
Wherein, step（3）In specific using using the full variation of second order broad sense as priori, the full variation definition of second order broad sense
For：
；
WhereinFor non-negative weight coefficient；The auxiliary parameter introduced for the full variation of broad sense, and takeSymmetric gradient operator is represented, whereinRepresent gradient operator,Representing matrix transposition computing.
Step（3）The object function for image denoising of middle structureSpecially：, wherein X represents to obtain after denoising
Power spectrum CT images, Y is the obtained power spectrum CT view data of measurement,WithIt is regularization parameter, becomes entirely for portraying broad sense
Divide regularization intensity.
Step（4）The specific calculating process of middle division Bregman algorithms is：
Introduce formula A, formula B and formula C and be iterated solution,
, wherein
It is the vector value of an introducing,Represent residual error,nRepresent iterative steps.
Specific iterative process is carried out in accordance with the following steps：
（4.1）Ordern=0,
（4.2）According to formulaAWithB,Solved by primal dual algorithm；
（4.3）By step（4.1）ObtainSubstitute into formulaCSolve；
（4.4）Judge whether iteration ends
Judge n whether be equal to N, if n be equal to N, iteration ends, using current results as denoising after power spectrum CT figure
Picture；
If n is less than N, into step（4.5）；
（4.5）Ordern=n+ 1, return to step（4.2）.
The substratess matter decomposition model that the present invention is met using high low energy image in power spectrum CT, Momentum profiles CT image informations
With substratess matter image information, power spectrum CT image denoisings are realized.While the present invention can use the low dosage to launch, still ensure that
High-quality power spectrum CT denoising images are produced, the inventive method has good robustness, eliminated in noise and artifact suppresses two
Aspect has good effect.
Embodiment 2.
The specific implementation process of the method for the invention is described with the Voxel Phantom data instance of Computer Simulation, is such as schemed
Shown in 1, the implementation process of the present embodiment is as follows.
（1）The checking that generation low dosage power spectrum CT data for projection carries out inventive algorithm is simulated using Clock Voxel Phantoms
Assess.In the present embodiment, the distance of simulation CT machines x-ray source to pivot and detector is respectively：570.00mm and
1040.00mm, the number of detection member is 672, and size is 1.407mm, and the angular number of samples of the detection rotated a circle is 1160.
Clock phantom images size is 512 × 512.By CT system emulate respectively generation size for 1160 × 672 80kVp and
140kVp data for projection.The variance of system electronic noise is 10.0.
（2）Data reconstruction：Detection data correction is carried out using the systematic parameter of acquisition, logarithmic transformation is carried out, and filtered
Ripple backprojection reconstruction.
（3）Build image denoising model：To step（2）The substratess matter that power spectrum CT view data after obtained reconstruction is met
Decomposition model carries out mathematical modeling, completes the design of the priori of the full variation of broad sense, constructs for power spectrum CT image denoisings
The object function of belt restraining,, wherein
X represents the power spectrum CT images obtained after denoising, and Y is the power spectrum CT view data that measurement is obtained,WithIt is regularization parameter,
In embodiments of the present invention,,, for portraying the full variational regularization intensity of broad sense.
Substratess matter decomposition model concrete form is：
Mass absorption function of the material to X-rayIt is the mass absorption letter that substratess are verified by any two material
Count to represent：, whereinWithIt is the quality suction of two materials respectively
Receive function,It is the density of required substratess matter respectively, andValue it is unrelated with the energy of X-ray；
According to substratess matter decomposition model, for step（1）Power spectrum CT high-energy CT data for projection and low energy CT projection
Data, the expression formula of the mass absorption function of corresponding material is：,
Define material mass absorption function matrix, substratess matter mass absorption Jacobian matrix,
Substratess matter density matrix；
CCalculated and directly obtained by inverse matrix, formula is, define substratess
Matter mass absorption matrixAInverse matrix form。
The detailed process that the full variational regularization priori of above-mentioned broad sense is built is：Elder generation is used as using the full variation of second order broad sense
Test, its definition is：；Wherein、For non-negative plus
Weight coefficient；The full variation of broad sense introduces auxiliary parameter, and take。
（3）Complete denoising：In step（3）On the basis of the correlation model of structure, image is carried out using division Bregman algorithms
Denoising, specific calculating process is：
Introduce formula A, formula B and formula C and be iterated solution,
, wherein
It is the vector value of an introducing,Represent residual error,nRepresent iterative steps.
Specific iterative process is carried out in accordance with the following steps：
（4.1）Ordern=0,
（4.2）According to formulaAWithB,Solved by primal dual algorithm；
（4.3）By step（4.1）ObtainSubstitute into formulaCSolve；
（4.4）Judge whether iteration ends
Judge n whether be equal to N, if n be equal to N, iteration ends, using current results as denoising after power spectrum CT figure
Picture；
If n is less than N, into step（4.5）；
（4.5）Ordern=n+ 1, return to step（4.2）.
In order to verify the effect of method for reconstructing of the present invention, the result of the present embodiment is shown as shown in Fig. 2-Fig. 7, wherein：Fig. 2
（a）And Fig. 2（b）It is preferable Clock bodies mould image without any artifact and noise under 80kVp and 140kVp respectively.Fig. 3
（a）And Fig. 3（b）It is that Clock bodies mould is obtained after 80kVp and 140kVp low dosages data are directly reconstructed using FBP algorithms respectively
Image, it can be seen that because the reduction of dosage causes reconstruction image serious statistical noise occur.Fig. 4（a）And Fig. 4（b）Point
It is not that preferable Clock bodies mould rebuilds obtained water base figure and bone base figure based on projection domain decomposition method.Fig. 5（a）With Fig. 5 (b) respectively
It is that low dosage Clock bodies mould rebuilds obtained water base figure and bone base figure, equally, original high low energy image based on projection domain decomposition method
Present in noise result in the density image of substratess matter that there is also serious noise.Fig. 6 (a) and Fig. 6 (b) are adopted respectively
The water base figure and bone base figure obtained with method for reconstructing of the present invention, utilizes present invention side it can be seen from Fig. 6 two width reconstruction images
Method rebuilds the result obtained and acts on obvious in terms of noise and artifact is suppressed.
Fig. 7（a）With 7（b）In depict corresponding to substratess matter reconstruction image horizontal central line profile in Fig. 4, Fig. 5 and Fig. 6,
In view of containing 512 pixels in the entire profile figure, all display is then difficult to differentiate between each method, therefore only intercepts it when only showing
In one section, for water base figure, its interval is [189,320].For bone base figure, its interval is [147,189].It can be seen by Fig. 7
Go out, in water base figure and in bone base figure, no matter background area or target area, the inventive method reconstructed value is closer to ideal
Value.
The substratess matter decomposition model that the present invention is met using high low energy image in power spectrum CT, Momentum profiles CT image informations
With substratess matter image information, power spectrum CT image denoisings are realized.While the present invention can use the low dosage to launch, still ensure that
High-quality power spectrum CT denoising images are produced, the inventive method has good robustness, eliminated in noise and artifact suppresses two
Aspect has excellent performance.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention rather than the present invention is protected
The limitation of scope, although being explained in detail with reference to preferred embodiment to the present invention, one of ordinary skill in the art should manage
Solution, technical scheme can be modified or equivalent substitution, without departing from technical solution of the present invention essence and
Scope.
Claims (1)
1. a kind of low dosage power spectrum CT image de-noising methods, it is characterised in that：Comprise the following steps,
(1) low energy CT data for projection and high-energy CT data for projection of the imaging object under low dosage ray are obtained, and respectively
CT image reconstructions are carried out to low energy CT data for projection and high-energy CT data for projection, low energy CT images μ is obtainedLAnd high-energy
CT images μH, wherein H represents high energy, and L represents low energy；
(2) the substratess matter decomposition model that the reconstruction data in step (1) are met, builds for power spectrum CT image denoisings
Mathematical modeling；
(3) by the use of the full variation of broad sense as regularization priori, built with reference to the mathematical modeling that step (2) is obtained for power spectrum CT figures
As the object function of denoising；
(4) division Bregman Algorithm for Solving is used to the object function for power spectrum CT image denoisings built in step (3),
Complete power spectrum CT image denoisings；
Substratess matter decomposition model in the step (2) is：
Material is the mass absorption function verified of substratess come table by any two material to the mass absorption function mu (E) of X-ray
Show：μ (E)=c1μ1(E)+c2μ2(E), wherein μ1And μ (E)2(E) be respectively two materials mass absorption function, c1And c2It is institute
The corresponding density of substratess confrontation needed, and c1、c2Value it is unrelated with the energy of X-ray；
According to substratess matter decomposition model, for step (1) power spectrum CT high-energy CT data for projection and low energy CT data for projection,
The expression formula of the mass absorption function of corresponding material is：
Define material mass absorption function matrixSubstratess matter mass absorption Jacobian matrixBase
Material density matrix
C is calculated by inverse matrix and directly obtained, and formula isDefine substratess matter mass absorption
The inverse matrix form of matrix A
Specific using the full variation of second order broad sense is used as priori in the step (3), the full variation definition of second order broad sense is：
Wherein α0、α1For non-negative weight coefficient；V is the auxiliary parameter that the full variation of broad sense introduces, and is taken
Symmetric gradient operator is represented, whereinRepresent gradient operator, T representing matrix transposition computings；
The object function Φ (X) for power spectrum CT image denoisings of structure is specially in the step (3)：Wherein X represents the power spectrum CT figures obtained after denoising
Picture, the power spectrum CT view data that Y obtains for measurement, β1It is regularization parameter, for portraying the full variational regularization intensity of broad sense；
The specific calculating process of division Bregman algorithms is in the step (4)：
Introduce formula A, formula B and formula C and be iterated solution,
A：
B：
C：Wherein
d1And d2It is the vector value of an introducing, v1And v2Residual error is represented, n represents iterative steps；
Specific iterative process is carried out in accordance with the following steps：
(4.1) n=0 is made,
(4.2) according to formula A and B, solved by primal dual algorithmWith
(4.3) step (4.2) is obtainedWithSubstitute into formula C and solve Xn+1；
(4.4) iteration ends are judged whether
Judge n whether be equal to N, if n be equal to N, iteration ends, using current results as denoising after power spectrum CT images；
If n is less than N, into step (4.5)；
(4.5) n=n+1, return to step (4.2) are made；
The step (1) is additionally provided with registration process step, is specifically：
Low energy CT data for projection and high-energy CT data for projection obtained by judging are offset with the presence or absence of position, work as existence position
Low energy CT data for projection and high-energy CT data for projection are carried out by registration process using the method for Registration of Measuring Data during skew.
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| DE102016221684A1 (en) * | 2015-11-27 | 2017-06-01 | Siemens Healthcare Gmbh | A method and image data processing device for processing a multi-energy computed tomography image data set |
| CN108475419B (en) * | 2015-12-15 | 2022-04-26 | 皇家飞利浦有限公司 | Method for data processing for computed tomography |
| CN107356615B (en) * | 2016-05-10 | 2020-01-21 | 清华大学 | Method and system for dual-energy X-ray CT |
| CN106529552B (en) * | 2016-11-03 | 2019-11-08 | 中国海洋大学 | A kind of segmentation and recognition methods of scallop shell growth lines |
| CN108596861A (en) * | 2018-05-14 | 2018-09-28 | 南方医科大学 | A kind of CT image metal artifact minimizing technologies based on the study of depth residual error |
| CN109035169B (en) * | 2018-07-19 | 2020-06-12 | 西安交通大学 | Unsupervised/semi-supervised CT image reconstruction depth network training method |
| CN109448071B (en) * | 2018-11-06 | 2023-05-16 | 深圳安科高技术股份有限公司 | Energy spectrum image reconstruction method and system |
| CN109741411B (en) * | 2018-12-13 | 2023-04-28 | 深圳先进技术研究院 | Low-dose PET image reconstruction method, device, equipment and medium based on gradient domain |
| CN109949215B (en) * | 2019-03-29 | 2023-03-31 | 浙江明峰智能医疗科技有限公司 | Low-dose CT image simulation method |
| CN110246199B (en) * | 2019-05-26 | 2023-05-09 | 天津大学 | Projection domain data noise removing method for energy spectrum CT |
| CN111899312B (en) * | 2020-07-08 | 2023-03-14 | 西北工业大学 | Iterative compensation finite angle CT projection reconstruction method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102208100A (en) * | 2011-05-31 | 2011-10-05 | 重庆大学 | Total-variation (TV) regularized image blind restoration method based on Split Bregman iteration |
| CN103810734A (en) * | 2014-02-28 | 2014-05-21 | 南方医科大学 | Low-dose X-ray CT projection data restoring method |
| US8886283B1 (en) * | 2011-06-21 | 2014-11-11 | Stc.Unm | 3D and 4D magnetic susceptibility tomography based on complex MR images |
-
2015
- 2015-01-27 CN CN201510040324.2A patent/CN104574416B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102208100A (en) * | 2011-05-31 | 2011-10-05 | 重庆大学 | Total-variation (TV) regularized image blind restoration method based on Split Bregman iteration |
| US8886283B1 (en) * | 2011-06-21 | 2014-11-11 | Stc.Unm | 3D and 4D magnetic susceptibility tomography based on complex MR images |
| CN103810734A (en) * | 2014-02-28 | 2014-05-21 | 南方医科大学 | Low-dose X-ray CT projection data restoring method |
Non-Patent Citations (3)
| Title |
|---|
| Sparse-view x-ray CT reconstruction via total generalized variation regularization;Shanzhou Niu 等;《Physics in Medicine and Biology》;20140519;第59卷(第12期);摘要，正文第1节，第2.1-2.5节，第5节 * |
| 修正ROF模型的分裂Bregman算法;李孟飞 等;《计算机工程与设计》;20130730;第34卷(第7期);第2455-2459页 * |
| 双能谱CT的迭代重建模型及重建方法;赵云松 等;《电子学报》;20140430;第42卷(第4期);第2节，第4.3节 * |
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