CN103440676B - Method for reconstruction of super-resolution coronary sagittal plane image of lung 4D-CT image based on motion estimation - Google Patents
Method for reconstruction of super-resolution coronary sagittal plane image of lung 4D-CT image based on motion estimation Download PDFInfo
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Abstract
The invention discloses a method for reconstruction of a super-resolution coronary sagittal plane image of a lung 4D-CT image based on motion estimation. The method for reconstruction of the super-resolution coronary sagittal plane image of the lung 4D-CT image based on the motion estimation comprises the sequential steps of (1) reading data of the lung 4D-CT image which is formed by a plurality of lung 3D images, wherein the phase positions of the lung 3D images are different; (2) extracting coronary sagittal plane images, corresponding to the same position of the lung, from all the phase positions according to the data of the lung 4D-CT image; (3) estimating motion vector fields between the lung coronary sagittal plane images with different frames based on the full search block matching algorithm; (4) reconstructing the super-resolution lung 4D-CT coronary sagittal plane image by means of the iteration back projection method and based on the motion vector fields obtained in the step (3). According to the method for reconstruction of the super-resolution coronary sagittal plane image of the lung 4D-CT image, the resolution ratio of the reconstructed super-resolution lung 4D-CT coronary sagittal plane image obtained with the method is improved obviously, the brightness and definition of blood vessels and peripheral tissue in the lung parenchyma are improved obviously in a partial enlarged image, the limitation of low resolution caused by the collection time and radiological dose is eliminated, and accurate radiotherapy of lung cancer can be effectively guided.
Description
Technical field
The present invention relates to technical field of medical image processing is and in particular to a kind of lung 4D-CT image based on estimation
Super-resolution hat sagittal plane image rebuilding method.
Background technology
Lung 4D-CT image can provide comprehensive high accuracy radiation therapy respiratory movement to characterize.In lung 4D-CT view data
In, due to there being the image of multiple phase places, typically 10-20, contribute to obtaining pulmonary's respiratory movement letter by each phase image
Breath, is the pinpoint key of radiation therapy target, and therefore lung 4D-CT technology plays more in lung tumor precise radiotherapy
Carry out more important effect.
The acquisition of lung 4D-CT data, typically according to bed and lung volume, by multiple 3D-CT data segments freely breathing
Sort and obtain.However, due to the intrinsic high-dose irradiation of CT, along longitudinally（It is generally termed as Z-direction）Intensive sampling often
Being unpractiaca, thus leading to the interlayer resolution of obtained lung 4D-CT data to be far below layer intrinsic resolution, causing data to show
The anisotropic writing.
Therefore, when each phase place 3D data is carried out be preced with sagittal plane observation, in order to obtain the image of correct proportions, need root
According to the interlayer resolution of 3D data and the ratio of layer intrinsic resolution, carry out interpolation amplification along Z-direction.Conventional interpolation method is
Arest neighbors or bilinear interpolation, but, these methods all can lead to image blurring, especially when in interlayer resolution with layer point
Resolution ratio difference than larger when image blurring more serious.
Therefore, not enough for prior art, provide a kind of super-resolution of lung 4D-CT image based on estimation to be preced with arrow
Shape face image rebuilding method is to overcome prior art deficiency very necessary.
Content of the invention
Present invention aims to prior art is not enough, provide a kind of lung 4D-CT image based on estimation
Super-resolution is preced with sagittal plane image rebuilding method, and the method can improve the resolution of the hat sagittal view picture of lung 4D-CT image.
The above-mentioned purpose of the present invention is achieved through the following technical solutions.
A kind of super-resolution hat sagittal plane image rebuilding method of lung 4D-CT image based on estimation, includes successively
Following steps,
（1）Read the pulmonary's 4D-CT view data being made up of pulmonary's 3D rendering of multiple outs of phase；
（2）According to pulmonary's 4D-CT view data, the corresponding hat sagittal view to each phase extraction same pulmonary position
Picture；
（3）Estimate that different " frame " pulmonarys are preced with the motion vector field between sagittal view picture；
（4）With step（3）Based on the motion vector field obtaining, rebuild high-resolution lung 4D-CT hat sagittal view picture.
Above-mentioned steps（3）Be using based on Full-search block-matching algorithm estimate different " frame " pulmonarys hat sagittal view picture it
Between motion vector field.
Above-mentioned steps（3）Specifically include：
（3.1）Choose a sub-block in the current frame, according to least absolute error matching criterior, in given the searching of reference frame
The block most like with the current block in present frame is found out as match block in rope region；
Moving displacement is calculated as the motion vector of current block, described motion according to the relative position of match block and current block
Vector is also the relative motion vectors of global optimum；
（3.2）Described least absolute error matching criterior is as follows：
... formula（Ⅰ）
Wherein, the size of current block resolution is, the coordinate in the current block upper left corner is；Motion vector is；WithIt is respectively present frame and reference frame in pixelThe value at place, makes in region of searchThe minimum motion vector of value is the optimal motion vector of current block；
（3.3）To different frames, it is repeated in above-mentioned steps（3.1）With（3.2）, obtain different " frame " pulmonarys hat sagittal plane
Motion vector field between image.
Above-mentioned steps（4）Iterative backprojection method is specifically adopted to rebuild high-resolution lung 4D-CT hat sagittal view picture.
Above-mentioned steps（4）Specifically include：
（4.1）The original low-resolution image that will need to rebuildInterpolation amplification is initial high-resolution image,For
Iterationses；
（4.2）According to degradation model by initial high-resolution imageAnalog imaging process obtains the collection of low resolution image
Close,Represent the quantity of original series low-resolution image；
Described degradation model is specially：
；
Wherein：RepresentIn width low-resolution imageWidth,Represent the initial high resolution figure needing to rebuild
Picture；
Represent geometric transformation, the motion vector tried to achieve by estimation；
Represent down-sampling matrix；
It is system additive noise；
Represent fuzzy matrix, be by the relative motion of optical system itself, imaging system and original scene, and low point
The point spread function of resolution sensor and cause；
Specifically,In secondary iterative process,Imaging process by degradation model simulation obtain：
；
Wherein：Represent theThe high-definition picture assumed in secondary iterative process；RepresentBy moving back after secondary iteration
Change the low-resolution image that model obtains；Represent fromArriveTwo-dimensional geometry conversion, as step（3）The motion arrow obtaining
Amount；It is Gaussian Blur operator；It is down-sampling operator；
（4.3）Error in judgementWhether reach minima, if reaching minima, stopping iteration, estimating in the past
The high-definition picture of meterFor finally required super-resolution image；
If error is not up to minima, enter step（4.4）；
（4.4）According to error, Current high resolution image is updated, renewal process formula specific as follows：
Wherein,Represent up-sampling operator；Represent back projection operator, byWithDetermine；
（4.5）Using the high-definition picture after updating as initial high-resolution image, enter step（4.2）.
Preferably, above-mentioned steps（4.3）Middle error in judgementWhether reach minima particular by judgement
Error functionWhether less than the threshold value settingCome to carry out, the specific formula for calculation of error function is：
.
Preferably, above-mentioned steps（4.3）Middle error in judgementWhether reach minima particular by judgement
Whether reach maximum iteration timeCome to judge, work as iterationsesReachWhen, decision errors reach minima, otherwise sentence
Determine error and be not up to minima.
Preferably, maximum iteration timeScope is set greater than being less than or equal to 5 equal to 1.
Further, maximum iteration timeIt is set to 3.
Compared to the prior art, the super-resolution of the lung 4D-CT image that the present invention obtains is preced with dividing of reformed sagittal image
Resolution is significantly improved, partial enlargement in figure, and the blood vessel in pulmonary parenchyma and the brightness of perienchyma and definition have obvious increasing
By force, the image resolution ratio causing due to acquisition time and radiological dose can be overcome to limit, thus effective guiding pulmonary carcinoma is accurate
Radiotherapy.
Brief description
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 coronalplane initial low resolution image of certain phase place in the present invention one lung 4D-CT image；
Fig. 2 is that the coronalplane initial low resolution image of certain phase place of the lung 4D-CT image to Fig. 1 adopts arest neighbors method
The image obtaining after interpolation.
Fig. 3 is the estimated result schematic diagram of the motion vector field of coronalplane between two outs of phase in the present invention.
Fig. 4 is the estimated result schematic diagram of sagittal motion vector field between two outs of phase in the present invention.
Fig. 5 be phase place 0 of the present invention coronalplane adopt distinct methods rebuild result schematic diagram, be from left to right corresponding in turn to
The interpolation method of arest neighbors interpolation method, bilinear interpolation method and the present invention.
Fig. 6 is the enlarged diagram of the Blocked portion in corresponding Fig. 5.Fig. 7 is the sagittal plane of phase place 0 of the present invention using different
The result schematic diagram that method is rebuild, is from left to right corresponding in turn to arest neighbors interpolation method, bilinear interpolation method and the present invention
Interpolation method.
Fig. 8 is the enlarged diagram of the Blocked portion in corresponding Fig. 7.Fig. 9 is the coronalplane of phase place 7 of the present invention using different
The result schematic diagram that method is rebuild, is from left to right corresponding in turn to arest neighbors interpolation method, bilinear interpolation method and the present invention
Interpolation method.
Figure 10 is the enlarged diagram of the Blocked portion in corresponding Fig. 9.Figure 11 is the sagittal plane of phase place 7 of the present invention using not
The result schematic diagram rebuild with method, is from left to right corresponding in turn to arest neighbors interpolation method, bilinear interpolation method and the present invention
Interpolation method.
Figure 12 is the enlarged diagram of the Blocked portion in corresponding Figure 11.
Specific embodiment
Describe the present invention with reference to specific embodiment.
Embodiment 1.
A kind of super-resolution hat sagittal plane image rebuilding method of lung 4D-CT image based on estimation, includes successively
Following steps,
（1）Read the pulmonary's 4D-CT view data being made up of pulmonary's 3D rendering of multiple outs of phase.
（2）From pulmonary's 4D-CT view data, hat sagittal view picture corresponding to each phase extraction same pulmonary position.
（3）Estimate that different " frame " pulmonarys are preced with the motion vector field between sagittal view picture.
Step（3）Be using based on Full-search block-matching algorithm estimate different " frame " pulmonarys hat sagittal view as between
Motion vector field.
Step（3）Specifically include：
（3.1）Choose a sub-block in the current frame, according to least absolute error matching criterior, in given the searching of reference frame
The block most like with the current block in present frame is found out as match block in rope region；
Moving displacement is calculated as the motion vector of current block, described motion according to the relative position of match block and current block
Vector is also the relative motion vectors of global optimum；
（3.2）Described least absolute error matching criterior is as follows：
... formula（Ⅰ）
Wherein, the size of current block resolution is, the coordinate in the current block upper left corner is；Motion vector is；WithIt is respectively present frame and reference frame in pixelThe value at place, makes in region of searchThe minimum motion vector of value is the optimal motion vector of current block.
（3.3）To different frames, it is repeated in above-mentioned steps（3.1）With（3.2）, obtain different " frame " pulmonarys hat sagittal plane
Motion vector field between image.
（4）With step（3）Based on the motion vector field obtaining, rebuild high-resolution lung 4D-CT hat sagittal view picture.
Step（4）Iterative backprojection method is specifically adopted to rebuild high-resolution lung 4D-CT hat sagittal view picture.
Step（4）Specifically include：
（4.1）The original low-resolution image that will need to rebuildInterpolation amplification is initial high-resolution image,For
Iterationses；
（4.2）According to degradation model by initial high-resolution imageAnalog imaging process obtains the collection of low resolution image
Close,Represent the quantity of original series low-resolution image；
Described degradation model is specially：
；
Wherein：RepresentIn width low-resolution imageWidth,Represent the initial high resolution figure needing to rebuild
Picture；
Represent geometric transformation, the motion vector tried to achieve by estimation；
Represent down-sampling matrix；
It is system additive noise；
Represent fuzzy matrix, be by the relative motion of optical system itself, imaging system and original scene, and low point
The point spread function of resolution sensor and cause；
Specifically,In secondary iterative process,Imaging process by degradation model simulation obtain：
；
Wherein：Represent theThe high-definition picture assumed in secondary iterative process；RepresentBy moving back after secondary iteration
Change the low-resolution image that model obtains；Represent fromArriveTwo-dimensional geometry conversion, as step（3）The motion arrow obtaining
Amount；It is Gaussian Blur operator；It is down-sampling operator；
（4.3）Error in judgementWhether reach minima, if reaching minima, stopping iteration, estimating in the past
MeterFor finally required super-resolution image；
If error is not up to minima, enter step（4.4）.
Above-mentioned steps（4.3）Middle error in judgementWhether reaching minima can be by error in judgement functionWhether less than the threshold value settingCome to carry out, the specific formula for calculation of error function is：
.
Step（4.3）Middle error in judgementWhether reaching minima can also be by judging whether to reach
Big iterationsesCome to judge, work as iterationsesReachWhen, decision errors reach minima, and otherwise decision errors do not reach
To minima.
Maximum iteration timeScope is set greater than being less than or equal to 5 equal to 1, is preferably arranged to 3 times, can take into account figure
The resolution of picture, can take into account the demand that operand is little, process time is short again.
（4.4）According to error, Current high resolution image is updated, renewal process formula specific as follows：
Wherein,Represent up-sampling operator；Represent back projection operator, byWithDetermine；
（4.5）Using the high-definition picture after updating as initial high-resolution image, enter step（4.2）.
The present invention, according to the feature of lung 4D-CT view data, rebuilds clearly lung hat sagittal plane using super-resolution technique
Image.Image super-resolution technology is to be weighed with regard to the low resolution degraded image having mutual displacement of Same Scene using multiframe
Build the technology of high-resolution high quality graphic.Lung 4D-CT image, is made up of the 3D rendering of 10-20 out of phase, it is each
The phase place correspondence different lung motion moment.Therefore for the low resolution hat sagittal view picture at a certain position of a certain 3D rendering,
The low resolution of many " frame " Same Scene that the hat sagittal view picture of other phase place correspondence positions is regarded as correlation, having moving displacement
Rate image, therefore high-resolution lung 4D-CT hat sagittal view picture can be obtained using image super-resolution rebuilding technology.
Compared with prior art, the super-resolution of the lung 4D-CT image that the present invention obtains is preced with dividing of reformed sagittal image
Resolution has obvious raising, partial enlargement in figure, and the blood vessel in pulmonary parenchyma and the brightness of perienchyma and definition have substantially
Strengthen, the low restriction of the image resolution ratio being caused by acquisition time and radiological dose can be overcome such that it is able to effectively guide lung
The precise radiotherapy of cancer.
Embodiment 2.
To describe the processing procedure of the inventive method, this lung in conjunction with a 4D-CT sequence image with 10 phase places in detail
4D-CT is preced with comprising the following steps that of sagittal plane super resolution ratio reconstruction method：
（1）Read pulmonary's 4D-CT view data, this view data is by pulmonary's 3D-CT view data of 10 outs of phase
Constitute, resolution is 256*256*49, image layer intrinsic resolution is 1.13mm, interlayer resolution is 5mm；
（2）Extract respectively from pulmonary's 4D-CT view data 10 phase places the coronalplane at correspondence same pulmonary position and
Sagittal view picture, as the initial low resolution image of the present invention, resolution is 256*49.
Fig. 1 shows the coronalplane initial low resolution image of lung 4D-CT phase place, and Fig. 2 is to adopt arest neighbors to insert Fig. 1
Image after the process of value method, as can be seen from the figure adopts the image after the process of arest neighbors interpolation method very fuzzy.
(3) estimate that different " frame " pulmonarys are preced with the motion arrow between sagittal view picture using based on Full-search block-matching algorithm
Amount field.
Specifically include：
（3.1）Choose a sub-block in the current frame, according to least absolute error matching criterior, in given the searching of reference frame
The block most like with the current block in present frame is found out as match block in rope region；
Moving displacement is calculated as the motion vector of current block, described motion according to the relative position of match block and current block
Vector is also the relative motion vectors of global optimum.
（3.2）Specifically, in estimation, the size of block is set as 16*16, and region of search is the overall situation, using minimum absolute
Error matching criterior, is defined as follows：
；
Wherein, the size of current block is, the coordinate in the block upper left corner is；Motion vector is；WithIt is respectively present frame and reference frame in pixelThe value at place.Make in region of searchValue
Minimum motion vector is the optimal motion vector of current block.
（3.3）To different frames, it is repeated in step（3.1）With（3.2）, obtain different " frame " pulmonarys hat sagittal view picture
Between motion vector field.
Fig. 3 and Fig. 4 respectively illustrates between two outs of phase coronalplane and sagittal view as motion vector field, by motion
Vector field is evident that the mass motion trend of lung.
（4）With step（3）Based on the motion vector field obtaining, high-resolution lung 4D- is rebuild using iterative backprojection method
CT is preced with sagittal view picture.
Specifically include：
（4.1）The original low-resolution image that will need to rebuildInterpolation amplification is the initial high-resolution of 256*216 for size
Rate image,For iterationses；
（4.2）According to degradation model by initial high-resolution imageIt is 256*49 that analog imaging process obtains resolution
Low resolution image set,Represent the quantity of original series low-resolution image.
Degradation model is specially：
；
Wherein：RepresentIn width low-resolution imageWidth,Represent the initial high resolution figure needing to rebuild
Picture；
Represent geometric transformation, the motion vector tried to achieve by estimation；
Represent down-sampling matrix；
It is system additive noise；
Represent fuzzy matrix, be by the relative motion of optical system itself, imaging system and original scene, and low point
The point spread function of resolution sensor and cause.
Specifically,In secondary iterative process,Imaging process by degradation model simulation obtain：
；
Wherein：Represent theThe high-definition picture assumed in secondary iterative process；RepresentBy moving back after secondary iteration
Change the low-resolution image that model obtains；Represent fromArriveTwo-dimensional geometry conversion, as step（3）The motion arrow obtaining
Amount；It is Gaussian Blur operator, the present invention adopts Gauss model, and standard deviation is set to 3；It is down-sampling operator.
（4.3）Error in judgementWhether reaching minima, if reaching minima, stopping iteration, with current
EstimateAs finally required super-resolution image；
If error is not up to minima, enter step（4.4）.
Step（4.3）Middle error in judgementWhether reaching minima can be by error in judgement function
Whether less than the threshold value settingCome to carry out, the specific formula for calculation of error function is：
.
Step（4.3）Middle error in judgementWhether reaching minima can also be by judging whether to reach maximum
IterationsesCome to judge, work as iterationsesReachWhen, decision errors reach minima, and otherwise decision errors are not up to
Minima.In the present embodiment, maximum iteration timeIt is set to 3 times, the resolution of image can be taken into account, operand can be taken into account again
The short demand of little, process time.
（4.4）According to error, Current high resolution image is updated, renewal process formula specific as follows：
；
Wherein,Represent up-sampling operator；Represent back projection operator, byWithDetermine.
（4.5）By the high-definition picture after updatingAs initial high-resolution image, enter step
（4.2）.
By iterating, untilLess than given thresholdOr reach maximum iteration time, iteration terminates, currently
EstimateFor finally required super-resolution image.
Fig. 5, Fig. 6 show the typical coronal image display effect of phase place 0, from left to right respectively arest neighbors interpolation,
Bilinear interpolation and the result of the inventive method reconstruction.Fig. 7, Fig. 8 show the typical sagittal plane image display effect of phase place 0,
It is respectively the result that arest neighbors interpolation, bilinear interpolation and the inventive method are rebuild from left to right.
Fig. 9, Figure 10 show the typical coronal image display effect of phase place 7, from left to right respectively arest neighbors interpolation,
Bilinear interpolation and the result of the inventive method reconstruction.Figure 11, Figure 12 show the typical sagittal view picture display effect of phase place 7
Really, the result that respectively arest neighbors interpolation, bilinear interpolation and the inventive method are rebuild from left to right.
As can be seen from the above results, the super-resolution rebuilding image that the present invention obtains is than traditional arest neighbors interpolation, double
Linear interpolation method has larger improvement, has obvious raising, from the point of view of partial enlargement image, excess of the lung to the resolution of image
The brightness of the blood vessel in matter and perienchyma and definition are remarkably reinforced.
Finally it should be noted that above example only in order to illustrate technical scheme rather than to the present invention protect
The restriction of scope, although being explained in detail to the present invention with reference to preferred embodiment, those of ordinary skill in the art should manage
Solution, technical scheme can be modified or equivalent, without deviating from technical solution of the present invention essence and
Scope.
Claims (3)
1. a kind of lung 4D-CT image based on estimation super-resolution hat sagittal plane image rebuilding method it is characterised in that：
In turn include the following steps,
（1）Read the pulmonary's 4D-CT view data being made up of pulmonary's 3D rendering of multiple outs of phase；
（2）According to pulmonary's 4D-CT view data, hat sagittal view picture corresponding to each phase extraction same pulmonary position；
（3）Estimate that different " frame " pulmonarys are preced with the motion vector field between sagittal view picture；
（4）With step（3）Based on the motion vector field obtaining, rebuild high-resolution lung 4D-CT hat sagittal view picture；
Described step（3）Be using based on Full-search block-matching algorithm estimate different " frame " pulmonarys hat sagittal view as between
Motion vector field, specifically includes：
（3.1）Choose a sub-block in the current frame, according to least absolute error matching criterior, in the given field of search of reference frame
The block most like with the current block in present frame is found out as match block in domain；
Moving displacement is calculated as the motion vector of current block, described motion vector according to the relative position of match block and current block
Also it is the relative motion vectors of global optimum；
（3.2）Described least absolute error matching criterior is as follows：
... formula（Ⅰ）
Wherein, the size of current block resolution is, the coordinate in the current block upper left corner is；Motion vector is；WithIt is respectively present frame and reference frame in pixelThe value at place, makes in region of searchThe minimum motion vector of value is the optimal motion vector of current block；
(3.3）To different frames, it is repeated in above-mentioned steps（3.1）With（3.2）, obtain pulmonary's hat sagittal view of different " frame "
Motion vector field between picture；
Described step（4）Iterative backprojection method is specifically adopted to rebuild high-resolution lung 4D-CT hat sagittal view picture；Specifically wrap
Include：
（4.1）The original low-resolution image that will need to rebuildInterpolation amplification is initial high-resolution image,For iteration
Number of times；
（4.2）According to degradation model by initial high-resolution imageAnalog imaging process obtains the set of low resolution image,Represent the quantity of original series low-resolution image；
Described degradation model is specially：
；
Wherein：RepresentIn width low-resolution imageWidth,Represent the initial high-resolution image needing to rebuild；
Represent down-sampling matrix；
It is system additive noise；
Represent geometric transformation, be the motion vector tried to achieve by estimation；
Represent fuzzy matrix, be by the relative motion of optical system itself, imaging system and original scene, and low resolution
The point spread function of sensor and cause；
Specifically,In secondary iterative process,Imaging process by degradation model simulation obtain：
；
Wherein：Represent theThe high-definition picture assumed in secondary iterative process；RepresentBy degradation model after secondary iteration
The low-resolution image obtaining；Represent fromArriveTwo-dimensional geometry conversion, as step（3）The motion vector obtaining；It is
Gaussian Blur operator；It is down-sampling operator；
（4.3）Error in judgementWhether reaching minima, if reaching minima, stopping iteration, with current estimation
High-definition pictureAs finally required super-resolution image；
If error is not up to minima, enter step（4.4）；
（4.4）According to error, Current high resolution image is updated, renewal process formula specific as follows：
Wherein,Represent up-sampling operator；Represent back projection operator, byWithDetermine；
（4.5）Using the high-definition picture after updating as initial high-resolution image, enter step（4.2）；
Described step（4.3）Middle error in judgementWhether reach minima particular by error in judgement function
Whether less than the threshold value settingCome to carry out, the specific formula for calculation of error function is：
；
Described step（4.3）Middle error in judgementWhether reach minima particular by judging whether to reach maximum
IterationsesCome to judge, work as iterationsesReachWhen, decision errors reach minima, and otherwise decision errors are not up to
Minima.
2. the super-resolution hat sagittal plane image reconstruction of the lung 4D-CT image based on estimation according to claim 1
Method it is characterised in that：Maximum iteration timeScope is set greater than being less than or equal to 5 equal to 1.
3. the super-resolution hat sagittal plane image reconstruction of the lung 4D-CT image based on estimation according to claim 2
Method it is characterised in that：Maximum iteration timeIt is set to 3.
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Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103985111B (en) * | 2014-02-21 | 2017-07-25 | 西安电子科技大学 | A kind of 4D MRI ultra-resolution ratio reconstructing methods learnt based on doubledictionary |
| CN103886568B (en) * | 2014-03-18 | 2017-06-06 | 南方医科大学 | Lung 4D CT image super-resolution rebuilding methods based on registration |
| CN104299209A (en) * | 2014-09-18 | 2015-01-21 | 南方医科大学 | Lung 4D-CT image super-resolution reconstruction method based on fast sub-pixel motion estimation |
| WO2017137795A1 (en) * | 2016-02-08 | 2017-08-17 | Elekta Ltd. | Motion management in image-guided radiotherapy |
| CN106846250B (en) * | 2017-01-22 | 2020-05-22 | 宁波星帆信息科技有限公司 | Super-resolution reconstruction method based on multi-scale filtering |
| CN111317923A (en) * | 2020-01-30 | 2020-06-23 | 哈尔滨理工大学 | Resistance chain distribution method for respiratory motion signal wrong-phase super-resolution circuit |
| CN111751371B (en) * | 2020-06-24 | 2021-01-08 | 武汉中纪生物科技有限公司 | Immunohistochemical digital slide reading system and method |
| CN111882588B (en) * | 2020-07-29 | 2024-07-02 | Oppo广东移动通信有限公司 | Image block registration method and related product |
| CN112184549B (en) * | 2020-09-14 | 2023-06-23 | 阿坝师范学院 | Super-resolution image reconstruction method based on space-time transformation technology |
| CN114820739B (en) * | 2022-07-01 | 2022-10-11 | 浙江工商大学 | Multispectral camera-oriented image rapid registration method and device |
| CN115239558A (en) * | 2022-07-19 | 2022-10-25 | 河南省肿瘤医院 | Low-dose lung CT image detail super-resolution reconstruction method and system |
-
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Non-Patent Citations (4)
| Title |
|---|
| Lung Deformation Estimation and Four-Dimensional CT Lung Reconstruction;Sheng Xu et al;《MICCAI 2005》;20051231;第LNCS 3750卷;第2节、第4节第1段，及图8、9 * |
| Motion analysis for image enhancement: Resolution, occlusion, and transparency;Irani M and Peleg S;《Journal of Visual Communication and Image Representation》;19931231;第4卷(第4期);第3节 * |
| 一种基于MAP的超分辨率图像重建的快速算法;肖创柏;《计算机研究与发展》;20091231;第46卷(第5期);全文 * |
| 序列图像超分辨率重建方法的研究;禹晶;《中国优秀硕士学位论文全文数据库 信息科技辑》;20071215(第06期);第3.2节 * |
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