CN108198235A - A kind of three dimentional reconstruction method, apparatus, equipment and storage medium - Google Patents
A kind of three dimentional reconstruction method, apparatus, equipment and storage medium Download PDFInfo
- Publication number
- CN108198235A CN108198235A CN201711421838.8A CN201711421838A CN108198235A CN 108198235 A CN108198235 A CN 108198235A CN 201711421838 A CN201711421838 A CN 201711421838A CN 108198235 A CN108198235 A CN 108198235A
- Authority
- CN
- China
- Prior art keywords
- pile
- image
- dimensional
- images
- volume data
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 70
- 238000002604 ultrasonography Methods 0.000 claims abstract description 61
- 230000009191 jumping Effects 0.000 claims abstract 3
- 239000011159 matrix material Substances 0.000 claims description 83
- 230000006870 function Effects 0.000 claims description 67
- 230000009466 transformation Effects 0.000 claims description 41
- 238000004590 computer program Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 6
- 239000013598 vector Substances 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims 2
- 238000010276 construction Methods 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract 1
- 230000008569 process Effects 0.000 description 16
- 238000005457 optimization Methods 0.000 description 14
- 238000005259 measurement Methods 0.000 description 6
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000000354 decomposition reaction Methods 0.000 description 4
- 210000000056 organ Anatomy 0.000 description 4
- 239000000523 sample Substances 0.000 description 4
- 230000001131 transforming effect Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000011524 similarity measure Methods 0.000 description 3
- 230000003247 decreasing effect Effects 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 238000011478 gradient descent method Methods 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 230000000241 respiratory effect Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000002922 simulated annealing Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000003444 anaesthetic effect Effects 0.000 description 1
- 210000000988 bone and bone Anatomy 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000001125 extrusion Methods 0.000 description 1
- 230000001605 fetal effect Effects 0.000 description 1
- 238000002695 general anesthesia Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 210000004872 soft tissue Anatomy 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 230000026683 transduction Effects 0.000 description 1
- 238000010361 transduction Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/005—General purpose rendering architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Graphics (AREA)
- Ultra Sonic Daignosis Equipment (AREA)
- Image Processing (AREA)
Abstract
The applicable field of computer technology of the present invention, provides a kind of three dimentional reconstruction method, apparatus, equipment and storage medium, this method and includes：The heap image of kinematic error minimum is set as template heap image in the heap image scanned in Multi-angle ultrasound, remaining heap image and template heap image are subjected to global registration, according to remaining heap image, template heap picture construction three-dimensional data, the three-dimensional data is updated by the volume reconstruction mode based on kernel regression function, judge whether updated three-dimensional data restrains, it is then by remaining heap image, template heap image carries out local registration with three-dimensional data, otherwise the step of being updated to the three-dimensional data is jumped to, judge whether the three-dimensional data after local registration restrains, it is to export the three-dimensional data after local registration, otherwise the step of jumping to structure three-dimensional data, it is achieved thereby that the three dimentional reconstruction based on multi-angle scanning, it is effectively improved the reconstruction quality of three-dimensional ultrasound pattern.
Description
Technical Field
The invention belongs to the technical field of medical images, and particularly relates to a three-dimensional ultrasonic reconstruction method, a three-dimensional ultrasonic reconstruction device, three-dimensional ultrasonic reconstruction equipment and a storage medium.
Background
The three-dimensional ultrasonic system can intuitively provide the spatial position and three-dimensional shape of the interested organ or tissue in the body for doctors to more accurately reflect the shape, volume, contour and adjacency between organs of the organ lesion of the human body in clinic. At present, three-dimensional ultrasound systems mainly include two major types, one is a volumetric imaging method based on a two-dimensional area array probe (also called a three-dimensional ultrasound dedicated volumetric probe), which includes a transduction wafer and a driving device in the probe, and a mechanical device drives the wafer to perform equidistant sector scanning or annular scanning, and the other is a method that acquires a sequence of two-dimensional ultrasound images by using a traditional two-dimensional ultrasound apparatus in combination with specific spatial positioning information, and then generates three-dimensional ultrasound volume data by using a three-dimensional ultrasound reconstruction method, which mainly includes a three-dimensional ultrasound system (Motorized 3D ultrasound) based on a mechanical arm, a three-dimensional ultrasound system (Sensorless 3D ultrasound) without a positioner, and a free three-dimensional ultrasound system (Freehand 3D ultrasound). From the clinical application perspective of three-dimensional ultrasound, the Freehand three-dimensional ultrasound system better conforms to the operation habits of doctors and Huaning of operating rooms, and is a scheme with wider target application prospects.
Currently, three-dimensional ultrasound reconstruction algorithms can be classified into three categories: Voxel-Based methods (VBM), pixel-Based methods (PBM) and Function-Based methods (FBM). The three types of reconstruction algorithms do not consider two types of key problems, namely, when a doctor observes a result obtained after reconstructing a two-dimensional ultrasonic sequence image scanned at a single angle, may not be well diagnosed due to occlusion of bone tissue and other organs, the limitation of single-angle scanning is the influence of motion artifacts on ultrasonic data, the sources of the motion artifacts mainly include motion artifacts caused by non-shelterable physiological factors (for example, respiratory motion, although the artifacts caused by respiratory motion can be avoided by ultrasonic scanning under general anesthesia, a patient is injured by using anesthetic), the scanning subjects do not cooperate with scanning experiments in action (for example, involuntary motion and fetal movement of infants), and soft tissue deformation caused by natural factors such as extrusion or gravity field of an ultrasonic probe.
Disclosure of Invention
The invention aims to provide a three-dimensional ultrasonic reconstruction method, a three-dimensional ultrasonic reconstruction device, an image processing device and a storage medium, and aims to solve the problem that in the prior art, the reconstruction quality of a three-dimensional ultrasonic image is poor due to the fact that the three-dimensional ultrasonic reconstruction does not consider the limitation of single-angle scanning and the influence of motion artifacts on ultrasonic data.
In one aspect, the present invention provides a three-dimensional ultrasound reconstruction method, comprising the steps of:
when pile images scanned by multi-angle ultrasound are received, setting the pile image with the minimum motion error in all the pile images as a template pile image, and globally registering the rest pile images in all the pile images with the template pile image;
constructing three-dimensional volume data through a preset volume reconstruction mode based on a point spread function according to the residual pile images and the template pile images;
updating the three-dimensional volume data through a preset volume reconstruction mode based on a kernel regression function, and judging whether the updated three-dimensional volume data is converged;
when the three-dimensional volume data are converged, respectively and locally registering the residual pile images and the template pile images with the three-dimensional volume data, otherwise, skipping to a step of updating the three-dimensional volume data in a preset volume reconstruction mode based on a kernel regression function;
and judging whether the three-dimensional volume data after local registration is converged, if so, outputting the three-dimensional volume data, and otherwise, skipping to the step of constructing the three-dimensional volume data in a preset volume reconstruction mode based on a point spread function.
In another aspect, the present invention provides a three-dimensional ultrasound reconstruction apparatus, comprising:
the global registration unit is used for setting a pile image with the minimum motion error in all pile images as a template pile image and registering the rest pile images in all the pile images with the template pile image globally when the pile images scanned by multi-angle ultrasound are received;
the three-dimensional reconstruction unit is used for constructing three-dimensional volume data through a preset volume reconstruction mode based on a point spread function according to the residual pile images and the template pile images;
the three-dimensional body updating unit is used for updating the three-dimensional body data through a preset body reconstruction mode based on a kernel regression function and judging whether the updated three-dimensional body data is converged;
the local registration unit is used for locally registering the residual pile images and the template pile images with the three-dimensional volume data when the three-dimensional volume data are converged, otherwise, the three-dimensional volume updating unit executes the operation of updating the three-dimensional volume data in a preset volume reconstruction mode based on a kernel regression function; and
and the volume data output unit is used for judging whether the three-dimensional volume data after local registration is converged or not, if so, outputting the three-dimensional volume data, and otherwise, executing the operation of constructing the three-dimensional volume data in a preset volume reconstruction mode based on a point spread function by the three-dimensional reconstruction unit.
In another aspect, the present invention further provides an image processing apparatus, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the three-dimensional ultrasound reconstruction method when executing the computer program.
In another aspect, the present invention also provides a computer-readable storage medium, which stores a computer program, which when executed by a processor implements the steps of the three-dimensional ultrasound reconstruction method as described above.
The invention selects the pile image with the minimum motion error from the pile images scanned by multi-angle ultrasonic as the template pile image, globally registers the residual pile image and the template pile image, constructs three-dimensional data by a point expansion function-based body reconstruction mode according to the registered residual pile image and the template pile image, updates the three-dimensional data by a kernel regression function-based body reconstruction mode, judges whether the updated three-dimensional data is converged, if so, locally registers the residual pile image and the template pile image with the three-dimensional data respectively, otherwise, continuously updates the three-dimensional data, judges whether the locally registered three-dimensional data is converged, outputs the three-dimensional data, otherwise, continuously jumps to the step of constructing the three-dimensional data by the point expansion function-based body reconstruction mode, thereby realizing the three-dimensional ultrasonic reconstruction of multi-angle scanning, solving the observation limitation brought by single-angle scanning to doctors, the artifacts in the three-dimensional ultrasonic image are effectively removed, and the reconstruction quality of the three-dimensional ultrasonic image is improved.
Drawings
Fig. 1 is a flowchart illustrating an implementation of a three-dimensional ultrasonic reconstruction method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a three-dimensional ultrasonic reconstruction apparatus according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a three-dimensional ultrasonic reconstruction apparatus according to a second embodiment of the present invention; and
fig. 4 is a schematic structural diagram of an image processing apparatus according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of specific implementations of the present invention is provided in conjunction with specific embodiments:
the first embodiment is as follows:
fig. 1 shows an implementation flow of a three-dimensional ultrasound reconstruction method provided by a first embodiment of the present invention, and for convenience of description, only parts related to the first embodiment of the present invention are shown, which are detailed as follows:
in step S101, when the heap images scanned by the multi-angle ultrasound are received, the heap image with the smallest motion error among all the heap images is set as a template heap image, and the remaining heap images among all the heap images are globally registered with the template heap image.
The embodiment of the invention is suitable for a three-dimensional ultrasonic system. The ultrasound scanning is performed at different angles to obtain two-dimensional ultrasound image sequences (i.e., two-dimensional slice sequences) scanned at different angles, and the two-dimensional ultrasound image sequences at the same angle can be called a heap image. A pile image with the minimum motion error can be selected from all pile images as a template pile image by adopting a method based on motion amount estimation, and then the residual pile images in all pile images are subjected to global registration with the template pile image, so that each residual pile image is aligned with the template pile image, and the artifacts in the process of reconstructing the ultrasonic image are reduced. By way of example, the different angles may include axial, coronal, and sagittal directions.
In the embodiment of the present invention, the process of selecting a pile image with the smallest motion error from all pile images as the template pile image can be implemented by the following steps:
(1) and vectorizing each two-dimensional ultrasonic image to obtain a matrix corresponding to each pile image.
In the embodiment of the present invention, the matrix corresponding to each pile image may be represented as a ═ vec (I)1)；...；vec(Ik)]∈Rm*kWherein, vec (I)j) Represents the j (j e 1, a., k) th two-dimensional ultrasound image I in the pile imagejAs a column vector.
(2) And generating an observation matrix corresponding to each pile image according to the matrix corresponding to each pile image, and performing singular value decomposition on the observation matrix of each pile image.
In an embodiment of the invention, the observation matrix of the heap image is D ═ a + E, E is the identity matrix, D is full rank and D ∈ Rm*kSo that the decreasing order of the singular values of D is s1≥s2≥...≥skNot less than 0, D can be decomposed by singular value to obtain D ═ USVT(U∈Rm*k，S∈Rk*k，V∈Rk*k) And S is a diagonal matrix with elements on the diagonal being singular values.
(3) And generating a matrix similar to the observation matrix according to the singular value decomposition result of the observation matrix, and determining the template pile images in all pile images according to the observation matrix and the matrix similar to the observation matrix.
In the embodiment of the invention, the slave units can respectively receive Um*k、Sk*k、Vk*kR column of sub-matrix U 'is taken from the upper left corner of the matrix'm*r、S′r*r、U′r*kFrom D '═ U' S 'V'TD and D' are very similarThe Frobenius norm (Frobenius norm) can be used to measure the degree of similarity between D and D':
wherein | D | is the frobenius norm of D, | D '| is the frobenius norm of D', and | D-D '| is used to represent the degree of similarity between D and D'. Therefore, the relative similarity difference between D and D' can be expressed as:
at the point of satisfying deltarUnder the condition that the image is smaller than the preset threshold value, each pile image can obtain the corresponding deltarThe smallest r value, and then the pile image with the smallest r value among all the pile images is selected as the template pile image.
Preferably, for greater intuition and convenience, the above process is simplified by using a variable ω, r · δ, for measuring the motion estimatorrAnd calculating the minimum value of omega corresponding to each pile image, and setting the pile image with the minimum omega as a template pile image.
In the embodiment of the invention, when the residual stack image and the template stack image are subjected to global registration, a rigid registration mode can be adopted to preliminarily calculate a transformation matrix, namely the transformation matrix is calculated by utilizing translation parameters and rotation parameters in a three-dimensional space, in order to be convenient for distinguishing from subsequent local registration, the transformation matrix is called as a global transformation matrix, then the residual stack image is subjected to registration through the global transformation matrix, and specifically, the residual stack image S is subjected to registration through the following formulaiAnd (3) carrying out global registration:
S′i＝Tglobal·Sii ≠ T, where TglobalFor global transformation matrix, STRepresenting the template pile image, S when i ≠ TiRepresents the ith residual pile image, S'iRepresenting the registered remaining stack images. After the registration is completed,and calculating the measure between the residual pile image and the template pile image, judging whether the measure meets a preset first measure threshold value, if so, finishing the registration of the pile image, otherwise, optimizing the global transformation matrix by a preset optimization method, and registering the pile image by the optimized global transformation matrix. The measure is used to quantitatively measure the matching effect between two stack images, that is, to describe the difference or similarity between the remaining stack images and the template stack images, for example, the difference measure is composed of Mean square difference (Mean square difference), difference between entropies (Entropy of difference), and the like, the similarity measure is normalized correlation coefficient (normalized correlation coefficient), Mutual information (Mutual information), and the like, the ultrasound image registration is a process of continuously iterating and solving a minimum value through a difference measure function, or a process of continuously iterating and solving a maximum value through a similarity measure function, and the optimization method may adopt a global optimization algorithm such as a gradient descent method, a genetic algorithm, or a simulated annealing method.
In step S102, three-dimensional volume data is constructed by a preset volume reconstruction method based on a point spread function according to the remaining pile images and the template pile images.
In the embodiment of the present invention, when three-dimensional volume data is constructed, pixels on a two-dimensional ultrasound image in a residual stack image and a template stack image need to be converted into voxels on the three-dimensional volume data, the pixels on the two-dimensional ultrasound image and the voxels on the three-dimensional volume data are not aligned well, a pixel point on the two-dimensional ultrasound image affects values of a plurality of voxel points on the three-dimensional volume data, in order to reconstruct the three-dimensional volume data more accurately, a volume reconstruction method based on a point spread function may be adopted, and each reconstruction process is a process of updating voxel values on the three-dimensional volume data by using pixels on the two-dimensional ultrasound image. After the global registration of the residual pile images and the template pile images, the three-dimensional volume data is firstly reconstructed by adopting a volume reconstruction method based on a point spread function, the three-dimensional volume data which is firstly reconstructed needs to be updated, locally registered and the like subsequently, and if the three-dimensional volume data obtained through the operations is not converged, the three-dimensional volume data is reconstructed again (namely the three-dimensional volume data is updated), updated, locally registered and the like until the final three-dimensional volume data is converged.
In the embodiment of the invention, the slave pixel point psTo the voxel point prThe spatial transformation process of (a) may be expressed as:
wherein, WsFor transforming the pile image from the image coordinate system to the transformation matrix of the world coordinate system, WrTransformation matrix for transforming a three-dimensional reconstruction from a target coordinate system to a world coordinate system, TtotalFor the registration matrix, T is the registration matrix at the time of the primary reconstruction since no local registration has been performed yettotal＝TglobalDuring subsequent multiple reconstructions, Ttotal＝Tglobal·Tlocal，TlocalFor local transformation matrix for local registration, letThen p iss＝F·pr. Therefore, the pixels on the two-dimensional ultrasound image in the remaining pile image and the template pile image can be converted to three-dimensional volume data according to the spatial conversion process, so as to obtain a three-dimensional volume data, and then step S103 is performed.
When the three-dimensional volume data is reconstructed (or updated) by using the point spread function-based volume reconstruction method at the subsequent nth time, the update formula of the voxel value can be expressed as:
the PSF is a point spread function and is a pixel point obtained by space conversion of an original voxel point on a three-dimensional body. Wherein, the point spread function can be three-dimensional Gaussian point spread function:
in step S103, the three-dimensional volume data is updated by a preset volume reconstruction method based on the kernel regression function.
In the embodiment of the invention, since the sampling data of the three-dimensional ultrasound is generally sparse, some blank areas without pixels are still present in the three-dimensional volume data after reconstruction or update, and therefore, each pixel point on the three-dimensional volume data is updated by adopting a volume reconstruction mode based on a kernel regression function.
In the embodiment of the invention, the mathematical observation model adopting the kernel regression function-based body reconstruction mode is as follows:
Yi＝r(Xi)+εiwhere, i ═ 1.·, M is the number of all voxel points on the three-dimensional volume data, r (·) is the kernel regression function, Xi＝(Xi0，Xi1，Xi2) For three-dimensional volume data XiThree-dimensional coordinates of the upper voxel point, r (X)i) What is obtained is the voxel point XiIdeal voxel value of, YiIs a voxel point XiActual voxel value of epsiloniMean difference is 0, variance is sigma2Gaussian noise. Sequentially setting voxel points on the three-dimensional data as voxel points to be updated, and when the current voxel point X to be updated is far away from the voxel point XiWhen it is very close, the voxel point X can be obtained according to the Taylor formula of N-th orderiThe ideal voxel values of are:
according to the minimization of the second multiplication, the N-level Taylor formula and the mathematical observation model, the optimization problem can be obtained:
where m is the voxel point X within the neighborhood of the voxel point X to be found (e.g., 3X 3 size neighborhood, where the size of the neighborhood is not limited)iH is a predetermined smoothing parameter, and K (-) isLet the kernel function K (·) be an exponential function or gaussian function, and it is necessary to satisfy · tk (t) dt ═ 0, · t ·2K (t) dt ═ c. The above optimization problem can be represented by a matrix:
wherein Y is all voxel points XiY ═ Y1，Y2，...，Ym]T，Is all βiSet of vectors consisting of W ═ diag [ K (X)0-X)，K(X1-X)，...，K(Xm-X)]Is a diagonal matrix with elements K (-) at the diagonal and zero for the other elements,solving the optimization problem expressed by the matrix according to a least square method can obtain:
wherein, is the voxel estimation value of the voxel point X, i.e. the voxel value that is finally used to update the voxel point X to be updated.
In step S104, it is determined whether the updated three-dimensional volume data converges.
In the embodiment of the present invention, a maximum update time for updating all voxel points on the three-dimensional volume data in a kernel regression function-based volume reconstruction method may be preset, after updating all voxels on the three-dimensional volume points once, whether the updated three-dimensional volume data converges is determined by determining whether the current update time exceeds the maximum update time, if so, step S105 is executed, otherwise, the current update time is incremented by one, and the step S103 is skipped to continue to update the three-dimensional volume data. In addition, whether the updated three-dimensional volume data converges may also be determined by whether an error between two three-dimensional volume data before and after the update exceeds a preset update error threshold.
In step S105, the residual pile image and the template pile image are locally registered with the three-dimensional volume data, respectively.
In an embodiment of the present invention, the two-dimensional ultrasound images in the remaining pile image and the template pile image are locally registered with the three-dimensional volume data, respectively, to adjust the spatial correspondence between each two-dimensional ultrasound image and the three-dimensional volume data. The local transformation matrix T can be calculated by adopting a non-rigid registration modelocalAnd registering the two-dimensional ultrasonic image through the local transformation matrix:
wherein j is 1jRepresenting the jth two-dimensional ultrasound image, l ', in the remaining or template pile image'jIs a pair IjAnd k represents the number of the two-dimensional ultrasonic images in the residual pile images or the template pile images. And after registration, calculating the measurement between the two-dimensional ultrasonic image and the three-dimensional volume data, judging whether the measurement meets a preset second measurement threshold, if so, finishing registration of the two-dimensional ultrasonic image, otherwise, optimizing the local transformation matrix by a preset optimization method, and continuously registering the two-dimensional ultrasonic image by the optimized global transformation matrix. The measure can be a difference measure or a similarity measure, and the optimization mode can adopt a global optimization algorithm such as a gradient descent method, a genetic algorithm or a simulated annealing method.
In step S106, it is determined whether the three-dimensional volume data after the local registration converges.
In the embodiment of the present invention, the maximum number of times of constructing the three-dimensional volume data in the volume reconstruction method based on the point spread function may be preset, and whether the three-dimensional volume data is converged is determined by determining whether the current number of times of constructing exceeds the maximum number of times of constructing, if yes, step S107 is executed, otherwise, the current number of times of constructing is increased by one, and the method jumps to step S102 to continue constructing the three-dimensional volume data in the preset volume reconstruction method based on the point spread function. In addition, whether the three-dimensional reconstruction volume data are converged can be determined by judging whether the error between the three-dimensional volume data obtained by two times of construction is smaller than a preset reconstruction error threshold value.
In step S107, three-dimensional volume data is output.
In the embodiment of the invention, the pile image with the minimum motion error is selected from the pile images scanned by the multi-angle ultrasonic as the template pile image, the rest pile images and the template pile image are subjected to global registration, constructing three-dimensional reconstruction data according to the residual heap image and the template heap image after global registration, updating the three-dimensional volume data for multiple times by adopting a nuclear regression mode until the three-dimensional volume data is converged, locally registering the two-dimensional ultrasonic image and the three-dimensional volume data in the residual heap image and the template heap image, detecting whether the three-dimensional volume data after local registration is converged, if so, outputting the three-dimensional volume data, otherwise, continuously constructing the three-dimensional volume data, therefore, the three-dimensional ultrasonic reconstruction of multi-angle scanning is realized, the observation limitation of doctors caused by single-angle scanning is solved, the artifacts in the three-dimensional ultrasonic image are effectively removed, and the reconstruction quality of the three-dimensional ultrasonic image is improved.
Example two:
fig. 2 shows a structure of a three-dimensional ultrasonic reconstruction apparatus according to a second embodiment of the present invention, and for convenience of description, only the parts related to the second embodiment of the present invention are shown, which include:
and a global registration unit 21, configured to set a pile image with the smallest motion error among all pile images as a template pile image when receiving the pile images scanned by the multi-angle ultrasound, and to globally register the remaining pile images among all pile images with the template pile image.
In the embodiment of the invention, ultrasonic scanning is carried out at different angles to obtain the pile images obtained by scanning at different angles. A pile image with the minimum motion error can be selected from all pile images as a template pile image by adopting a method based on motion amount estimation, and then the residual pile images in all pile images are subjected to global registration with the template pile image, so that each residual pile image is aligned with the template pile image, and the artifacts in the process of reconstructing the ultrasonic image are reduced.
In the embodiment of the present invention, the process of selecting a pile image with the smallest motion error from all pile images as the template pile image can be implemented by the following steps:
(1) and vectorizing each two-dimensional ultrasonic image to obtain a matrix corresponding to each pile image.
In the embodiment of the present invention, the matrix corresponding to each pile image may be represented as a ═ vec (I)1)；...；vec(Ik)]∈Rm*kWherein, vec (I)j) Represents the j (j e 1, a., k) th two-dimensional ultrasound image I in the pile imagejAs a column vector.
(2) And generating an observation matrix corresponding to each pile image according to the matrix corresponding to each pile image, and performing singular value decomposition on the observation matrix of each pile image.
In an embodiment of the invention, the observation matrix of the heap image is D ═ a + E, E is the identity matrix, D is full rank and D ∈ Rm*kSo that the decreasing order of the singular values of D is s1≥s2≥...≥skNot less than 0, D can be decomposed by singular value to obtain D ═ USVT(U∈Rm*k，S∈Rk*k，V∈Rk*k) And S is a diagonal matrix with elements on the diagonal being singular values.
(3) And generating a matrix similar to the observation matrix according to the singular value decomposition result of the observation matrix, and determining the template pile images in all pile images according to the observation matrix and the matrix similar to the observation matrix.
In the embodiment of the invention, the slave units can respectively receive Um*k、Sk*k、Vk*kR column of sub-matrix U 'is taken from the upper left corner of the matrix'm*r、S′r*r、U′r*kFrom D '═ U' S 'V'TD and D 'are very similar, and the degree of similarity between D and D' can be measured using the Frobenius norm (Frobenius norm):
wherein | D | is the frobenius norm of D, | D '| is the frobenius norm of D', and | D-D '| is used to represent the degree of similarity between D and D'. Therefore, the relative similarity difference between D and D' can be expressed as:
at the point of satisfying deltarUnder the condition that the image is smaller than the preset threshold value, each pile image can obtain the corresponding deltarThe smallest r value, and then the pile image with the smallest r value among all the pile images is selected as the template pile image.
Preferably, for greater intuition and convenience, the above process is simplified by using a variable ω, r · δ, for measuring the motion estimatorrAnd calculating the minimum value of omega corresponding to each pile image, and setting the pile image with the minimum omega as a template pile image.
In the embodiment of the invention, when the residual pile images are in global registration with the template pile images, a rigid registration mode can be adopted to preliminarily calculate the global transformation matrix, then the residual pile images are registered through the global transformation matrix, and specifically, the residual pile images S are registered through the following formulaiAnd (3) carrying out global registration:
S′i＝Tglobal·Sii ≠ T, where TglobalFor global transformation matrix, STRepresenting the template pile image, S when i ≠ TiRepresents the ith residual pile image, S'iRepresenting the registered remaining stack images. After registration, calculating the measure between the residual heap image and the template heap image, judging whether the measure meets a preset first measure threshold value, if so, finishing registration of the heap image, otherwise, optimizing the global transformation matrix by a preset optimization method, and registering the heap image by the optimized global transformation matrix.
And the three-dimensional reconstruction unit 22 is configured to construct three-dimensional volume data according to the residual pile images and the template pile images in a preset volume reconstruction mode based on the point spread function.
In the embodiment of the invention, when the three-dimensional volume data is constructed, pixels on the two-dimensional ultrasonic image in the residual pile image and the template pile image need to be converted into voxels on the three-dimensional volume data, the pixels on the two-dimensional ultrasonic image and the voxels on the three-dimensional volume data are not well aligned, one pixel point on the two-dimensional ultrasonic image can influence the values of a plurality of voxel points on the three-dimensional volume data, in order to reconstruct the three-dimensional volume data more accurately, a volume reconstruction method based on a point spread function can be adopted, and each reconstruction process is a process of updating the voxel values on the three-dimensional volume data by using the pixels on the two-dimensional ultrasonic image. After the global registration of the residual pile images and the template pile images, the three-dimensional volume data is firstly reconstructed by adopting a volume reconstruction method based on a point spread function, the three-dimensional volume data which is firstly reconstructed needs to be updated, locally registered and the like subsequently, and if the three-dimensional volume data obtained through the operations is not converged, the three-dimensional volume data is reconstructed again (namely the three-dimensional volume data is updated), updated, locally registered and the like until the final three-dimensional volume data is converged.
In the embodiment of the invention, the slave pixel point psTo the voxel point prThe spatial transformation process of (a) may be expressed as:
wherein, WsFor transforming the pile image from the image coordinate system to the transformation matrix of the world coordinate system, WrTransformation matrix for transforming a three-dimensional reconstruction from a target coordinate system to a world coordinate system, TtotalFor the registration matrix, T is the registration matrix at the time of the primary reconstruction since no local registration has been performed yettotal＝TglobalDuring subsequent multiple reconstructions, Ttotal＝Tglobal·Tlocal，TlocalFor local transformation matrix for local registration, letThen p iss＝F·pr. Therefore, the pixels on the two-dimensional ultrasound image in the remaining pile image and the template pile image can be converted to three-dimensional volume data according to the spatial conversion process, a piece of three-dimensional volume data is obtained preliminarily, and then the three-dimensional volume updating unit 23 performs an operation of updating the three-dimensional volume data.
When the three-dimensional volume data is reconstructed (or updated) by using the point spread function-based volume reconstruction method at the subsequent nth time, the update formula of the voxel value can be expressed as:
the PSF is a point spread function and is a pixel point obtained by space conversion of an original voxel point on a three-dimensional body. Wherein, the point spread function can be three-dimensional Gaussian point spread function:
and the three-dimensional volume updating unit 23 is configured to update the three-dimensional volume data in a preset volume reconstruction mode based on the kernel regression function, and determine whether the updated three-dimensional volume data converges.
In the embodiment of the invention, since the sampling data of the three-dimensional ultrasound is generally sparse, some blank areas without pixels are still present in the three-dimensional volume data after reconstruction or update, and therefore, each pixel point on the three-dimensional volume data is updated by adopting a volume reconstruction mode based on a kernel regression function.
In the embodiment of the invention, the mathematical observation model adopting the kernel regression function-based body reconstruction mode is as follows:
Yi＝r(Xi)+εiwhere, i ═ 1.. said, M is the number of all voxel points on the three-dimensional volume data, and r (·) is the kernel regression function Xi＝(Xi0，Xi1，Xi2) Is the three-dimensional coordinate of a voxel point on the three-dimensional volume data, r (X)i) What is obtained is the voxel point XiIdeal voxel value of, YiIs a voxel point XiActual voxel value of epsiloniMean difference is 0, variance is sigma2Gaussian noise. Sequentially setting voxel points on the three-dimensional data as voxel points to be updated, and when the current voxel point X to be updated is far away from the voxel point XiWhen it is very close, the voxel point X can be obtained according to the Taylor formula of N-th orderiThe ideal voxel values of are:
according to the minimization of the second multiplication, the N-level Taylor formula and the mathematical observation model, the optimization problem can be obtained:
wherein m is a neighborhood internal voxel point X of the voxel point X to be obtainediIs a predetermined smoothing parameter, K (·) is a predetermined kernel function, the kernel function K (·) is usually an exponential function or gaussian function, and it is required to satisfy ^ tk (t) dt ═ 0, [ integral ] t ^2K (t) dt ═ c. The above optimization problem can be represented by a matrix:
wherein Y is all voxel points XiY ═ Y1，Y2，...，Ym]T，Is all βiSet of vectors consisting of W ═ diag [ K (X)0-X)，K(X1-X)，...，K(Xm-X)]Is a diagonal matrix with elements K (-) at the diagonal and zero for the other elements,solving the optimization problem expressed by the matrix according to a least square method can obtain:
wherein, is the voxel estimation value of the voxel point X, i.e. the voxel value that is finally used to update the voxel point X to be updated.
In the embodiment of the present invention, the maximum update times for updating all voxel points on the three-dimensional volume data in a kernel regression function-based volume reconstruction method may be preset, and after once updating all voxels on the three-dimensional volume point, whether the updated three-dimensional volume data is converged may be determined by determining whether the current update times exceeds the maximum update times. In addition, whether the updated three-dimensional volume data converges may also be determined by whether an error between two three-dimensional volume data before and after the update exceeds a preset update error threshold.
And the local registration unit 24 is configured to locally register the residual stack image and the template stack image with the three-dimensional volume data when the three-dimensional volume data is converged, and otherwise, the three-dimensional volume updating unit 23 performs an operation of updating the three-dimensional volume data in a preset volume reconstruction mode based on a kernel regression function.
In the embodiment of the present invention, when the updated three-dimensional volume data converges, the two-dimensional ultrasound images in the remaining pile images and the template pile images are respectively locally registered with the three-dimensional volume data to adjust the spatial correspondence between each two-dimensional ultrasound image and the three-dimensional volume data, otherwise, the current update frequency is increased by one, and the three-dimensional volume updating unit 23 performs an operation of updating the three-dimensional volume data in a preset volume reconstruction mode based on a kernel regression function.
In the embodiment of the invention, when the two-dimensional ultrasonic images in the residual pile images and the template pile images are respectively locally registered with the three-dimensional volume data, the local transformation matrix T can be calculated by adopting a non-rigid registration modelocalAnd registering the two-dimensional ultrasonic image through the local transformation matrix:
wherein j is 1jRepresenting the jth two-dimensional ultrasound image, l ', in the remaining or template pile image'jIs a pair IjAnd k represents the number of the two-dimensional ultrasonic images in the residual pile images or the template pile images. And after registration, calculating the measurement between the two-dimensional ultrasonic image and the three-dimensional volume data, judging whether the measurement meets a preset second measurement threshold, if so, finishing registration of the two-dimensional ultrasonic image, otherwise, optimizing the local transformation matrix by a preset optimization method, and continuously registering the two-dimensional ultrasonic image by the optimized global transformation matrix.
And the volume data output unit 25 is configured to determine whether the locally registered three-dimensional volume data converges, if so, output the three-dimensional volume data, and otherwise, the three-dimensional reconstruction unit 22 performs an operation of constructing the three-dimensional volume data in a preset volume reconstruction mode based on a point spread function.
In the embodiment of the present invention, the maximum number of times of constructing the three-dimensional volume data by the volume reconstruction method based on the point spread function may be preset, whether the three-dimensional volume data is converged is determined by judging whether the current number of times of construction exceeds the maximum number of times of construction, if so, the three-dimensional volume data is output, otherwise, the current number of times of construction is increased by one, and the three-dimensional reconstruction unit 22 performs an operation of constructing the three-dimensional volume data by the preset volume reconstruction method based on the point spread function. In addition, whether the three-dimensional reconstruction volume data are converged can be determined by judging whether the error between the three-dimensional volume data obtained by two times of construction is smaller than a preset reconstruction error threshold value.
Preferably, as shown in fig. 3, the global registration unit 21 includes:
the template determining unit 311 is configured to calculate a motion estimator corresponding to each heap image according to a preset frobenius norm, and set a heap image with the minimum motion estimator among all the heap images as a template heap image;
a global registration subunit 312, configured to perform global registration on the residual heap images in all the heap images and the template heap image according to a preset global transformation matrix, and calculate a measure between the residual heap images after the global registration and the template heap image; and
and the global registration judging unit 313 is configured to judge whether the measure between the residual pile images and the template pile images meets a preset first measure threshold, if yes, the global registration of the residual pile images and the template pile images is ended, otherwise, the global transformation matrix is optimized, and the global registration sub-unit 312 performs an operation of globally registering the residual pile images in all the pile images and the template pile images.
Preferably, the local registration unit 24 includes:
a local registration subunit 341, configured to locally register the two-dimensional ultrasound image and the three-dimensional volume data in the remaining pile image and the template pile image according to a preset local transformation matrix, and calculate a measure between the two-dimensional ultrasound image and the three-dimensional volume data; and
a local registration judging unit 342, configured to judge whether the measure between the two-dimensional ultrasound image and the three-dimensional volume data meets a preset second measure threshold, if yes, ending the local registration of the two-dimensional ultrasound image and the three-dimensional volume data, otherwise, optimizing the local transformation matrix, and executing an operation of locally registering the two-dimensional ultrasound image and the three-dimensional volume data in the residual pile image and the template pile image by the local registration subunit 341.
In the embodiment of the invention, the pile image with the minimum motion error is selected from the pile images scanned by the multi-angle ultrasonic as the template pile image, the rest pile images and the template pile image are subjected to global registration, constructing three-dimensional reconstruction data according to the residual heap image and the template heap image after global registration, updating the three-dimensional volume data for multiple times by adopting a nuclear regression mode until the three-dimensional volume data is converged, locally registering the two-dimensional ultrasonic image and the three-dimensional volume data in the residual heap image and the template heap image, detecting whether the three-dimensional volume data after local registration is converged, if so, outputting the three-dimensional volume data, otherwise, continuously constructing the three-dimensional volume data, therefore, the three-dimensional ultrasonic reconstruction of multi-angle scanning is realized, the observation limitation of doctors caused by single-angle scanning is solved, the artifacts in the three-dimensional ultrasonic image are effectively removed, and the reconstruction quality of the three-dimensional ultrasonic image is improved.
In the embodiment of the present invention, each unit of a three-dimensional ultrasound reconstruction apparatus may be implemented by a corresponding hardware or software unit, and each unit may be an independent software or hardware unit, or may be integrated into a software or hardware unit, which is not limited herein.
Example three:
fig. 4 shows a structure of an image processing apparatus provided in a third embodiment of the present invention, and only a part related to the third embodiment of the present invention is shown for convenience of explanation.
The image processing apparatus 4 of the embodiment of the present invention includes a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. The processor 40, when executing the computer program 42, implements the steps in the above-described method embodiments, such as the steps S101 to S107 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functionality of the units in the above-described apparatus embodiments, such as the units 21 to 25 shown in fig. 2.
In the embodiment of the invention, the pile image with the minimum motion error is selected from the pile images scanned by the multi-angle ultrasonic as the template pile image, the rest pile images and the template pile image are subjected to global registration, constructing three-dimensional reconstruction data according to the residual heap image and the template heap image after global registration, updating the three-dimensional volume data for multiple times by adopting a nuclear regression mode until the three-dimensional volume data is converged, locally registering the two-dimensional ultrasonic image and the three-dimensional volume data in the residual heap image and the template heap image, detecting whether the three-dimensional volume data after local registration is converged, if so, outputting the three-dimensional volume data, otherwise, continuously constructing the three-dimensional volume data, therefore, the three-dimensional ultrasonic reconstruction of multi-angle scanning is realized, the observation limitation of doctors caused by single-angle scanning is solved, the artifacts in the three-dimensional ultrasonic image are effectively removed, and the reconstruction quality of the three-dimensional ultrasonic image is improved.
Example four:
in an embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps in the above-described method embodiment, for example, steps S101 to S107 shown in fig. 1. Alternatively, the computer program may be adapted to perform the functions of the units of the above-described apparatus embodiments, such as the functions of the units 21 to 25 shown in fig. 2, when executed by the processor.
In the embodiment of the invention, the pile image with the minimum motion error is selected from the pile images scanned by the multi-angle ultrasonic as the template pile image, the rest pile images and the template pile image are subjected to global registration, constructing three-dimensional reconstruction data according to the residual heap image and the template heap image after global registration, updating the three-dimensional volume data for multiple times by adopting a nuclear regression mode until the three-dimensional volume data is converged, locally registering the two-dimensional ultrasonic image and the three-dimensional volume data in the residual heap image and the template heap image, detecting whether the three-dimensional volume data after local registration is converged, if so, outputting the three-dimensional volume data, otherwise, continuously constructing the three-dimensional volume data, therefore, the three-dimensional ultrasonic reconstruction of multi-angle scanning is realized, the observation limitation of doctors caused by single-angle scanning is solved, the artifacts in the three-dimensional ultrasonic image are effectively removed, and the reconstruction quality of the three-dimensional ultrasonic image is improved.
The computer readable storage medium of the embodiments of the present invention may include any entity or device capable of carrying computer program code, a recording medium, such as a ROM/RAM, a magnetic disk, an optical disk, a flash memory, or the like.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A method of three-dimensional ultrasound reconstruction, the method comprising the steps of:
when pile images scanned by multi-angle ultrasound are received, setting the pile image with the minimum motion error in all the pile images as a template pile image, and globally registering the rest pile images in all the pile images with the template pile image;
constructing three-dimensional volume data through a preset volume reconstruction mode based on a point spread function according to the residual pile images and the template pile images;
updating the three-dimensional volume data through a preset volume reconstruction mode based on a kernel regression function, and judging whether the updated three-dimensional volume data is converged;
when the three-dimensional volume data are converged, respectively and locally registering the residual pile images and the template pile images with the three-dimensional volume data, otherwise, skipping to a step of updating the three-dimensional volume data in a preset volume reconstruction mode based on a kernel regression function;
and judging whether the three-dimensional volume data after local registration is converged, if so, outputting the three-dimensional volume data, and otherwise, skipping to the step of constructing the three-dimensional volume data in a preset volume reconstruction mode based on a point spread function.
2. The method of claim 1, wherein the step of setting a pile image of all pile images with a minimum motion error as a template pile image and globally registering the remaining pile images of all pile images with the template pile image comprises:
calculating a motion estimator corresponding to each pile image through a preset Frobenius norm, and setting the pile image with the minimum motion estimator in all the pile images as a template pile image;
globally registering residual heap images in all the heap images with the template heap image according to a preset global transformation matrix, and calculating the measure between the residual heap images and the template heap image after global registration;
judging whether the measure between the residual pile images and the template pile images meets a preset first measure threshold value, if so, ending the overall registration of the residual pile images and the template pile images, otherwise, optimizing the overall transformation matrix, and jumping to the step of overall registration of the residual pile images in all pile images and the template pile images according to the preset overall transformation matrix.
3. The method of claim 1, wherein the step of locally registering the residual stack image, the template stack image, respectively, with the three-dimensional volumetric data comprises:
locally registering the two-dimensional ultrasonic images in the residual pile images and the template pile images with the three-dimensional volume data according to a preset local transformation matrix, and calculating the measure between the two-dimensional ultrasonic images and the three-dimensional volume data;
judging whether the measure between the two-dimensional ultrasonic image and the three-dimensional volume data meets a preset second measure threshold value, if so, ending the local registration of the two-dimensional ultrasonic image and the three-dimensional volume data, otherwise, optimizing the local transformation matrix, and jumping to the step of locally registering the two-dimensional ultrasonic image and the three-dimensional volume data in the residual pile image and the template pile image according to the preset local transformation matrix.
4. The method of claim 1, wherein the step of constructing three-dimensional volume data by a predetermined point spread function-based volume reconstruction method comprises:
calculating and updating voxel values on the three-dimensional volume data according to the residual pile images, the pixel values on the two-dimensional ultrasonic images in the template pile images and the volume reconstruction mode based on the point spread function, wherein the calculation formula of the voxel values is as follows:
wherein said V is said three-dimensional volume data, saidThe three-dimensional volume data calculated for the nth iteration update is at the voxel point prA voxel value of (a), saidFor the two-dimensional ultrasonic image at a pixel point psA pixel value of (a), saidIs the voxel pr nAnd the PSF is the point spread function.
5. The method of claim 1, wherein the step of updating the three-dimensional volume data by a predetermined kernel regression function based volume reconstruction method comprises:
sequentially setting each voxel point on the three-dimensional data as a voxel point to be updated, calculating a voxel estimation value corresponding to the voxel point to be updated according to the voxel point in the neighborhood of the voxel point to be updated, the voxel values of all the voxel points in the neighborhood and the kernel regression function-based volume reconstruction mode, and setting the voxel estimation value as the voxel value of the voxel point to be updated, wherein the calculation formula of the voxel estimation value is as follows:
wherein, theIs the voxel estimated value of the voxel point X to be updated,said XiIs a voxel point in the X neighborhood, wherein i is 1iA set of vectors formed by voxel values of (i.e. Y ═ Y1,Y2,...,Ym]TWherein W is diag [ K (X)0-X),K(X1-X),...,K(Xm-X)]K (-) is a predetermined kernel function, the
6. A three-dimensional ultrasound reconstruction apparatus, characterized in that the apparatus comprises:
the global registration unit is used for setting a pile image with the minimum motion error in all pile images as a template pile image and registering the rest pile images in all the pile images with the template pile image globally when the pile images scanned by multi-angle ultrasound are received;
the three-dimensional reconstruction unit is used for constructing three-dimensional volume data through a preset volume reconstruction mode based on a point spread function according to the residual pile images and the template pile images;
the three-dimensional body updating unit is used for updating the three-dimensional body data through a preset body reconstruction mode based on a kernel regression function and judging whether the updated three-dimensional body data is converged;
the local registration unit is used for locally registering the residual pile images and the template pile images with the three-dimensional volume data when the three-dimensional volume data are converged, otherwise, the three-dimensional volume updating unit executes the operation of updating the three-dimensional volume data in a preset volume reconstruction mode based on a kernel regression function; and
and the volume data output unit is used for judging whether the three-dimensional volume data after local registration is converged, if so, outputting the three-dimensional volume data, and otherwise, executing the operation of constructing the three-dimensional volume data in a preset volume reconstruction mode based on a point spread function by the three-dimensional reconstruction unit.
7. The apparatus of claim 6, wherein the global registration unit comprises:
the template determining unit is used for calculating the motion estimation amount corresponding to each pile image through a preset Frobenius norm, and setting the pile image with the minimum motion estimation amount in all the pile images as a template pile image;
the global registration subunit is used for globally registering the residual pile images in all the pile images with the template pile images according to a preset global transformation matrix and calculating the measure between the residual pile images and the template pile images after global registration; and
and the global registration judging unit is used for judging whether the measure between the residual pile image and the template pile image meets a preset first measure threshold value, if so, the global registration of the residual pile image and the template pile image is ended, otherwise, the global transformation matrix is optimized, and the global registration subunit performs the operation of the global registration of the residual pile image and the template pile image in all the pile images.
8. The apparatus of claim 6, wherein the local registration unit comprises:
a local registration subunit, configured to locally register the two-dimensional ultrasound image in the residual pile image and the template pile image with the three-dimensional volume data according to a preset local transformation matrix, and calculate a measure between the two-dimensional ultrasound image and the three-dimensional volume data; and
a local registration judging unit, configured to judge whether a measure between the two-dimensional ultrasound image and the three-dimensional volume data satisfies a preset second measure threshold, if so, end local registration of the two-dimensional ultrasound image and the three-dimensional volume data, otherwise, optimize the local transformation matrix, and perform, by the local registration subunit, an operation of locally registering the two-dimensional ultrasound image and the three-dimensional volume data in the residual pile image and the template pile image.
9. An image processing apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711421838.8A CN108198235B (en) | 2017-12-25 | 2017-12-25 | Three-dimensional ultrasonic reconstruction method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711421838.8A CN108198235B (en) | 2017-12-25 | 2017-12-25 | Three-dimensional ultrasonic reconstruction method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108198235A true CN108198235A (en) | 2018-06-22 |
| CN108198235B CN108198235B (en) | 2022-03-04 |
Family
ID=62584057
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711421838.8A Active CN108198235B (en) | 2017-12-25 | 2017-12-25 | Three-dimensional ultrasonic reconstruction method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108198235B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109345629A (en) * | 2018-08-08 | 2019-02-15 | 安徽慧软科技有限公司 | A kind of 3 d medical images are fuzzy to highlight display methods |
| CN111210427A (en) * | 2020-01-17 | 2020-05-29 | 云南大学 | Time-change-based method for analyzing post-operation shrinkage of in-vivo light-weight patch |
| CN112258627A (en) * | 2020-09-18 | 2021-01-22 | 中国科学院计算技术研究所 | Three-dimensional reconstruction system for local fault |
| CN112672696A (en) * | 2018-09-14 | 2021-04-16 | 皇家飞利浦有限公司 | System and method for tracking tools in ultrasound images |
| CN112971982A (en) * | 2019-12-12 | 2021-06-18 | 珠海横乐医学科技有限公司 | Operation navigation system based on intrahepatic vascular registration |
| WO2023274413A1 (en) * | 2021-07-01 | 2023-01-05 | 先临三维科技股份有限公司 | Three-dimensional scanning system, auxiliary member, processing method and apparatus, device and medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101111194A (en) * | 2004-12-24 | 2008-01-23 | 株式会社Yd | Cardiac valve data measuring method and device |
| CN101238993A (en) * | 2008-02-01 | 2008-08-13 | 哈尔滨工业大学 | Medical ultrasound image registration method based on integer lifting wavelet multiresolution analysis |
| US20110109627A1 (en) * | 2009-10-05 | 2011-05-12 | Siemens Corporation | Method and Apparatus for Three-Dimensional Visualization and Analysis for Automatic Non-Destructive Examination of a Solid Rotor using Ultrasonic Phased Array |
| CN104077791A (en) * | 2014-05-22 | 2014-10-01 | 南京信息工程大学 | Joint reconstruction method for multiple dynamic contrast enhancement nuclear magnetic resonance images |
| CN104584074A (en) * | 2012-08-30 | 2015-04-29 | 皇家飞利浦有限公司 | Coupled segmentation in 3D conventional ultrasound and contrast-enhanced ultrasound images |
-
2017
- 2017-12-25 CN CN201711421838.8A patent/CN108198235B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101111194A (en) * | 2004-12-24 | 2008-01-23 | 株式会社Yd | Cardiac valve data measuring method and device |
| CN101238993A (en) * | 2008-02-01 | 2008-08-13 | 哈尔滨工业大学 | Medical ultrasound image registration method based on integer lifting wavelet multiresolution analysis |
| US20110109627A1 (en) * | 2009-10-05 | 2011-05-12 | Siemens Corporation | Method and Apparatus for Three-Dimensional Visualization and Analysis for Automatic Non-Destructive Examination of a Solid Rotor using Ultrasonic Phased Array |
| CN104584074A (en) * | 2012-08-30 | 2015-04-29 | 皇家飞利浦有限公司 | Coupled segmentation in 3D conventional ultrasound and contrast-enhanced ultrasound images |
| CN104077791A (en) * | 2014-05-22 | 2014-10-01 | 南京信息工程大学 | Joint reconstruction method for multiple dynamic contrast enhancement nuclear magnetic resonance images |
Non-Patent Citations (8)
| Title |
|---|
| BERNHARD KAINZ;MARKUS STEINBERGER;WOLFGANG WEIN;ET AL.: "Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices", 《IEEE TRANSACTIONS ON MEDICAL IMAGING》 * |
| WEN T , LI L , ZHU Q , ET AL.: "GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging", 《ULTRASONIC IMAGING》 * |
| WEN T , LIU R , LIU L , ET AL.: "GPU-based volume reconstruction for freehand 3D ultrasound imaging", 《ENGINEERING IN MEDICINE & BIOLOGY SOCIETY》 * |
| WEN T X, YANG F, GU J.: "An adaptive kernel regression method", 《NEUROCOMPUTING》 * |
| WENJIAN Q , TIEXIANG W , ZHEN Z , ET AL.: "A pilot study on simulation of Ultrasound from MRI using ray-based model", 《IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL & HEALTH INFORMATICS》 * |
| 杨金耀; 李德来: "一种扇形扫描三维超声成像系统三维", 《中国医疗器械信息》 * |
| 温铁祥，秦文健，辜嘉: "一种基于光学定位的自由式三维超声系统研发", 《集成技术》 * |
| 范阳阳，倪倩，温铁祥等: "Freehand 三维超声体数据重建的最新研究进展", 《中国生物医学工程学报》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109345629A (en) * | 2018-08-08 | 2019-02-15 | 安徽慧软科技有限公司 | A kind of 3 d medical images are fuzzy to highlight display methods |
| CN112672696A (en) * | 2018-09-14 | 2021-04-16 | 皇家飞利浦有限公司 | System and method for tracking tools in ultrasound images |
| CN112971982A (en) * | 2019-12-12 | 2021-06-18 | 珠海横乐医学科技有限公司 | Operation navigation system based on intrahepatic vascular registration |
| CN112971982B (en) * | 2019-12-12 | 2022-08-19 | 珠海横乐医学科技有限公司 | Operation navigation system based on intrahepatic vascular registration |
| CN111210427A (en) * | 2020-01-17 | 2020-05-29 | 云南大学 | Time-change-based method for analyzing post-operation shrinkage of in-vivo light-weight patch |
| CN111210427B (en) * | 2020-01-17 | 2023-03-10 | 云南大学 | Time-change-based method for analyzing post-operation shrinkage of in-vivo light-weight patch |
| CN112258627A (en) * | 2020-09-18 | 2021-01-22 | 中国科学院计算技术研究所 | Three-dimensional reconstruction system for local fault |
| CN112258627B (en) * | 2020-09-18 | 2023-09-15 | 中国科学院计算技术研究所 | Local fault three-dimensional reconstruction system |
| WO2023274413A1 (en) * | 2021-07-01 | 2023-01-05 | 先临三维科技股份有限公司 | Three-dimensional scanning system, auxiliary member, processing method and apparatus, device and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108198235B (en) | 2022-03-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108198235B (en) | Three-dimensional ultrasonic reconstruction method, device, equipment and storage medium | |
| US10699410B2 (en) | Automatic change detection in medical images | |
| US9179890B2 (en) | Model-based positioning for intracardiac echocardiography volume stitching | |
| US10026016B2 (en) | Tracking and representation of multi-dimensional organs | |
| US8121362B2 (en) | Registration of medical images using learned-based matching functions | |
| EP3879487B1 (en) | Matching patient images and images of an anatomical atlas | |
| US11672505B2 (en) | Correcting probe induced deformation in an ultrasound fusing imaging system | |
| JP2010119654A (en) | Image processing apparatus, image processing method, program, and storage medium | |
| WO2014064063A1 (en) | Determining an anatomical atlas | |
| CN115830016B (en) | Medical image registration model training method and equipment | |
| CN104933672B (en) | Method based on quick convex optimized algorithm registration three dimensional CT with ultrasonic liver image | |
| JP2022111705A (en) | Leaning device, image processing apparatus, medical image pick-up device, leaning method, and program | |
| JP2022111704A (en) | Image processing apparatus, medical image pick-up device, image processing method, and program | |
| Dam et al. | Prostate shape modeling based on principal geodesic analysis bootstrapping | |
| US20220328195A1 (en) | System and method for heart strain determination | |
| JP5943267B2 (en) | Personal model data generation method, generation program, and generation system | |
| EP3910597A1 (en) | Body representations | |
| US11138736B2 (en) | Information processing apparatus and information processing method | |
| CN113850710A (en) | Cross-modal medical image accurate conversion method | |
| Takahashi et al. | Segmentation method for cardiac region in CT images based on active shape model | |
| CN112819789A (en) | Apparatus and method for cardiac assessment | |
| EP2912632B1 (en) | Determining an anatomical atlas | |
| EP4336460A1 (en) | Method and device for generating a complete 3d representation of an object from a partial signal | |
| Mao | Three-dimensional Ultrasound Fusion for Transesophageal Echocardiography | |
| Chen et al. | A novel merged strategy with deformation field reconstruction for constructing statistical shape models |
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 |