WO2018133003A1 - Ct三维重建方法及系统 - Google Patents

Ct三维重建方法及系统 Download PDF

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Publication number
WO2018133003A1
WO2018133003A1 PCT/CN2017/071694 CN2017071694W WO2018133003A1 WO 2018133003 A1 WO2018133003 A1 WO 2018133003A1 CN 2017071694 W CN2017071694 W CN 2017071694W WO 2018133003 A1 WO2018133003 A1 WO 2018133003A1
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dimensional
projection image
reconstructed
volume data
iteration
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PCT/CN2017/071694
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English (en)
French (fr)
Inventor
陈垦
王澄
秦文健
熊璟
谢耀钦
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深圳先进技术研究院
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Priority to PCT/CN2017/071694 priority Critical patent/WO2018133003A1/zh
Publication of WO2018133003A1 publication Critical patent/WO2018133003A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects

Definitions

  • Embodiments of the present disclosure relate to image processing techniques, for example, to a Computed Tomography (CT) three-dimensional reconstruction method and system.
  • CT Computed Tomography
  • the cone beam CT device using the flat panel detector technology has advantages in imaging quality and imaging efficiency, so how to use the cone Fast and accurate CT 3D reconstruction under the beam CT model has become a very important issue.
  • FDK Feldkamp, Davis, Kress
  • the FDK algorithm is used as the filtered back projection algorithm.
  • the calculation speed is fast and the system resource requirements are low, the reconstructed image quality is poor, and there are artifacts in the image. Affects visualization and doctor diagnosis.
  • Algebraic Reconstruction Technique (ART), although it can solve the image quality problem of the filtered back projection algorithm, but ART needs to calculate the projection matrix, the calculation amount is large, the calculation efficiency is low, and the storage hardware is saved when the projection matrix is saved. The requirements are also high, limiting the clinical application of ART.
  • Embodiments of the present disclosure provide a CT three-dimensional reconstruction method and system, which can implement CT three-dimensional reconstruction.
  • an embodiment of the present disclosure provides a computed tomography CT three-dimensional reconstruction method, the method comprising:
  • each lower computer Controlling each lower computer to calculate a projection matrix of each pixel point in the received two-dimensional projection image, and storing the projection matrix in each memory of each lower computer;
  • the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is used as the CT three-dimensional reproduction result.
  • controlling each lower computer to calculate a projection matrix of each pixel point in the received two-dimensional projection image comprises:
  • each of the lower computers determines whether a line equation of each pixel intersects each voxel in the three-dimensional volume data to be reconstructed; if a line equation of the pixel points and a voxel in the three-dimensional volume data to be reconstructed Intersecting, obtaining a length value of a line connecting the line equations in the voxel; if the line equation of the pixel points does not intersect the voxel in the three-dimensional volume data to be reconstructed, a line connecting the line points of the line equation having a length value of 0 in the voxel;
  • controlling each of the lower computers to calculate a projection matrix of each pixel point in the received two-dimensional projection image comprises:
  • each lower computer is controlled to calculate an iterative attenuation component corresponding to the two-dimensional projection image according to the projection matrix and a current iteration state of the three-dimensional volume data to be reconstructed, and return the iterative attenuation component, including :
  • N is the number of frames of the two-dimensional projection image
  • T i is the iterative attenuation component corresponding to the two-dimensional projection image of the ith frame
  • M is the total number of pixels in the two-dimensional projection image
  • a ij is the projection matrix of the jth pixel point in the 2D projection image of the i-th frame
  • b ij is the pixel value of the jth pixel in the 2D projection image of the ith frame
  • x n is the current iteration state of the 3D volume data to be reconstructed in the nth iteration.
  • updating the current iteration state of the three-dimensional volume data to be reconstructed according to the iterative attenuation component returned by each lower-level machine includes:
  • Updating a current iteration state of the three-dimensional volume data to be reconstructed is x n+1 ;
  • x n+1 is the current iteration state of the three-dimensional volume data to be reconstructed in the n+1th iteration
  • ⁇ and ⁇ are preset weight values
  • R( ⁇ ) is a preset constraint term equation.
  • the two-dimensional projection image acquired by the CT at at least two rotation angles of the X light source is sent to at least two lower computers, including at least one of the following:
  • Two-dimensional projection images at the same rotation angle are respectively sent to the at least two lower-level machines.
  • an embodiment of the present disclosure further provides a CT three-dimensional reconstruction system, where the system includes:
  • An image acquisition module configured to send a two-dimensional projection image acquired by the CT at at least two rotation angles of the X light source to at least two lower-level machines;
  • a projection matrix receiving module configured to control each lower computer to calculate a projection matrix of each pixel in the received two-dimensional projection image, and store the projection matrix in each memory of each lower computer;
  • a component calculation module configured to control each of the lower computers to calculate an iterative attenuation component corresponding to the two-dimensional projection image according to the projection matrix and a current iteration state of the three-dimensional volume data to be reconstructed, and return the iterative attenuation component ;
  • An iterative state update module configured to update a current iteration state of the three-dimensional volume data to be reconstructed according to the iterative attenuation component returned by each lower-level machine;
  • the iterative return module is set to determine whether the iteration termination condition is satisfied. If the iterative termination condition is not met, the return execution control is performed according to the projection matrix and the current iteration state of the 3D volume data to be reconstructed, and is calculated and received.
  • the two-dimensional projection image corresponds to an iteratively attenuating component and returns the operation of the iterative attenuation component until a predetermined iteration termination condition is satisfied;
  • the recurring result determining module is set to be the current iterative state of the three-dimensional volume data to be reconstructed after the iteration is terminated, if the iterative termination condition is satisfied, as the CT three-dimensional reproduction result.
  • the projection matrix receiving module is configured to: control each of the lower-level machines to construct each of the two-dimensional projection images of the X-ray source at the rotation angle according to the rotation angle corresponding to the received two-dimensional projection image The line equation of the pixel;
  • each of the lower computers determines whether a line equation of each pixel intersects with each voxel in the three-dimensional volume data to be reconstructed; if a line equation of each pixel is in the three-dimensional volume data to be reconstructed Obtaining a voxel, obtaining a length value of a line connecting the line equations of each pixel in the voxel; if the line equation of each pixel is in the three-dimensional volume data to be reconstructed If the voxels do not intersect, the length of the line connecting the pixel points is 0 in the voxel;
  • the projection matrix receiving module is configured to:
  • the component calculation module is configured to:
  • N is the number of frames of the two-dimensional projection image
  • T i is an iterative attenuation component corresponding to the two-dimensional projection image of the ith frame
  • M is a pixel point of the two-dimensional projection image
  • a ij is the projection matrix of the jth pixel point in the 2D projection image of the i-th frame.
  • b ij is the pixel value of the jth pixel in the 2D projection image of the i-th frame
  • x n is the current iteration state of the 3D volume data to be reconstructed in the nth iteration.
  • the iterative state update module is configured to:
  • Updating a current iteration state of the three-dimensional volume data to be reconstructed is x n+1 ;
  • x n+1 is the current iteration state of the three-dimensional volume data to be reconstructed in the n+1th iteration
  • ⁇ and ⁇ are preset weight values
  • R( ⁇ ) is a preset constraint term equation.
  • the image acquisition module is set to at least one of the following:
  • Two-dimensional projection images at the same rotation angle are respectively sent to the at least two lower-level machines.
  • an embodiment of the present disclosure further provides a non-transitory computer readable storage medium storing computer executable instructions, the computer executable instructions being configured to perform the method described above.
  • an embodiment of the present disclosure further provides an electronic device, including:
  • At least one processor At least one processor
  • a memory communicatively coupled to the at least one processor, configured to store a program executable by the at least one processor
  • the program is executed by the at least one processor such that the at least one processor performs the CT three-dimensional reconstruction method described in the embodiments of the present disclosure.
  • the projection matrix and the iterative attenuation component of each pixel in the two-dimensional projection image are calculated by at least two lower computers, and the current iteration state of the three-dimensional volume data to be reconstructed is updated, and the iteration is terminated.
  • the current iterative state of the 3D volume data to be reconstructed is used as the CT three-dimensional reproduction result, which reduces the calculation amount of the algorithm, improves the calculation efficiency and the reusability of the calculation result.
  • FIG. 1 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 1 of the present disclosure
  • FIG. 2 is a schematic diagram of cone beam CT scanning in a CT three-dimensional reconstruction method according to an embodiment of the present disclosure
  • FIG. 3 is a schematic diagram of a three-dimensional volume data coordinate system and a projection data coordinate system in a CT three-dimensional reconstruction method according to an embodiment of the present disclosure
  • FIG. 5 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 3 of the present disclosure.
  • FIG. 6 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 4 of the present disclosure.
  • FIG. 7 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 5 of the present disclosure.
  • FIG. 8 is a schematic diagram of a CT three-dimensional reconstruction system according to Embodiment 6 of the present disclosure.
  • Embodiment 9 is a structural diagram of an electronic device in Embodiment 8 of the present disclosure.
  • Embodiment 1 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 1 of the present disclosure. This embodiment is applicable to a case where a three-dimensional reconstruction is performed under a cone beam CT model, and the method may be performed by a CT three-dimensional reconstruction system.
  • the system can be implemented by software, hardware, or software and hardware, and the system can be integrated into a device such as a computer.
  • step 110 the two-dimensional projection image acquired by the CT at at least two rotation angles of the X-ray source is transmitted to at least two lower-level machines.
  • CT is the electronic computed tomography technology, which can be divided into traditional CT, spiral CT and cone beam CT.
  • the cone beam CT has fast scanning speed, high image resolution and high radiation utilization rate.
  • the hundreds of tomographic scans of traditional CT and spiral CT are completed, and the resolution of the cone beam CT in the X, Y, and Z directions is consistent, which can reduce the volume effect commonly seen in traditional CT and spiral CT detection.
  • Present disclosure takes cone beam CT as an example to solve how to perform 3D reconstruction quickly and accurately under the cone beam CT model.
  • FIG. 2 is a schematic diagram of a cone beam CT scan in a CT three-dimensional reconstruction method according to an embodiment of the present disclosure, wherein the rays I, J, K, and L are rays emitted by the X-ray source 210 and passing through the object to be measured 220, crossing the The X-ray cone beam of the object is received by the detector 230, and the obtained data is a two-dimensional projection image at the rotation angle.
  • the X-ray source rotates through an angle and performs the same operation until the scanning of the plurality of angles is completed.
  • the size of the rotation angle and the number of selections can be determined according to requirements.
  • the projection data is stored in an N*H*W three-dimensional array as unsigned short type data.
  • FIG. 3 is a schematic diagram of a three-dimensional volume data coordinate system and a projection data coordinate system in a CT three-dimensional reconstruction method according to an embodiment of the present disclosure. Assuming that the light source rotates around the voxel X-axis, a two-dimensional projection image acquired at each rotation angle of the light source is obtained, and the two-dimensional projection image is sent to at least two lower-level machines.
  • the sending method may be that the two-dimensional projection image corresponding to the same rotation angle is sent to the same lower position machine, or the two-dimensional projection image corresponding to different rotation angles may be sent to the same lower position machine, and increasing the number of lower position machines may improve the data processing speed.
  • each lower computer is controlled to calculate a projection matrix of each pixel in the received two-dimensional projection image, and the projection matrix is stored in a memory of each of the lower computers.
  • A is the projection matrix of each pixel in the two-dimensional projection image
  • x is the three-dimensional volume data to be reconstructed
  • b is the pixel value of the projected image
  • R( ⁇ ) is the preset constraint term equation
  • ⁇ is the preset weight value. Iteratively finds x, takes the objective function F to the minimum value, and determines the three-dimensional volume data x to be reconstructed.
  • the factors that limit the application of ART algorithm in cone beam CT include: the calculation of projection matrix A is complicated; the data of positional relationship between all ray and three-dimensional volume data voxel in each frame projection is large, which requires high storage hardware and limits. The reusability of the calculation results.
  • the projection matrix A is stored in the respective lower computer, and the stored projection matrix A can be called in the subsequent cycle. At least two lower-level machines perform calculations at the same time, and the results are returned to the upper computer for accumulation, which can greatly speed up the calculation efficiency of the projection matrix.
  • each of the lower computers is controlled to calculate an iterative attenuation component corresponding to the two-dimensional projection image and return the iterative attenuation component according to the projection matrix and the current iteration state of the three-dimensional volume data to be reconstructed.
  • the iterative attenuation component is related to the current iteration state and the pixel value of the two-dimensional projection image.
  • the current iteration state is a numerical value, and the initial current iteration state may be 0, or may be a FDK algorithm.
  • the current iteration state is initially determined.
  • step 140 the current iteration state of the three-dimensional volume data to be reconstructed is updated according to the iterative attenuation component returned by each lower computer.
  • the current iteration state of the three-dimensional volume data to be reconstructed in the n+1th iteration is updated by using the returned iterative attenuation component and the nth iteration state.
  • step 150 it is determined whether the iteration termination condition is satisfied. If the iteration termination condition is not satisfied, then step 130 is performed; if the iteration termination condition is satisfied, step 160 is performed.
  • the iterative termination condition may be that the number of iterations meets the preset number of iterations, or that the difference between the two successive iteration states satisfies the preset difference.
  • step 160 the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is taken as the CT three-dimensional reproduction result.
  • the current iterative state of the three-dimensional volume data to be reconstructed after the iteration is terminated can cause the objective function to reach a maximum value (or a minimum value), and the current iteration state at this time is determined as a CT three-dimensional reproduction result of the two-dimensional projection image.
  • the projection matrix and the iterative attenuation component of each pixel in the two-dimensional projection image are calculated by at least two lower computers, and the current iteration state of the three-dimensional volume data to be reconstructed is updated, and the iteration is terminated.
  • the current iterative state of the 3D volume data to be reconstructed is used as the CT three-dimensional reproduction result, which reduces the calculation amount of the algorithm, improves the calculation efficiency and the reusability of the calculation result.
  • FIG. 4 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 2 of the present disclosure, and the embodiment of the present disclosure is based on the foregoing embodiment.
  • step 410 the two-dimensional projection image acquired by the CT at at least two rotation angles of the X-ray source is transmitted to at least two lower-level machines.
  • each lower computer is controlled to construct a connection equation of the X-ray source and each pixel point in the two-dimensional projection image at the rotation angle according to the rotation angle corresponding to the received two-dimensional projection image.
  • step 430 the lower computer is controlled to determine whether the line equation of each pixel intersects each voxel in the three-dimensional volume data to be reconstructed; if the line equation of the pixel is in the three-dimensional volume data to be reconstructed When the voxels intersect, the length value of the connection of the line equation of the pixel in the voxel is obtained; if the image If the line equation of the prime point does not intersect with the voxel in the three-dimensional volume data to be reconstructed, the length of the connection of the line equation of the pixel is 0 in the voxel.
  • step 440 controlling, by each of the lower-level machines, a projection matrix of each pixel in the two-dimensional projection image according to a length value of a line connecting the line equations of each pixel in each voxel, And storing the projection matrix in the respective memory of the lower computer.
  • the voxel is the abbreviation of the volume element, which is suitable for the fields of three-dimensional imaging and medical imaging.
  • the voxel itself does not contain the data of the position in the space (ie the coordinates), and the voxel can represent a three-dimensional region with a constant scalar or vector. .
  • For each frame of the two-dimensional projection image if it is the first loop iteration process, calculate the connection equation of the X-ray source corresponding to the two-dimensional projection image of the frame and each pixel point in the two-dimensional projection image. Determining whether the connection equation intersects with each voxel in the three-dimensional volume data to be reconstructed.
  • connection equation of the pixel intersects the voxel in the three-dimensional volume data to be reconstructed, the connection of the connection equation of the pixel is calculated.
  • the length value of the portion within the voxel, and the three-dimensional position coordinates (x, y, z) of the voxel in the three-dimensional volume data are recorded.
  • the length of the connection equation of the pixel is 0 in the voxel.
  • the length value constitutes a projection matrix of the pixel points of the current frame two-dimensional projection image, and the projection matrix is stored in the respective memory of the lower computer.
  • the projection matrix can be calculated only once, and the stored projection matrix can be used in subsequent loop iterations, so if it is not the first iteration of the loop, the step of calculating the projection matrix is skipped.
  • each of the lower computers is controlled to calculate an iterative attenuation component corresponding to the two-dimensional projection image according to a projection matrix and a current iteration state of the three-dimensional volume data to be reconstructed, and return the iterative attenuation component.
  • step 460 the current iteration state of the three-dimensional volume data to be reconstructed is updated according to the iterative attenuation component returned by each lower computer.
  • step 470 it is determined whether the preset iteration termination condition is satisfied. If the iteration termination condition is satisfied, step 480 is performed; if the iteration termination condition is not met, step 450 is returned.
  • step 480 the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is taken as the CT three-dimensional reproduction result.
  • the technical solution of the embodiment of the present disclosure calculates a line equation of a pixel point in a two-dimensional projection image corresponding to a two-dimensional projection image, according to a connection line equation of the pixel point and a three-dimensional volume data to be reconstructed.
  • the length value of the intersection of each voxel constructs the projection matrix of all the pixel points, and only one projection matrix can be calculated, and the projection matrix after the initial calculation is saved in the memory of the lower computer, and the subsequent cycle can be To use the stored projection matrix, the calculation process is simplified and the calculation efficiency is accelerated.
  • controlling, by each lower computer, the projection matrix of each pixel in the received two-dimensional projection image comprises:
  • the lower computer allocates one thread for each pixel point, and the projection matrix of each pixel point is distributedly calculated by at least two threads, and when all threads are executed, A projection matrix corresponding to all pixels of the current frame two-dimensional projection image is obtained.
  • the multi-threaded distributed computing method can improve the calculation speed and speed up the calculation efficiency of the projection matrix.
  • FIG. 5 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 3 of the present disclosure, and the embodiment of the present disclosure is based on the foregoing embodiment.
  • step 510 the two-dimensional projection image acquired by the CT at at least two rotation angles of the X light source is transmitted to at least two lower computers.
  • each lower computer is controlled to calculate a projection matrix of each pixel in the received two-dimensional projection image, and the projection matrix is stored in a memory of each of the lower computers.
  • each of the lower machines is controlled according to the formula: An iterative attenuation component corresponding to the received two-dimensional projection image is calculated.
  • N is the number of frames of the two-dimensional projection image
  • T i is the iterative attenuation component corresponding to the two-dimensional projection image of the ith frame
  • M is the total number of pixels in the two-dimensional projection image
  • a ij is the projection matrix of the jth pixel point in the 2D projection image of the i-th frame
  • b ij is the pixel value of the jth pixel point in the 2D projection image of the ith frame
  • x n is the nth iteration Rebuild the current iteration state of the 3D volume data.
  • step 540 the current iteration state of the three-dimensional volume data to be reconstructed is updated according to the iterative attenuation component returned by each lower computer.
  • step 550 it is determined whether the preset iteration termination condition is satisfied. If the iteration termination condition is satisfied, step 560 is performed; if the iteration termination condition is not met, the execution 530 is returned.
  • step 560 the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is performed. The result is reproduced for the CT three-dimensional.
  • the projection matrix and the iterative attenuation component of each pixel in the two-dimensional projection image are calculated by using multiple lower computers, and the current iteration state of the three-dimensional volume data to be reconstructed is updated, and the iteration is terminated.
  • the current iterative state of the reconstructed three-dimensional volume data is used as the CT three-dimensional reproduction result, which reduces the calculation amount of the algorithm, improves the calculation efficiency, and reusability of the calculation result.
  • FIG. 6 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 4 of the present disclosure, and the embodiment of the present disclosure is based on the foregoing embodiment.
  • step 610 the two-dimensional projection image acquired by the CT at at least two rotation angles of the X-ray source is transmitted to at least two lower-level machines.
  • each lower computer is controlled to calculate a projection matrix of each pixel point in the received two-dimensional projection image, and the projection matrix is stored in a memory of the lower computer.
  • each of the lower machines is controlled according to the formula: An iterative attenuation component corresponding to each of the two-dimensional projection images is calculated.
  • step 640 the cumulative summation result of the iterative attenuation component corresponding to the two-dimensional projection image is used as the current iteration state of the three-dimensional volume data to be reconstructed and each of the two-dimensional projection images in the nth iteration.
  • the total attenuation between the iterations Sum is used as the cumulative summation result of the iterative attenuation component corresponding to the two-dimensional projection image as the current iteration state of the three-dimensional volume data to be reconstructed and each of the two-dimensional projection images in the nth iteration.
  • the lower computer sends the iterative attenuation component back to the upper computer, and the upper computer accumulates the iterative attenuation component corresponding to each frame of the two-dimensional projection image to obtain the iterative attenuation total Sum.
  • step 650 Updating the current iteration state of the three-dimensional volume data to be reconstructed is x n+1 .
  • x n+1 is the current iteration state of the three-dimensional volume data to be reconstructed in the n+1th iteration
  • ⁇ and ⁇ are preset weight values
  • R( ⁇ ) is a preset constraint term equation.
  • step 660 it is determined whether the iteration termination condition is satisfied. If the iteration termination condition is not satisfied, then step 630 is performed; if the iteration termination condition is satisfied, step 670 is performed.
  • step 670 the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is taken as the CT three-dimensional reproduction result.
  • the projection matrix and the iterative attenuation component of each pixel in the two-dimensional projection image are calculated by at least two lower computers, and the three-dimensional volume data to be reconstructed is updated.
  • the current iterative state of the 3D volume data to be reconstructed after the iteration is terminated is taken as the CT three-dimensional reproduction result, which reduces the calculation amount of the algorithm, improves the calculation efficiency, and reusability of the calculation result.
  • FIG. 7 is a flowchart of a CT three-dimensional reconstruction method according to Embodiment 5 of the present disclosure, and the embodiment of the present disclosure is based on the foregoing embodiment.
  • step 710 different two-dimensional projection images are separately sent to different lower computers.
  • different two-dimensional projection images are respectively sent to different lower-level machines, including at least one of the following:
  • Two-dimensional projection images at the same rotation angle are respectively sent to the at least two lower-level machines.
  • each lower computer uses a graphics processing unit (GPU) for parallel computing, processing only one frame of image.
  • GPU graphics processing unit
  • each lower computer is controlled to calculate a projection matrix of each pixel in the received two-dimensional projection image, and the projection matrix is stored in a memory of each of the lower computers.
  • step 730 the lower-level machine is controlled to calculate an iterative attenuation component corresponding to the received two-dimensional projection image according to the projection matrix and a current iteration state of the three-dimensional volume data to be reconstructed, and return the iterative attenuation. Component.
  • step 740 the current iteration state of the three-dimensional volume data to be reconstructed is updated according to the iterative attenuation component returned by each lower computer.
  • step 750 it is determined whether the preset iteration termination condition is satisfied. If the iteration termination condition is satisfied, step 760 is performed; if the iteration termination condition is not met, step 730 is returned.
  • step 760 the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is taken as the CT three-dimensional reproduction result.
  • multiple lower computers use multiple GPUs for parallel computing, and each lower computer processes only one frame of image, which reduces the amount of calculation and the amount of data, and improves the calculation efficiency.
  • Embodiment 6 uses multiple GPUs for parallel computing, and each lower computer processes only one frame of image, which reduces the amount of calculation and the amount of data, and improves the calculation efficiency.
  • FIG. 8 is a schematic diagram of a CT three-dimensional reconstruction system according to Embodiment 6 of the present disclosure.
  • the system includes an image acquisition module 810, an iterative state update module 840, an iterative return module 850, and a reproduction result determination module 860.
  • the image obtaining module 810, the iterative state updating module 840, the iterative returning module 850, and the reproduction result determining module 860 are all located in the upper computer.
  • the CT three-dimensional reconstruction system may further include a projection matrix receiving module 820 and a component calculation module 830, and the projection matrix receiving module 820 and the component calculation module 830 are located in the lower computer.
  • the image acquisition module 810 is configured to transmit the two-dimensional projection image acquired by the CT at at least two rotation angles of the X light source to at least two lower computers.
  • the projection matrix receiving module 820 is configured to control each lower computer to calculate a projection matrix of each pixel point in the received two-dimensional projection image, and store the projection matrix in a memory of each of the lower computers.
  • the component calculation module 830 is configured to control the each lower computer to calculate an iterative attenuation component corresponding to the two-dimensional projection image according to the projection matrix and a current iteration state of the three-dimensional volume data to be reconstructed, and return an iterative attenuation component.
  • the iterative state update module 840 is configured to update the current iteration state of the three-dimensional volume data to be reconstructed according to the iterative attenuation component returned by all lower-level machines.
  • the iterative returning module 850 is configured to determine whether the iterative termination condition is satisfied. If the iterative termination condition is not met, the execution execution control returns, according to the projection matrix stored in the memory, and the current iteration state of the 3D volume data to be reconstructed, The received iterative attenuation component corresponding to the two-dimensional projection image and returning the operation of the iterative attenuation component until the preset iteration termination condition is satisfied.
  • the recurring result determining module 860 is configured to set, as the iterative termination condition, the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated as the CT three-dimensional reproduction result.
  • the projection matrix receiving module 820 is configured to:
  • each of the lower computers determines whether a line equation of each pixel intersects each voxel in the three-dimensional volume data to be reconstructed; if the line equation of the pixel points intersects the voxel in the three-dimensional volume data to be reconstructed, a length value of a line connecting the line points in the voxel; if the line equation of the pixel point does not intersect the voxel in the three-dimensional volume data to be reconstructed, the line equation of the pixel point Connection in the office The length of the voxel is 0;
  • the projection matrix receiving module 820 is configured to:
  • the component calculation module 830 is configured to:
  • N is the number of frames of the two-dimensional projection image
  • T i is an iterative attenuation component corresponding to the two-dimensional projection image of the ith frame
  • M is a pixel point of the two-dimensional projection image Total
  • a ij is the projection matrix of the jth pixel in the 2D projection image of the ith frame
  • b ij is the pixel value of the jth pixel in the 2D projection image of the ith frame
  • x n is the nth iteration The current iteration state of the 3D volume data to be reconstructed.
  • the iterative state update module 850 is configured to:
  • Updating a current iteration state of the three-dimensional volume data to be reconstructed is x n+1 ;
  • n n+1 is a current iteration state of the three-dimensional volume data to be reconstructed in the n+1th iteration;
  • the ⁇ and ⁇ are preset weight values; and
  • the R( ⁇ ) is a preset constraint term equation; For the gradient operation.
  • the image obtaining module 810 is configured to:
  • the CT three-dimensional reconstruction system can perform the CT three-dimensional reconstruction method provided by any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of performing the CT three-dimensional reconstruction method.
  • Embodiments of the present disclosure also provide a non-transitory computer readable storage medium, the storage medium storing There are computer executable instructions that, when executed by a processor, can perform a CT three dimensional reconstruction method.
  • the method includes:
  • each lower computer Controlling each lower computer to calculate a projection matrix of each pixel point in the received two-dimensional projection image, and storing the projection matrix in each memory of each lower computer;
  • the current iteration state of the three-dimensional volume data to be reconstructed after the iteration is terminated is used as the CT three-dimensional reproduction result.
  • the computer executable instructions when executed by a computer processor, can also be used to implement the technical solution of the CT three-dimensional reconstruction method provided by any embodiment of the present disclosure.
  • the present disclosure can be implemented by software and hardware, and of course can also be implemented by hardware.
  • the technical solution of the present disclosure may be embodied in the form of a software product, which may be stored in a non-transitory computer readable storage medium, such as a computer floppy disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FLASH), a hard disk or an optical disk, etc., the non-transitory computer readable storage medium comprising one or more instructions for causing a computer device (which may be a personal computer)
  • the server, or network device, etc. performs the method described in the embodiments of the present disclosure.
  • FIG. 9 a hardware structure diagram of an electronic device according to Embodiment 8 of the present disclosure is shown in FIG. 9 .
  • the electronic device includes:
  • At least one processor 910 such as one processor 910 in FIG. 9;
  • the electronic device may further include an input device 930 and an output device 940.
  • the processor 910, the memory 920, the input device 930, and the output device 940 in the electronic device may be connected by a bus or other means, as exemplified by a bus connection in FIG.
  • the memory 920 is a non-transitory computer readable storage medium, and can store a software program, a computer executable program, and a module, such as a program instruction or a module corresponding to the CT three-dimensional reconstruction method in the embodiment of the present application (for example, FIG. 8
  • the image acquisition module 810, the projection matrix receiving module 820, the component calculation module 830, the iterative state update module 840, the iterative return module 850, and the reproduction result determination module 860) are shown.
  • the processor 910 executes the functional application of the server and the data processing by executing software programs, instructions, and modules stored in the memory 920, that is, implementing the CT three-dimensional reconstruction method of the above method embodiment.
  • the memory 920 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to usage of the electronic device, and the like.
  • memory 920 can include high speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device.
  • memory 920 can optionally include memory remotely located relative to processor 910, which can be connected to the terminal device over a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • Input device 930 can be configured to receive input digital or character information and to generate key signal inputs related to user settings and function controls of the electronic device.
  • Output device 940 can include a display device such as a display screen.
  • the CT three-dimensional image reconstruction method and system provided by the embodiments of the present disclosure utilizes multiple lower-range machine distributed parallel computing to improve computational efficiency and reusability of calculation results.

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Abstract

一种CT三维重建方法及系统。方法包括：将计算机断层扫描CT在X光源（210）的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机（110）；控制每个下位机计算接收到的二维投影图像中每个像素点的投影矩阵，将投影矩阵存储于每个下位机各自的内存中（120）；控制每个下位机根据投影矩阵和待重建三维体数据的当前迭代状态，计算迭代衰减分量并返回迭代衰减分量（130）；根据迭代衰减分量更新当前迭代状态（140）；判断是否满足迭代终止条件（150），若不满足迭代终止条件，返回执行控制每个下位机投影矩阵和当前迭代状态，计算迭代衰减分量并返回迭代衰减分量的操作，直至满足预设迭代终止条件；若满足迭代终止条件，将迭代终止后的当前迭代状态作为CT三维重现结果（160）。

Description

CT三维重建方法及系统 技术领域
本公开实施例涉及图像处理技术，例如涉及一种计算机断层扫描(Computed Tomography，CT)三维重建方法及系统。
背景技术
相关的CT技术中，由于平板探测器有更高的图像分辨率和更大的视场角，应用平板探测器技术的锥束CT设备在成像质量与成像效率上均具有优势，因此如何在锥束CT模型下快速、准确地进行CT三维重建成为十分重要的问题。
相关的基于FDK(Feldkamp，Davis，Kress)算法的三维重建方法中，FDK算法作为滤波反投影算法，虽然计算速度较快，对系统资源要求低，但重建图像质量差，影像中存在伪影，影响可视化效果和医生诊断。而代数迭代算法(Algebraic Reconstruction Technique，ART)，虽然可以解决滤波反投影算法的图像质量问题，但是ART需要计算投影矩阵，计算量大，计算效率较低，在对投影矩阵进行保存时对存储硬件要求也较高，限制了ART在临床上的应用。
发明内容
本公开实施例提供一种CT三维重建方法及系统，可以实现CT三维重建。
第一方面，本公开实施例提供了一种计算机断层扫描CT三维重建方法，该方法包括：
将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机；
控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于所述每个下位机各自的内存中；
控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与所述二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量；
根据所述每个下位机返回的所述迭代衰减分量，更新所述待重建三维体数 据的当前迭代状态；
判断是否满足迭代终止条件，若不满足迭代终止条件，返回执行控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与接收到的二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量的操作，直至满足预设的迭代终止条件；
若满足迭代终止条件，将迭代终止后所述待重建三维体数据的当前迭代状态，作为所述CT三维重现结果。
可选地，控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵包括：
控制所述每个下位机根据接收到的二维投影图像对应的旋转角度，构造X光源与该旋转角度下的二维投影图像中每个像素点的连线方程；
控制所述每个下位机判断每个像素点的连线方程与所述待重建三维体数据中每个体素是否相交；若像素点的连线方程与所述待重建三维体数据中的体素相交，则获取所述像素点的连线方程的连线在所述体素内的长度值；若所述像素点的连线方程与所述待重建三维体数据中的体素不相交，则所述像素点的连线方程的连线在所述体素内的长度值为0；以及
控制所述每个下位机根据所述每个像素点的连线方程的连线在每个体素内的长度值，构造所述二维投影图像中每个像素点的投影矩阵。
可选地，控制所述每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵包括：
控制所述每个下位机为接收到的所述二维投影图像中的每个像素点分别分配一个线程，以通过至少两个线程分布式地计算所述二维投影图像中每个像素点的投影矩阵。
可选地，控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与所述二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量，包括：
控制所述每个下位机根据公式：
Figure PCTCN2017071694-appb-000001
计算与所述二维投影图像对应的迭代衰减分量，
其中，i∈[1，N]，N为所述二维投影图像的帧数，Ti为第i帧二维投影图像对应 的迭代衰减分量，M为所述二维投影图像中像素点总数，Aij为第i帧二维投影图像中第j个像素点的投影矩阵，
Figure PCTCN2017071694-appb-000002
为Aij的转置矩阵，bij为第i帧二维投影图像中第j个像素点的像素值，xn为第n次迭代中所述待重建三维体数据的当前迭代状态。
可选地，根据所述每个下位机返回的所述迭代衰减分量更新所述待重建三维体数据的当前迭代状态，包括：
将与所述二维投影图像对应的在第n次迭代的迭代衰减分量的累加求和结果，作为第n次迭代中，所述待重建三维体数据的当前迭代状态与所述二维投影图像之间的迭代衰减总量Sum；以及
根据公式：
Figure PCTCN2017071694-appb-000003
更新所述待重建三维体数据的当前迭代状态为xn+1；
其中，xn+1为第n+1次迭代中待重建三维体数据的当前迭代状态，所述γ、λ为预设权重值，所述R(·)为预设约束项方程，
Figure PCTCN2017071694-appb-000004
为求梯度运算。
可选地，将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机，包括以下至少之一：
将不同所述旋转角度下的二维投影图像发送至同一下位机中；
将同一所述旋转角度下的二维投影图像发送至同一下位机中；以及
将同一所述旋转角度下的二维投影图像分别发送至所述至少两个下位机中。
第二方面，本公开实施例还提供了一种CT三维重建系统，该系统包括：
图像获取模块，设置为将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机；
投影矩阵接收模块，设置为控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于所述每个下位机各自的内存中；
分量计算模块，设置为控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与所述二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量；
迭代状态更新模块，设置为根据所述每个下位机返回的所述迭代衰减分量，更新所述待重建三维体数据的当前迭代状态；以及
迭代返回模块，设置为判断是否满足迭代终止条件，若不满足迭代终止条件，返回执行控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与接收到的二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量的操作，直至满足预设的迭代终止条件；以及
重现结果确定模块，设置为若满足迭代终止条件，将迭代终止后所述待重建三维体数据的当前迭代状态，作为所述CT三维重现结果。
可选地，所述投影矩阵接收模块设置为：控制所述每个下位机根据接收到的二维投影图像对应的旋转角度，构造X光源该旋转角度下的所述二维投影图像中每个像素点的连线方程；
控制所述每个下位机判断每个像素点的连线方程与所述待重建三维体数据中每个体素是否相交；若每个像素点的连线方程与所述待重建三维体数据中的体素相交，则获取所述每个像素点的连线方程的连线在所述体素内的长度值；若所述每个像素点的连线方程与所述待重建三维体数据中的体素不相交，则所述像素点的连线方程的连线在所述体素内的长度值为0；以及
控制所述每个下位机根据所述每个像素点的连线方程的连线在每个体素内的长度值，构造所述二维投影图像中每个像素点的投影矩阵。
可选地，所述投影矩阵接收模块设置为：
控制所述每个下位机为接收到的所述二维投影图像中的每个像素点分别分配一个线程，以通过至少两个线程分布式的计算所述二维投影图像中每个像素点的投影矩阵。
可选地，所述分量计算模块设置为：
控制所述每个下位机根据公式：
Figure PCTCN2017071694-appb-000005
计算与所述二维投影图像对应的迭代衰减分量；
其中，i∈[1，N]，N为所述二维投影图像的帧数，Ti为第i帧二维投影图像对应的迭代衰减分量，M为所述二维投影图像中像素点的总数，Aij为第i帧二维投影图像中第j个像素点的投影矩阵，
Figure PCTCN2017071694-appb-000006
为Aij的转置矩阵，bij为第i帧二维投影图像中第j个像素点的像素值，xn为第n次迭代中待重建三维体数据的当前迭代 状态。
可选地，所述迭代状态更新模块，设置为：
将与所述二维投影图像对应的在第n次迭代的迭代衰减分量的累加求和结果，作为第n次迭代中，所述待重建三维体数据的当前迭代状态与各所述二维投影图像之间的迭代衰减总量Sum；以及
根据公式：
Figure PCTCN2017071694-appb-000007
更新所述待重建三维体数据的当前迭代状态为xn+1；
其中，xn+1为第n+1次迭代中待重建三维体数据的当前迭代状态，所述γ、λ为预设权重值，所述R(·)为预设约束项方程，
Figure PCTCN2017071694-appb-000008
为求梯度运算。
可选地，所述图像获取模块设置为一下至少之一：
将不同的所述旋转角度下的二维投影图像发送至同一下位机中；
将同一所述旋转角度下的二维投影图像发送至同一下位机中；以及
将同一所述旋转角度下的二维投影图像分别发送至所述至少两个下位机中。
第三方面，本公开实施例还提供了一种非暂态计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令设置为执行上述的方法。
第四方面，本公开实施例还提供了一种电子设备，包括：
至少一个处理器；
存储器，与所述至少一个处理器通信连接，设置为存储可被所述至少一个处理器执行的程序，
所述程序被所述至少一个处理器执行，使得所述至少一个处理器执行本公开实施例所述的CT三维重建方法。
本实施例的技术方案，基于分布式技术，通过至少两个下位机计算二维投影图像中每个像素点的投影矩阵和迭代衰减分量，更新待重建三维体数据的当前迭代状态，将迭代终止后待重建三维体数据的当前迭代状态作为CT三维重现结果，减少了算法计算量、提高了计算效率以及计算结果可重用性。
附图说明
图1为本公开实施例一提供的一种CT三维重建方法的流程图；
图2是本公开实施例提供的一种CT三维重建方法中的锥束CT扫描示意图；
图3是本公开实施例提供的一种CT三维重建方法中的三维体数据坐标系和投影数据坐标系示意图；
图4是本公开实施例二提供的一种CT三维重建方法的流程图；
图5是本公开实施例三提供的一种CT三维重建方法的流程图；
图6是本公开实施例四提供的一种CT三维重建方法的流程图；
图7是本公开实施例五提供的一种CT三维重建方法的流程图；
图8是本公开实施例六提供的一种CT三维重建系统示意图；以及
图9是本公开实施例八中的一种电子设备的结构图。
具体实施方式
下面结合附图和实施例对本公开作详细说明。此处所描述的具体实施例仅仅用于解释本公开，而非对本公开的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与本公开相关的部分而非全部结构。在不冲突的情况下，以下实施例以及实施例中的特征可以相互任意组合。
实施例一
图1为本公开实施例一提供的一种CT三维重建方法的流程图，本实施例可适用于在锥束CT模型下进行三维重建的情况，该方法可以由CT三维重建系统来执行，该系统可由软件，硬件，或软件和硬件来实现，该系统可集成于计算机等设备中。
在步骤110中，将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机。
其中，CT即电子计算机断层成像技术，可分为传统CT、螺旋CT以及锥束CT，锥束CT的扫描速度快、图像分辨率高且辐射利用率高，锥束CT的一次圆周扫描即可完成传统CT及螺旋CT的数百个断层扫描，且锥束CT的X、Y、Z方向的分辨率一致，能减少传统CT及螺旋CT检测中常见的容积效应。本公开 实施例以锥束CT为例，解决如何采用锥束CT模型下快速、准确地进行三维重建。图2是本公开实施例提供的一种CT三维重建方法中的锥束CT扫描示意图，其中射线I、J、K及L是由X光源210发出并穿过被测物体220的射线，穿越被测物的X射线锥束被探测器230接收，所得数据即为该旋转角度下的二维投影图像，X光源每转过一个角度，进行相同的操作，直至完成多个角度的扫描，X光源旋转角度的大小及选取个数可根据需求进行确定。投影数据以无符号整型(unsigned short)类型数据保存在N*H*W三维数组中。其中N为投影数据帧数，H为投影数据高度，W为投影数据宽度。图3是本公开实施例提供的一种CT三维重建方法中的三维体数据坐标系和投影数据坐标系示意图。假设光源绕体素X轴进行旋转，得到光源的每个旋转角度下获取的二维投影图像，将所述二维投影图像发送至至少两个下位机中。发送方式可以是同一旋转角度对应的二维投影图像发送至同一个下位机，也可以是不同旋转角度对应的二维投影图像发送至同一个下位机，增加下位机的数量可提高数据处理速度。
在步骤120中，控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于所述每个下位机各自的内存中。其中，采用ART算法的基本方法是确定一个合适的目标函数，例如可以是F＝|(Ax-b)|2+λR(x)。其中A是二维投影图像中每个像素的投影矩阵，x是待重建三维体数据，b为投影图像的像素值，R(·)为预设约束项方程，λ为预设权重值。迭代寻找x，令目标函数F取最小值，确定待重建三维体数据x。限制ART算法在锥束CT中应用的因素包括：投影矩阵A的计算复杂；每一帧投影内所有射线与三维体数据体素间的位置关系数据的数据量大，对存储硬件要求高，限制了计算结果的可重用性。本公开实施例中在初次计算投影矩阵A后，将投影矩阵A存储在各自下位机中，在后续的循环中可调用存储的投影矩阵A。至少两个下位机同时进行计算，将结果返回上位机进行累加，能大大加快投影矩阵的计算效率。
在步骤130中，控制所述每个下位机根据所述投影矩阵以及待重建三维体数据的当前迭代状态，计算与所述二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量。
其中，迭代衰减分量与当前迭代状态与二维投影图像的像素值相关。其中，当前迭代状态为数值，初始的当前迭代状态可以为0，也可以是通过FDK算法 初步确定的当前迭代状态。
在步骤140中，根据所述每个下位机返回的所述迭代衰减分量，更新所述待重建三维体数据的当前迭代状态。
其中，利用返回的迭代衰减分量和第n次迭代状态，更新第n+1次迭代中待重建三维体数据的当前迭代状态。
在步骤150中，判断是否满足迭代终止条件，若不满足迭代终止条件，则返回执行步骤130；若满足迭代终止条件，执行步骤160。
其中，迭代终止条件可以是迭代次数满足预设迭代次数，也可以是相邻两次的迭代状态的差值满足预设差值。
在步骤160中，将迭代终止后所述待重建三维体数据的当前迭代状态，作为所述CT三维重现结果。
其中，迭代终止后待重建三维体数据的当前迭代状态能够使目标函数达到最大值(或者最小值)，将此时的当前迭代状态确定为二维投影图像的CT三维重现结果。
本实施例的技术方案，基于分布式技术，通过至少两个下位机计算二维投影图像中每个像素点的投影矩阵和迭代衰减分量，更新待重建三维体数据的当前迭代状态，将迭代终止后待重建三维体数据的当前迭代状态作为CT三维重现结果，减少了算法计算量、提高了计算效率以及计算结果可重用性。
实施例二
图4是本公开实施例二提供的一种CT三维重建方法的流程图，本公开实施例以上述实施例为基础。
在步骤410中，将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机。
在步骤420、中，控制每个下位机根据接收到的二维投影图像对应的旋转角度，构造X光源与该旋转角度下的二维投影图像中每个像素点的连线方程。
在步骤430、中，控制所述每个下位机判断每个像素点的连线方程与待重建三维体数据中每个体素是否相交；若像素点的连线方程与待重建三维体数据中的体素相交，则获取该像素点的连线方程的连线在该体素内的长度值；若该像 素点的连线方程与该待重建三维体数据中的体素不相交，则该像素点的连线方程的连线在所述体素内的长度值为0。
在步骤440、中，控制所述每个下位机根据每个像素点的连线方程的连线在每个体素内的长度值，构造所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于下位机各自的内存中。
其中，体素是体积元素的简称，适用于三维成像与医学影像等领域，体素本身并不含有空间中位置的数据(即坐标)，体素可以用恒定的标量或者向量表示一个立体的区域。对每一帧二维投影图像，若是第一次循环迭代过程，计算该帧二维投影图像对应的X光源与该二维投影图像中每个像素点的连线方程。判断该连线方程与待重建三维体数据中的每个体素是否相交，若像素的连线方程与待重建三维体数据中的体素相交，则计算该像素点的连线方程的连线在该体素内的部分的长度值，并记录该体素在三维体数据中的三维位置坐标(x，y，z)。若该连线方程与待重建三维体数据中的体素不相交，则该像素点的连线方程的连线在体素内的长度值为0。长度值构成当前帧二维投影图像的像素点的投影矩阵，将投影矩阵存储于下位机各自的内存中。投影矩阵可以只计算一次，后续的循环迭代过程中可以使用存储的投影矩阵，因此若不是第一次循环迭代过程，则跳过计算投影矩阵的步骤。
在步骤450中，控制所述每个下位机根据投影矩阵以及待重建三维体数据的当前迭代状态，计算与所述二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量。
在步骤460中，根据所述每个下位机返回的所述迭代衰减分量，更新所述待重建三维体数据的当前迭代状态。
在步骤470中，判断是否满足预设的迭代终止条件，若满足迭代终止条件，执行步骤480；若不满足迭代终止条件，返回执行步骤450。
在步骤480中，将迭代终止后所述待重建三维体数据的当前迭代状态，作为所述CT三维重现结果。
本公开实施例的技术方案，通过计算二维投影图像对应的X光源与二维投影图像中像素点的连线方程，根据该像素点的连线方程的连线与待重建三维体数据中的每个体素相交部分的长度值构造所有像素点的投影矩阵，可以只计算一次投影矩阵，将初次计算后投影矩阵保存在下位机的内存中，后续循环中可 以使用存储的投影矩阵，简化了计算过程，加快了计算效率。
可选地，在上述实施例的基础上，控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵包括：
控制所述每个下位机为接收到的所述二维投影图像中的每个像素点分别分配一个线程，以通过至少两个线程分布式地计算所述二维投影图像中每个像素点的投影矩阵。
其中，假设每一帧图像共有M个像素点，下位机为每个像素点分配一个线程，通过至少两个线程分布式地计算每个像素点的投影矩阵，当所有线程执行完成时，即可得到对应当前帧二维投影图像的所有像素点的投影矩阵。采用多线程分布式的计算方式可以提高计算速度，加快投影矩阵的计算效率。
实施例三
图5是本公开实施例三提供的一种CT三维重建方法的流程图，本公开实施例以上述实施例为基础。
在步骤510中，将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机。
在步骤520中，控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于所述每个下位机各自的内存中。
在步骤530中，控制所述每个下位机根据公式：
Figure PCTCN2017071694-appb-000009
计算与接收的二维投影图像对应的迭代衰减分量。
其中，i∈[1，N]，N为所述二维投影图像的帧数，Ti为第i帧二维投影图像对应的迭代衰减分量，M为所述二维投影图像中像素点总数，Aij为第i帧二维投影图像中第j个像素点的投影矩阵，bij为第i帧二维投影图像中第j个像素点的像素值，xn为第n次迭代中待重建三维体数据的当前迭代状态。
在步骤540中，根据所述每个下位机返回的所述迭代衰减分量，更新所述待重建三维体数据的当前迭代状态。
在步骤550中，判断是否满足预设的迭代终止条件，若满足迭代终止条件，执行步骤560；若不满足迭代终止条件，返回执行530。
在步骤560中，将迭代终止后所述待重建三维体数据的当前迭代状态，作 为所述CT三维重现结果。
本实施例的技术方案，基于分布式技术，通过多个下位机计算二维投影图像中每个像素点的投影矩阵和迭代衰减分量，更新待重建三维体数据的当前迭代状态，将迭代终止后待重建三维体数据的当前迭代状态作为CT三维重现结果，减少了算法计算量、提高了计算效率以及计算结果可重用性。实施例四
图6是本公开实施例四提供的一种CT三维重建方法的流程图，本公开实施例以上述实施例为基础。
在步骤610中，将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机。
在步骤620中，控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于下位机的内存中。
在步骤630中，控制所述每个下位机根据公式：
Figure PCTCN2017071694-appb-000010
计算与每个所述二维投影图像对应的迭代衰减分量。
在步骤640中，将与所述二维投影图像对应的迭代衰减分量的累加求和结果，作为第n次迭代中，所述待重建三维体数据的当前迭代状态与各所述二维投影图像之间的迭代衰减总量Sum。
其中，下位机将迭代衰减分量发送回上位机，上位机累加每一帧二维投影图像对应的迭代衰减分量，得到迭代衰减总量Sum。
在步骤650中，根据公式：
Figure PCTCN2017071694-appb-000011
更新所述待重建三维体数据的当前迭代状态为xn+1。
其中，xn+1为第n+1次迭代中待重建三维体数据的当前迭代状态，所述γ、λ为预设权重值，所述R(·)为预设约束项方程，
Figure PCTCN2017071694-appb-000012
为求梯度运算。
在步骤660中，判断是否满足迭代终止条件，若不满足迭代终止条件，则返回执行步骤630；若满足迭代终止条件，执行步骤670。
在步骤670中，将迭代终止后所述待重建三维体数据的当前迭代状态，作为所述CT三维重现结果。
本实施例的技术方案，基于分布式技术，通过至少两个下位机计算二维投影图像中每个像素点的投影矩阵和迭代衰减分量，更新待重建三维体数据的当 前迭代状态，将迭代终止后待重建三维体数据的当前迭代状态作为CT三维重现结果，减少了算法计算量、提高了计算效率以及计算结果可重用性。
实施例五
图7是本公开实施例五提供的一种CT三维重建方法的流程图，本公开实施例以上述实施例为基础。
在步骤710中，将不同的二维投影图像分别发送至不同的下位机中。
其中，将不同的二维投影图像分别发送至不同的下位机中，包括以下至少之一：
将不同的所述旋转角度下的二维投影图像发送至同一下位机中；
将同一所述旋转角度下的二维投影图像发送至同一下位机中；以及
将同一所述旋转角度下的二维投影图像分别发送至所述至少两个下位机中。
采用分布式系统，每个下位机使用图形处理器(Graphics Processing Unit，GPU)并行计算，仅处理一帧图像。
在步骤720中，控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于所述下位机各自的内存中。
在步骤730中，控制所述每个下位机根据所述投影矩阵以及待重建三维体数据的当前迭代状态，计算与接收到的所述二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量。
在步骤740中，根据所述每个下位机返回的所述迭代衰减分量，更新所述待重建三维体数据的当前迭代状态。
在步骤750中，判断是否满足预设的迭代终止条件，若满足迭代终止条件，执行步骤760；若不满足迭代终止条件，返回执行步骤730。
在步骤760中，将迭代终止后所述待重建三维体数据的当前迭代状态，作为所述CT三维重现结果。
本公开实施例的技术方案，多个下位机使用多个GPU并行计算，每个下位机仅处理一帧图像，减少了计算量和数据量，提高了计算效率。实施例六
图8是本公开实施例六提供的一种CT三维重建系统示意图，该系统包括：图像获取模块810、迭代状态更新模块840、迭代返回模块850以及重现结果确定模块860。其中，图像获取模块810、迭代状态更新模块840、迭代返回模块850以及重现结果确定模块860均位于上位机中。所述CT三维重建系统还可以包括投影矩阵接收模块820和分量计算模块830，投影矩阵接收模块820和分量计算模块830位于下位机中。
图像获取模块810设置为将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机。
投影矩阵接收模块820设置为控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于每个所述下位机各自的内存中。
分量计算模块830设置为控制所述每个下位机根据所述投影矩阵以及待重建三维体数据的当前迭代状态，计算与所述二维投影图像对应的迭代衰减分量并返回迭代衰减分量。
迭代状态更新模块840设置为根据所有下位机返回的所述迭代衰减分量，更新所述待重建三维体数据的当前迭代状态。
迭代返回模块850设置为判断是否满足迭代终止条件，若不满足迭代终止条件，返回执行控制所述每个下位机根据内存中存储的投影矩阵，以及待重建三维体数据的当前迭代状态，计算与接收到的二维投影图像对应的迭代衰减分量并返回迭代衰减分量的操作，直至满足预设的迭代终止条件。
重现结果确定模块860，设置为若满足迭代终止条件，将迭代终止后所述待重建三维体数据的当前迭代状态，作为所述CT三维重现结果。
可选地，所述投影矩阵接收模块820设置为：
控制所述每个下位机根据与接收到的二维投影图像对应的旋转角度，构造X光源与所述二维投影图像中每个像素点的连线方程；
控制所述每个下位机判断每个像素点的连线方程与待重建三维体数据中每个体素是否相交；若像素点的连线方程与待重建三维体数据中的体素相交，则获取所述像素点的连线方程的连线在所述体素内的长度值；若像素点的连线方程与待重建三维体数据中的体素不相交，则所述像素点的连线方程的连线在所 述体素内的长度值为0；
控制所述每个下位机根据所述每个像素点的连线方程的连线在每个体素内的长度值，构造所述二维投影图像中像素点的投影矩阵。
可选地，所述投影矩阵接收模块820设置为：
控制所述每个下位机为接收到的所述二维投影图像中的每个像素点分别分配一个线程，以通过至少两个线程分布式地计算所述二维投影图像中每个像素点的投影矩阵。
可选地，所述分量计算模块830设置为：
控制所述每个下位机根据公式：
Figure PCTCN2017071694-appb-000013
计算与所述二维投影图像对应的迭代衰减分量；
其中，i∈[1，N]，N为所述二维投影图像的帧数，Ti为第i帧二维投影图像对应的迭代衰减分量，M为所述二维投影图像中像素点的总数，Aij为第i帧二维投影图像中第j个像素点的投影矩阵，bij为第i帧二维投影图像中第j个像素点的像素值，xn为第n次迭代中待重建三维体数据的当前迭代状态。
可选地，所述迭代状态更新模块850设置为：
将与每个所述二维投影图像对应的迭代衰减分量Ti的累加求和结果，作为第n次迭代中，所述待重建三维体数据的当前迭代状态与所述二维投影图像之间的迭代衰减总量Sum；
根据公式：
Figure PCTCN2017071694-appb-000014
更新所述待重建三维体数据的当前迭代状态为xn+1；
其中，xn+1为第n+1次迭代中待重建三维体数据的当前迭代状态；所述γ、λ为预设权重值；所述R(·)为预设约束项方程；
Figure PCTCN2017071694-appb-000015
为求梯度运算。
可选地，所述图像获取模块810设置为：
将不同的二维投影图像分别发送至不同的下位机中。
上述CT三维重建系统可执行本公开任意实施例所提供的CT三维重建方法，具备执行CT三维重建方法相应的功能模块和有益效果。
实施例七
本公开实施例还提供一种非暂态计算机可读存储介质，所述存储介质存储 有计算机可执行指令，所述计算机可执行指令在由处理器执行时可以执行一种CT三维重建方法。该方法包括：
将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机；
控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于所述每个下位机各自的内存中；
控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与所述二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量；
根据所述每个下位机返回的所述迭代衰减分量，更新所述待重建三维体数据的当前迭代状态；
判断是否满足迭代终止条件，若不满足迭代终止条件，返回执行控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与接收到的二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量的操作，直至满足预设的迭代终止条件；
若满足迭代终止条件，将迭代终止后所述待重建三维体数据的当前迭代状态，作为所述CT三维重现结果。
可选的，该计算机可执行指令在由计算机处理器执行时还可以用于执行本公开任意实施例所提供的CT三维重建方法的技术方案。
通过以上关于实施方式的描述，所属领域的技术人员可以清楚地了解到，本公开可借助软件及硬件来实现，当然也可以通过硬件实现。本公开的技术方案本质上可以以软件产品的形式体现出来，该计算机软件产品可以存储在非暂态计算机可读存储介质中，如计算机的软盘、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、闪存(FLASH)、硬盘或光盘等，所述非暂态计算机可读存储介质包括一个或多个指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本公开实施例所述的方法。
实施例八
如图9所示，为本公开实施例八提供的电子设备的硬件结构示意图，如图9 所示，该电子设备包括：
至少一个处理器910，图9中以一个处理器910为例；以及
存储器920。
所述电子设备还可以包括：输入装置930和输出装置940。
所述电子设备中的处理器910、存储器920、输入装置930和输出装置940可以通过总线或者其他方式连接，图9中以通过总线连接为例。
存储器920作为一种非暂态计算机可读存储介质，可存储软件程序、计算机可执行程序以及模块，如本申请实施例中的CT三维重建方法对应的程序指令或模块(例如，附图8所示的图像获取模块810、投影矩阵接收模块820、分量计算模块830、迭代状态更新模块840、迭代返回模块850及重现结果确定模块860)。处理器910通过运行存储在存储器920中的软件程序、指令以及模块，从而执行服务器的功能应用以及数据处理，即实现上述方法实施例的CT三维重建方法。
存储器920可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储根据电子设备的使用所创建的数据等。此外，存储器920可以包括高速随机存取存储器，还可以包括非暂态性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非暂态性固态存储器件。在一些实施例中，存储器920可选包括相对于处理器910远程设置的存储器，这些远程存储器可以通过网络连接至终端设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
输入装置930可设置为接收输入的数字或字符信息，以及产生与电子设备的用户设置以及功能控制有关的键信号输入。输出装置940可包括显示屏等显示设备。
工业实用性
本公开实施例提供的CT三维图像重建方法及系统，利用多个下位机分布式并行计算，提高了计算效率及计算结果可重用性。

Claims (13)

  1. 一种计算机断层扫描CT三维重建方法，包括：
    将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机；
    控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于所述每个下位机各自的内存中；
    控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与所述二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量；
    根据所述每个下位机返回的所述迭代衰减分量，更新所述待重建三维体数据的当前迭代状态；以及
    判断是否满足迭代终止条件，若不满足迭代终止条件，返回执行控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与接收到的二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量的操作，直至满足预设的迭代终止条件；
    若满足迭代终止条件，将迭代终止后所述待重建三维体数据的当前迭代状态，作为所述CT三维重现结果。
  2. 根据权利要求1所述的方法，其中，控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵包括：
    控制所述每个下位机根据接收到的二维投影图像对应的旋转角度，构造X光源与该旋转角度下的二维投影图像中每个像素点的连线方程；
    控制所述每个下位机判断每个像素点的连线方程与所述待重建三维体数据中每个体素是否相交；若像素点的连线方程与所述待重建三维体数据中的体素相交，则获取所述像素点的连线方程的连线在所述体素内的长度值；若所述像 素点的连线方程与所述待重建三维体数据中的体素不相交，则所述像素点的连线方程的连线在所述体素内的长度值为0；以及
    控制所述每个下位机根据所述每个像素点的连线方程的连线在每个体素内的长度值，构造所述二维投影图像中每个像素点的投影矩阵。
  3. 根据权利要求2所述的方法，其中，控制所述每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵包括：
    控制所述每个下位机为接收到的所述二维投影图像中的每个像素点分别分配一个线程，以通过至少两个线程分布式地计算所述二维投影图像中每个像素点的投影矩阵。
  4. 根据权利要求1所述的方法，其中，控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与所述二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量，包括：
    控制所述每个下位机根据公式：
    Figure PCTCN2017071694-appb-100001
    计算与所述二维投影图像对应的迭代衰减分量，
    其中，i∈[1，N]，N为所述二维投影图像的帧数，Ti为第i帧二维投影图像对应的迭代衰减分量，M为所述二维投影图像中像素点的总数，Aij为第i帧二维投影图像中第j个像素点的投影矩阵，
    Figure PCTCN2017071694-appb-100002
    为Aij的转置矩阵，bij为第i帧二维投影图像中第j个像素点的像素值，xn为第n次迭代中所述待重建三维体数据的当前迭代状态。
  5. 根据权利要求4所述的方法，其中，根据所述每个下位机返回的所述迭代衰减分量，更新所述待重建三维体数据的当前迭代状态，包括：
    将与所述二维投影图像对应的在第n次迭代的迭代衰减分量的累加求和结果，作为第n次迭代中，所述待重建三维体数据的当前迭代状态与所述二维投影 图像之间的迭代衰减总量Sum；以及
    根据公式：
    Figure PCTCN2017071694-appb-100003
    更新所述待重建三维体数据的当前迭代状态为xn+1；
    其中，xn+1为第n+1次迭代中待重建三维体数据的当前迭代状态，所述γ、λ为预设权重值，所述R(·)为预设约束项方程，
    Figure PCTCN2017071694-appb-100004
    为求梯度运算。
  6. 根据权利要求1-5任一项所述的方法，其中，将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机，包括以下至少之一：
    将不同的所述旋转角度下的二维投影图像发送至同一下位机中；
    将同一所述旋转角度下的二维投影图像发送至同一下位机中；以及
    将同一所述旋转角度下的二维投影图像分别发送至所述至少两个下位机中。
  7. 一种计算机断层扫描CT三维重建系统，包括：
    图像获取模块，设置为将CT在X光源的至少两个旋转角度下获取的二维投影图像发送至至少两个下位机；
    投影矩阵接收模块，设置为控制每个下位机计算接收到的所述二维投影图像中每个像素点的投影矩阵，并将所述投影矩阵存储于所述每个下位机各自的内存中；
    分量计算模块，设置为控制所述每个下位机根据所述投影矩阵，以及待重建三维体数据的当前迭代状态，计算与所述二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量；
    迭代状态更新模块，设置为根据所述每个下位机返回的所述迭代衰减分量，更新所述待重建三维体数据的当前迭代状态；
    迭代返回模块，设置为判断是否满足迭代终止条件，若不满足迭代终止条 件，返回执行控制所述每个下位机根据内存中存储的投影矩阵，以及待重建三维体数据的当前迭代状态，计算与接收到的二维投影图像对应的迭代衰减分量并返回所述迭代衰减分量的操作，直至满足预设的迭代终止条件；以及
    重现结果确定模块，设置为若满足迭代终止条件，将迭代终止后所述待重建三维体数据的当前迭代状态，作为所述CT三维重现结果。
  8. 根据权利要求7所述的系统，其中，所述投影矩阵接收模块设置为：
    控制所述每个下位机根据接收到的二维投影图像对应的旋转角度，构造X光源与该旋转角度下的二维投影图像中每个像素点的连线方程；
    控制所述每个下位机判断每个像素点的连线方程与所述待重建三维体数据中每个体素是否相交；若像素点的连线方程与所述待重建三维体数据中的体素相交，则获取所述像素点的连线方程的连线在所述体素内的长度值；若所述像素点的连线方程与所述待重建三维体数据中的体素不相交，则所述像素点的连线方程的连线在所述体素内的长度值为0；以及
    控制所述每个下位机根据所述每个像素点的连线方程的连线在每个体素内的长度值，构造所述二维投影图像中每个像素点的投影矩阵。
  9. 根据权利要求8所述的系统，其中，所述投影矩阵接收模块设置为：
    控制所述每个下位机为接收到的所述二维投影图像中的每个像素点分别分配一个线程，以通过至少两个线程分布式的计算所述二维投影图像中每个像素点的投影矩阵。
  10. 根据权利要求7所述的系统，其中，所述分量计算模块设置为：
    控制所述每个下位机根据公式：
    Figure PCTCN2017071694-appb-100005
    计算与所述二维投影图像对应的迭代衰减分量；
    其中，i∈[1，N]，N为所述二维投影图像的帧数，Ti为第i帧二维投影图像对应 的迭代衰减分量，M为所述二维投影图像中像素点的总数，Aij为第i帧二维投影图像中第j个像素点的投影矩阵，
    Figure PCTCN2017071694-appb-100006
    为Aij的转置矩阵，bij为第i帧二维投影图像中第j个像素点的像素值，xn为第n次迭代中待重建三维体数据的当前迭代状态。
  11. 根据权利要求10所述的系统，其中，所述迭代状态更新模块，设置为：
    将与所述二维投影图像对应的在第n次迭代的迭代衰减分量的累加求和结果，作为第n次迭代中，所述待重建三维体数据的当前迭代状态与所述二维投影图像之间的迭代衰减总量Sum；以及
    根据公式：
    Figure PCTCN2017071694-appb-100007
    更新所述待重建三维体数据的当前迭代状态为xn+1；
    其中，xn+1为第n+1次迭代中待重建三维体数据的当前迭代状态，所述γ、λ为预设权重值，所述R(·)为预设约束项方程，
    Figure PCTCN2017071694-appb-100008
    为求梯度运算。
  12. 根据权利要求7-11任一项所述的系统，其中，所述图像获取模块设置为以下至少之一：
    将不同的所述旋转角度下的二维投影图像发送至同一下位机中；
    将同一所述旋转角度下的二维投影图像发送至同一下位机中；以及
    将同一所述旋转角度下的二维投影图像分别发送至所述至少两个下位机中。
  13. 一种非暂态计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令设置为执行权利要求1-6中任一项的方法。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112750511A (zh) * 2020-12-30 2021-05-04 东南大学 一种基于迭代法重构水泥基体孔隙分布的方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101596113A (zh) * 2008-06-06 2009-12-09 中国科学院过程工程研究所 一种ct并行重建系统及成像方法
US20110164031A1 (en) * 2010-01-06 2011-07-07 Kabushiki Kaisha Toshiba Novel implementation of total variation (tv) minimization iterative reconstruction algorithm suitable for parallel computation
CN103065342A (zh) * 2012-12-21 2013-04-24 深圳先进技术研究院 一种ct图像的重建方法
US20130336562A1 (en) * 2012-06-15 2013-12-19 Toshiba Medical Systems Corporation Adaptively determined parameter values in iterative reconstruction method and system
CN106846465A (zh) * 2017-01-19 2017-06-13 深圳先进技术研究院 一种ct三维重建方法及系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101596113A (zh) * 2008-06-06 2009-12-09 中国科学院过程工程研究所 一种ct并行重建系统及成像方法
US20110164031A1 (en) * 2010-01-06 2011-07-07 Kabushiki Kaisha Toshiba Novel implementation of total variation (tv) minimization iterative reconstruction algorithm suitable for parallel computation
US20130336562A1 (en) * 2012-06-15 2013-12-19 Toshiba Medical Systems Corporation Adaptively determined parameter values in iterative reconstruction method and system
CN103065342A (zh) * 2012-12-21 2013-04-24 深圳先进技术研究院 一种ct图像的重建方法
CN106846465A (zh) * 2017-01-19 2017-06-13 深圳先进技术研究院 一种ct三维重建方法及系统

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LIU, CHUNHUA ET AL.: "The Study of Cone-Beam ART Algorithm Using Parallel Computing", CT THEORY AND APPLICATIONS, vol. 16, no. 3, 30 September 2007 (2007-09-30), pages 13 - 19 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112750511A (zh) * 2020-12-30 2021-05-04 东南大学 一种基于迭代法重构水泥基体孔隙分布的方法
CN112750511B (zh) * 2020-12-30 2024-04-09 东南大学 一种基于迭代法重构水泥基体孔隙分布的方法

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