WO2021252410A1 - Réduction d'artefacts dans des clichés de tomodensitométrie - Google Patents

Réduction d'artefacts dans des clichés de tomodensitométrie Download PDF

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WO2021252410A1
WO2021252410A1 PCT/US2021/036284 US2021036284W WO2021252410A1 WO 2021252410 A1 WO2021252410 A1 WO 2021252410A1 US 2021036284 W US2021036284 W US 2021036284W WO 2021252410 A1 WO2021252410 A1 WO 2021252410A1
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image
domain
measurements
projection
shadows
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PCT/US2021/036284
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English (en)
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Gengsheng L. Zeng
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University Of Utah Research Foundation
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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
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5205—Devices using data or image processing specially adapted for radiation diagnosis involving processing of raw data to produce diagnostic data
    • 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]
    • A61B6/032—Transmission computed tomography [CT]
    • 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
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2211/00—Image generation
    • G06T2211/40—Computed tomography
    • G06T2211/448—Computed tomography involving metal artefacts, streaking artefacts, beam hardening or photon starvation

Definitions

  • Computers and computing systems have affected nearly every aspect of modem living. Computers are generally involved in work, recreation, healthcare, transportation, entertainment, household management, etc. Computers have developed an increasingly important role in physical security systems. Whereas, digital security systems maintain the security of digital spaces, such as servers and databases, physical security systems assist in maintaining the security of physical spaces, such as airports and large venues.
  • x-ray technology An example area where computers are utilized to assist in maintaining physical security is x-ray technology.
  • x-ray scanners such as CT scanners
  • CT scanners to inspect the contents of luggage being brought onto an airplane.
  • scanning bags provides an important keystone to modem physical security strategies, one will appreciate the significant difficulties involved in scanning luggage. Nearly every item of luggage will be filled with unique items of differing physical compositions and densities. Further, many modem suitcases and bags include metal frame elements that further increase the difficulty of performing meaningful x-ray scans of luggage and its contents.
  • Disclosed embodiments include a computer system for reducing metal artifacts in computer tomography (CT) images.
  • the computer system comprises one or more processors and one or more computer-readable storage devices having stored thereon executable instructions that, when executed by the one or more processors, configure the computer system to perform various acts.
  • the computer system is configured to reconstruct a raw CT image from a CT projection (also referred to as a sinogram) generated via a CT scan.
  • the CT projection includes a matrix of measurements obtained via the CT scan in a projection domain
  • the raw CT image includes a matrix of pixels in an image domain that are reconstructed from the matrix of measurements in the projection domain.
  • the computer system is also configured to identify one or more metal segments in the raw CT images and forward project the one or more metal segments to generate one or more shadows in the projection domain.
  • the computer system is also configured to iteratively adjust measurements in the one or more shadows in the projection domain to reduce metal artifacts in the raw CT image and reconstruct an improved CT image in the image domain based on the adjusted measurements in the one or more shadows and measurements not in the one or more shadows in the projection domain.
  • the disclosed embodiments also include a method implemented at a computer system for reducing metal artifacts in computed tomography (CT) images.
  • the method includes reconstructing a raw CT image from a CT projection.
  • the CT projection includes a matrix of measurements obtained via a CT scan in a projection domain, and the raw CT image includes a matrix of pixels in an image domain that are constructed from the matrix of measurements in the projection domain.
  • the method also includes identifying one or more metal segments in the raw CT image and forward projecting the one or more metal segments to generate one or more shadows in the projection domain.
  • the method further includes iteratively adjusting measurements in the one or more shadows in the projection domain to reduce metal artifacts in the raw CT image and reconstructing an improved CT image based on the adjusted measurements in the one or more shadows and measurements not in the one or more shadows in the projection domain.
  • Disclosed embodiments also include a computer system for identifying one or more features associated with a particular type of artifact in CT images and reducing the particular type of artifact in a given CT image.
  • the computer system comprises one or more processors and one or more computer-readable storage devices having stored thereon executable instructions that, when executed by the one or more processors, configure the computer system to perform various acts.
  • the computer system is configured to access a plurality of labeled CT images as training data and access a machine learning network. Each of the plurality of labeled CT images is labeled as having a particular type of artifact or not having the particular type of artifact.
  • the computer system is also configured to identify one or more artifact features that are associated with the particular type of artifact using the machine learning network and the plurality of labeled CT images.
  • the computer system receives a CT projection, the computer system is configured to reconstruct a raw CT image from the CT projection.
  • the CT projection includes a matrix of measurements obtained via a CT scan in a projection domain, and the raw CT image includes a matrix of pixels in an image domain that are reconstructed from the matrix of measurements in the projection domain.
  • the computer system is also configured to identify one or more artifact segments in the raw CT image and forward projects the one or more artifact segments to generate one or more shadows in the projection domain.
  • the computer system is also configured to iteratively adjust measurements in the one or more shadows in the projection domain to minimize effect of at least one of the one or more artifact features and reconstruct an improved CT image based on the adjusted measurements in the one or more shadows and measurements not in the one or more shadows in the projection domain.
  • Figure 1A illustrates a first airport bag in a conventional FBP reconstruction
  • Figure IB illustrates the first airport bag in a metal segmentation
  • Figure 1C illustrates the first airport bag in a reconstruction after damaged value recovery based on minimizing a total variation norm described herein;
  • Figure ID illustrates the first airport bag in an image-domain TV reconstruction
  • Figure 2A illustrates a second airport bag in a conventional FBP reconstruction
  • Figure 2B illustrates the second airport bag in a metal segmentation
  • Figure 2C illustrates the second airport bag in a reconstruction after damaged value recovery based on minimizing a total variation norm described herein;
  • Figure 2D illustrates the second airport bag in an image-domain TV reconstruction
  • Figure 3A illustrates a third airport bag in a conventional FBP reconstruction
  • Figure 3B illustrates the third airport bag in a metal segmentation
  • Figure 3C illustrates the third airport bag in a reconstruction after damaged value recovery based on minimizing a total variation norm described herein;
  • Figure 3D illustrates the third airport bag in an image-domain TV reconstruction
  • Figure 4A illustrates a fourth airport bag in a conventional FBP reconstruction
  • Figure 4B illustrates the fourth airport bag in a metal segmentation
  • Figure 4C illustrates the fourth airport bag in a reconstruction after damaged value recovery based on minimizing a total variation norm described herein;
  • Figure 4D illustrates the fourth airport bag in an image-domain TV reconstruction
  • Figure 5A illustrates a fifth airport bag in a conventional FBP reconstruction
  • Figure 5B illustrates the fifth airport bag in a metal segmentation
  • Figure 5C illustrates the fifth airport bag in a reconstruction after damaged value recovery based on minimizing a total variation norm described herein;
  • Figure 5D illustrates the fifth airport bag in an image-domain TV reconstruction
  • Figure 6A illustrates a projection mask for the first airport bag
  • Figure 6B illustrates a projection mask for the second airport bag
  • Figure 6C illustrates a projection mask for the third airport bag
  • Figure 6D illustrates a projection mask for the fourth airport bag
  • Figure 6E illustrates a projection mask for the fifth airport bag
  • Figure 7A illustrates a raw FBP reconstruction of the first airport bag
  • Figure 7B illustrates an improved FBP reconstruction of the first airport bag based on minimizing a sum of squares of values of pixels that have negative values described herein;
  • Figure 8A illustrates a raw FBP reconstruction of the second airport bag
  • Figure 8B illustrates an improved FBP reconstruction of the second airport bag based on minimizing a sum of squares of values of pixels that have negative values described herein;
  • Figure 9A illustrates a raw FBP reconstruction of the third airport bag
  • Figure 9B illustrates an improved FBP reconstruction of the third airport bag based on minimizing a sum of squares of values of pixels that have negative values described herein;
  • Figure 10A illustrates a raw FBP reconstruction of the fourth airport bag
  • Figure 10B illustrates an improved FBP reconstruction of the fourth airport bag based on minimizing a sum of squares of values of pixels that have negative values described herein;
  • Figure 11 A illustrates a raw FBP reconstruction of the fifth airport bag
  • Figure 11B illustrates an improved FBP reconstruction of the fifth airport bag based on minimizing a sum of squares of values of pixels that have negative values described herein;
  • Figure 12A illustrates a raw FBP reconstruction image of the first airport bag
  • Figure 12B illustrates an improved reconstruction image of the first airport bag using TV norm minimization
  • Figure 12C illustrates an improved reconstruction image of the first airport bag using negative pixel energy minimization
  • Figure 12D illustrates an improved reconstruction image of the first airport bag using a combination of TV norm minimization and negative pixel energy minimization
  • Figure 13A illustrates a raw FBP reconstruction image of the second airport bag
  • Figure 13B illustrates an improved reconstruction image of the second airport bag using TV norm minimization
  • Figure 13C illustrates an improved reconstruction image of the second airport bag using negative pixel energy minimization
  • Figure 13D illustrates an improved reconstruction image of the second airport bag using a combination of TV norm minimization and negative pixel energy minimization
  • Figure 14A illustrates a raw FBP reconstruction image of the third airport bag
  • Figure 14B illustrates an improved reconstruction image of the third airport bag using TV norm minimization
  • Figure 14C illustrates an improved reconstruction image of the third airport bag using negative pixel energy minimization
  • Figure 14D illustrates an improved reconstruction image of the third airport bag using a combination of TV norm minimization and negative pixel energy minimization
  • Figure 15 A illustrates a raw FBP reconstruction image of the fourth airport bag
  • Figure 15B illustrates an improved reconstruction image of the fourth airport bag using TV norm minimization
  • Figure 15C illustrates an improved reconstruction image of the fourth airport bag using negative pixel energy minimization
  • Figure 15D illustrates an improved reconstruction image of the fourth airport bag using a combination of TV norm minimization and negative pixel energy minimization
  • Figure 16A illustrates a raw FBP reconstruction image of the fifth airport bag
  • Figure 16B illustrates an improved reconstruction image of the fifth airport bag using TV norm minimization
  • Figure 16C illustrates an improved reconstruction image of the fifth airport bag using negative pixel energy minimization
  • Figure 16D illustrates an improved reconstruction image of the fifth airport bag using a combination of TV norm minimization and negative pixel energy minimization
  • Figure 17A illustrates a raw FBP reconstruction image of the fifth airport bag
  • Figure 17B illustrates an improved reconstruction image of the fifth airport bag using TV norm minimization with a different metal segmentation threshold
  • Figure 17C illustrates an improved reconstruction image of the fifth airport bag using negative pixel energy minimization with a different metal segmentation threshold
  • Figure 17D illustrates an improved reconstruction image of the fifth airport bag using a combination of TV norm minimization and negative pixel energy minimization with a different metal segmentation threshold
  • Figure 18A illustrates a metal map of the fifth airport bag based on segmentation threshold of 1/3 of the maximum pixel value
  • Figure 18B illustrates a metal map of the fifth airport bag based on segmentation threshold of 1/10 of the maximum pixel value
  • Figure 19A illustrates a sinogram processed with a method described herein
  • Figure 19B illustrates a raw sinogram generated by a CT scan
  • Figure 19C illustrates a difference between the processed sinogram of Figure 19A and the raw sinogram of Figure 19B;
  • Figure 20 illustrates a flowchart of an example method for reducing metal artifacts in CT images
  • Figure 21 illustrates a flowchart of an example method for using a machine learning network to identify one or more features associated with a particular type of artifact in CT images and removing the artifacts caused by the particular type of artifacts in a given CT image;
  • Figure 22 illustrates a schematic of a computer system for reduction of artifacts in computerized tomography.
  • Computed tomography refers to a computerized x-ray imaging procedure in which a narrow beam of x-rays is aimed at a body of an object and quickly rotated around the body of the object, producing signals that are processed by the machine’s computer to generate a matrix of measurements (also referred to as a projection or a sinogram) in a projection domain.
  • the measurements in the projection domain are machine-readable, but hard to understand by human users.
  • a human-readable CT image in an image domain can be reconstructed from the projection.
  • Different algorithms such as (but not limited to) filtered back projection (FBP), may be used to reconstruct images from projections.
  • FBP filtered back projection
  • the reconstructed images can also be converted back to projections via a forward projection (FP) operation.
  • metal artifacts are caused by beam hardening effects, which are nonlinearly dependent on the metallic materials. These nonlinear effects introduce errors to the line-integral model of the measurements.
  • the line-integral amplitudes are distorted when the integration lines pass through metals. Usually, the distorted line-integral value is smaller than the true value.
  • the distortion is nonlinear and difficult to estimate, because the metallic materials in the objects are unknown. For example, for a collection of random metallic and non-metallic objects, it is almost impossible to establish a beam hardening model to convert the broad-spectrum measurements into pseudo mono-energy measurements so that the metal artifacts can be removed.
  • the embodiments disclosed herein solve the above-described problem by identifying one or more metal segments in a raw CT image, forward projecting the one or more metal segments to generate one or more shadows in a projection domain, iteratively adjusting measurements in the one or more shadows in the projection domain to reduce metal artifacts in the raw CT image, and reconstructing an improved CT image based on the adjusted measurements in the one or more shadows (and measurements not in the one or more shadows) in the projection domain.
  • the iterative adjusting of the measurements in the one or more shadows in the projection domain to reduce the artifacts in the one or more metal segments includes iteratively adjusting the measurements in the one or more shadows to reduce a total variation norm of the matrix of non-metal pixels in the image domain (hereinafter also referred to as “total variation norm minimization”).
  • an optimization method such as (but not limited to) a gradient descent method, is used to minimize the total variation norm of the matrix of non-metal pixels in the image domain.
  • the iterative adjusting of the measurements in the one or more shadows in the projection domain to reduce the artifacts in the one or more metal segments includes identifying pixels in the image domain that have negative values and iteratively adjusting the measurements in the one or more shadows to reduce a sum of squares of values of the pixels in the image domain that have negative values (herein after also referred to as “negative pixel energy minimization”).
  • an optimization method such as (but not limited to) a gradient descent method, is used to minimize the sum of squares of values of the pixels in the image domain that have negative values.
  • the iterative adjusting of the measurements in the one or more shadows in the projection domain to reduce the artifacts in the one or more metal segments includes iteratively adjusting the measurements in the one or more shadows to reduce a combination of (1) a sum of squares of values of the pixels in the image domain that have negative values, and (2) a total variation norm of the matrix of non-metal pixels in the image domain.
  • the combination of (1) the sum of squares of values of the pixels in the image domain that have negative values, and (2) the total variation norm of the matrix of non-metal pixels in the image domain is a weighted combination, in which the sum of squares of values of the pixels in the image domain that have negative values is given a first weight, and the total variation norm of the matrix of non-metal pixels in the image domain is given a second weight.
  • an optimization method such as (but not limited to) a gradient descent method, is used to minimize the weighted combination.
  • the total variation norm and the sum of squares of values of pixels that have negative values are merely two artifact features that are associated with the metal artifacts. There may be additional artifact features that are also associated with the metal artifacts or any other artifacts caused by other materials or circumstances. A similar process may be implemented to minimize the metal artifacts based on other artifact features or minimize other artifacts based on the same or other artifact features.
  • a machine learning network such as (but not limited to) a neural network, a convolutional neural network, is used to identify one or more artifact features based on training data.
  • the training data includes a plurality of CT images, each of which is labeled as good or bad.
  • the images that are labeled good do not have metal artifacts.
  • the images that are labeled bad have metal artifacts.
  • the measurements in the one or more shadows in the projection domain are iteratively adjusted to minimize effect of at least one of the one or more artifact features .
  • a gradient descent method is used to minimize the effect of the at least one of the one or more artifact features.
  • the interactive adjusting of the measurements is to reduce or minimize a total variation norm of the matrix of non- metal pixels of an image in the image domain.
  • T in Equation (1) is used as an objective function for optimizing the image.
  • the partial derivative of T with respect to pixel (i, j) is calculated as: f j f — ij Equation (2) (f i-l,j-fi-l,j+l) 2 +(f i-lj-fij) 2
  • an iterative gradient descent algorithm is implemented to minimize the objective function T defined in Equation (1).
  • the iterative gradient descent algorithm may be defined as:
  • the Randon transform is applied to both sides of
  • Equation (4) to generate the following equation:
  • l is a relaxation parameter.
  • the relaxation parameter may be chosen to be 0.01.
  • the FBP algorithm is used to reconstruct an initial image, which may contain lots of metal artifacts.
  • a threshold value is selected (for example, 1/3 of the maximum pixel value) and used to segment the FBP reconstruction to create a metal object image f metal.
  • the mask denoted as maskmetai, is a function in the projection-domain and is based on the Radon transform of f metal.
  • the mask function maskmetai is 1 at the location that the Radon transform of fmetai is positive and is 0 at the location that the Radon transform of fmetai is 0.
  • the mask function restricts Equation (5) only on the region when maskmetai is 1.
  • Equation (6) updates the line-integral measurements that the projection rays pt, e(metal) touch the metal objects.
  • Equation (6) may smooth out some or all metallic objects while minimizing the TV norm in Equation (1).
  • the image-domain masking function fmetai can be used to hide the metals in TV gradient image.
  • Ui j is replaced with Ui j x ( 1 - fmetai) in Equation (6) to generate the following equation:
  • Equation (7) can then be implemented in the following steps: [00105] (i) obtain a raw FBP reconstruction (i.e., a raw image reconstructed via FBP);
  • the experimental data was acquired with an Imatron C300 clinical CT scanner, which was a fifth generation CT scanner based on a scanning electron beam X-ray source.
  • the number of views was 180 over 180°.
  • the number of channels i.e., the detection bin at each view
  • the projections used the parallel-beam imaging geometry.
  • the reconstructed image size was 420 x 420.
  • Figures 1 A- ID, 2A-2D, 3A-3D, 4A-4D, 5A-5D, and 6A-6E depict five airport- bag examples that illustrate the effectiveness of the method described herein.
  • Figures 1A, 2A, 3A, 4A, and 5A depict the raw FBP reconstruction of the x-ray of each respective bag.
  • Figures IB, 2B, 3B, 4B, and 5B illustrate the segmented metal image f metai of the x-ray of each respective bag.
  • Figures 1C, 2C, 3C, 4C, and 5C illustrate the final FBP reconstruction using the restored projections of the x-ray of each respective bag in accordance with embodiments of the present invention.
  • Figures ID, 2D, 3D, 4D, and 5D illustrate the reconstructed image using the image-domain TV iterative algorithm.
  • the relaxation parameter for the data fidelity term was 0.001
  • the relaxation parameter for the TV constraint term was 0.005.
  • the number of iterations was 400 for both the projection-domain iteration algorithm described herein and for the image-domain iterative TV algorithm.
  • the reconstructions are displayed from the minimum pixel value to 0.3 times the maximum pixel value. The results from these five examples are shown in Figures 1A-1D, 2A-2D, 3A-3D, 4A-4D, and 5A-5D, respectively.
  • Figures 6A-6E show the projection masks for the measurements of bags 1 to 5, respectively. These are binary images with values 0 (black) and 1 (white). The white regions indicate the projection values that are adjusted by the algorithm described herein. The black regions indicate the projection values that are kept unchanged. [00112] In this section, three methods are compared in terms of their performance on correcting metal-induced errors. These three methods are: (1) the conventional FBP algorithm, (2) the TV-minimization algorithm with image-domain update, and (3) the algorithm described herein (i.e., TV-minimization algorithm with projection-domain update). For the five airport bags, the reconstructed images using these three methods are shown in Figures 1A-1D, 2A-2D, 3A-3D, 4A-4D, and 5A-5D, respectively.
  • the metal obj ect-induced artifacts appear as dark undershoots around the bright metal objects.
  • Visual assessments indicate that the conventional FBP algorithm (shown in Figures 1 A, 2A, 3A, 4A, and 5A) gives the most severe artifacts, the image-domain-update TV algorithm (shown in Figures ID, 2D, 3D, 4D, and 5D) somewhat reduces the artifacts with worsened spatial resolution, and the algorithm described herein (shown in Figures 1C, 2C, 3C, 4C, and 5C) is most effective in metal artifact reduction.
  • the metal streaking artifacts can be reflected by the image TV norm.
  • the method described herein minimizes the image-domain TV norm of the FBP reconstruction.
  • This algorithm does not use any models for unreliable projections. Accordingly, this algorithm can also be applied to many applications other than removing beam-hardening artifacts.
  • Objective Function Associated with Negative Pixel Energy Minimization [00117] Additionally, in at least one embodiment, the metal artifacts have regions with negative pixel values in the FBP reconstruction. The principles described herein also include using an objective function associated with the negative pixel values. [00118] Let A be the operator of the FBP algorithm, P be proj ection measurements, and
  • X be the FBP reconstruction. Both P and X are expressed in the vector form, and A is expressed in the matrix form.
  • the FBP reconstruction X is AP.
  • the elements in X are xi.
  • an objective function is a squared L2-norm of Y, defined as the following equation:
  • Equation (9) [00123] The objective function defined by Equation (9) is then minimized to optimize the FBP reconstruction image.
  • the variables for this objective function are metal affected projections PM.
  • the entries in PM are determined by the following steps:
  • (i) use the FBP algorithm to generate a raw image X mw using projection measurements P.
  • the raw image may contain severe metal artifacts.
  • (ii) segment the raw image to obtain a metal-only image, using a threshold value, for example, as the 1/3 of the maximum pixel value of A,-,,,, ⁇ where all pixel values that are smaller than this threshold value are set to zero; and [00126] (iii) forward project the metal-only image to obtain the indices of PM.
  • a gradient descent algorithm is used to minimize the objective function defined by Equation (9) by updating the variables in PM.
  • Let pj be an entry in Pm.
  • To find the gradient of dF/dp j is not straightforward, because the min function in Equation (8) makes Equation (8) undifferentiable.
  • a T is the adjoint operator of the FBP algorithm and min (0, APj sets each positive entry of AP to zero.
  • AP is the FBP image reconstruction using projections P
  • a 1 is the forward projection followed by the ramp filtration with the one-directional (ID) convolution kernel, which is defined as: [00131]
  • the gradient descent iterative algorithm is given as
  • Equation (12) The parameter b in Equation (12) controls the step size of the gradient descent algorithm.
  • Figures 7A-7B, 8A-8B, 9A-9B, 10A-10B, and 1 lA-1 IB illustrate five different airport bags.
  • Figures 7A, 8A, 9A, 10A, and 11 A illustrate raw FBP reconstruction images
  • Figures 7B, 8B, 9B, 10B, and 11B illustrate the improved images processed by the method described herein.
  • the negative values are shown as the darkest color
  • the metals are shown as the brightest color.
  • the display window is set to [-0.1a, 0.5a], where a is the maximum pixel value.
  • the display window for the raw image and the display window for the final image are the same.
  • the negative image pixel values only appear in the closed neighborhood of the metals in the raw FBP reconstruction images.
  • the negative image pixels are significantly removed, and dark streaking artifacts are also reduced. Such improvement cannot be achieved by merely setting the negative image pixel values to zero in the raw FBP reconstructions.
  • the method described herein also reduces angularly aliasing artifacts (due to insufficient view angles).
  • an objective function associated with both TV and pixels having negative values may be implemented.
  • an objective function may be defined as:
  • T is defined by Equation (1)
  • F is defined by Equation (9)
  • [h and fh are two parameters whose values may be selected based on the conditions of the images.
  • the FBP reconstruction images are optimized by minimizing the objective function defined by Equation (13).
  • Figures 12A-12D, 13A-13D, 14A-14D, 15A-15D, and 16A-16D show the reconstruction results of five different unknown airport bags. For each airport bag, four images are shown.
  • Figures 12A, 13 A, 14A, 15A, and 16A are original raw FBP reconstruction images of the five airport bags.
  • the minimum raw FBP value (which may be negative) is mapped to the gray level 0 (black).
  • the mapping is linear in the range below 1/3 of the maximum value. All four images for a same airport bag use the same gray-scale mapping. Therefore, black pixels are the “negative pixels.”
  • the number of iterations for all experiments was 1000 in the gradient descent algorithm.
  • the image array size was 420 x 420, and the pixel size was 0.92 mm.
  • Figure 12A illustrates a raw FBP reconstruction image of the first airport bag (also referred to as “Bag 1”)
  • Figure 12B illustrates an improved reconstruction image of Bag 1 using TV norm minimization
  • Figure 12C illustrates an improved reconstruction Bag 1 using negative pixel energy minimization
  • Figure 12D illustrates an improved reconstruction image of Bag 1 using a combination of TV norm minimization and negative pixel energy minimization.
  • Table 2 below illustrates the minimum and maximum values in the different reconstruction images shown in Figures 12A-12D. Table 2
  • Figure 13A illustrates a raw FBP reconstruction image of the second airport bag (also referred to as “Bag 2”)
  • Figure 13B illustrates an improved reconstruction image of Bag 2 using TV norm minimization
  • Figure 13C illustrates an improved reconstruction Bag 2 using negative pixel energy minimization
  • Figure 13D illustrates an improved reconstruction image of Bag 2 using a combination of TV norm minimization and negative pixel energy minimization.
  • Table 3 below illustrates the minimum and maximum values in the different reconstruction images shown in Figures 13A-13D.
  • Figure 14A illustrates a raw FBP reconstruction image of the third airport bag (also referred to as “Bag 3”)
  • Figure 14B illustrates an improved reconstruction image of Bag 3 using TV norm minimization
  • Figure 14C illustrates an improved reconstruction Bag 3 using negative pixel energy minimization
  • Figure 14D illustrates an improved reconstruction image of Bag 3 using a combination of TV norm minimization and negative pixel energy minimization.
  • Table 4 below illustrates the minimum and maximum values in the different reconstruction images shown in Figures 14A-14D.
  • Figure 15 A illustrates a raw FBP reconstruction image of the fourth airport bag (also referred to as “Bag 4”)
  • Figure 15B illustrates an improved reconstruction image of Bag 4 using TV norm minimization
  • Figure 15C illustrates an improved reconstruction Bag 4 using negative pixel energy minimization
  • Figure 15D illustrates an improved reconstruction image of Bag 4 using a combination of TV norm minimization and negative pixel energy minimization.
  • Table 5 below illustrates the minimum and maximum values in the different reconstruction images shown in Figures 15A-15D.
  • Figure 16A illustrates a raw FBP reconstruction image of the fifth airport bag (also referred to as “Bag 5”)
  • Figure 16B illustrates an improved reconstruction image of Bag 5 using TV norm minimization
  • Figure 16C illustrates an improved reconstruction Bag 5 using negative pixel energy minimization
  • Figure 16D illustrates an improved reconstruction image of Bag 5 using a combination of TV norm minimization and negative pixel energy minimization.
  • Table 6 below illustrates the minimum and maximum values in the different reconstruction images shown in Figures 16A-16D.
  • the resultant images shown in Figures 12D, 13D, 14D, 15D, and 16D not only provide additional low-contrast structures compared to the resultant images shown in Figures 12B, 13B, 14B, 15B, and 16B, but also reduce noises compared to the resultant images shown in Figures 12C, 13C, 14C, 15C, and 16C.
  • the threshold value for segmenting the metal images and/or the values of the parameters bi and /3 ⁇ 4 can also be adjusted to further improve the results.
  • Figures 17A-17D illustrate another set of images of bag 5 (same as that in Figures 16A-16D).
  • Figure 17A illustrates a raw FBP reconstruction image
  • Table 7 illustrates the minimum and maximum values in the different reconstruction images shown in Figures 17A-17D
  • Figures 18A-18B illustrate as illustrated two different metal maps of Bag 5 with different thresholds.
  • Figure 18A illustrates a metal map of Bag 5based on a segmentation threshold of 1/3 of the maximum pixel value
  • Figure 18B illustrates a metal map of Bag 5based on a segmentation threshold of 1/10 maximum pixel value.
  • the segmentation threshold is set as 1/10 maximum pixel value, it is advantageous to change the value of bi from 0.004 to 0.0002 to prevent the resulting image from being too smooth or losing too many details.
  • Figures 19A-19C illustrate different sinograms (i.e., projections) processed with different parameters fii and/or /C
  • Figure 19B shows the raw sinogram
  • Figure 19C illustrates a difference between the raw sinogram (shown in Figure 19B) and the processed sinogram (shown in Figure 19 A).
  • the TV norm and negative pixels energy are two features that are associated with metal artifacts. There may be additional features that are also associated with metal artifacts and/or any other artifacts caused by different materials and/or circumstances.
  • a machine learning network e.g., (but not limited to) a neural network, a convolutional neural network, is implemented to identify one or more features that are associated with a particular type of artifacts.
  • supervised learning is used to identify the one or more features.
  • a plurality of labeled images are used as training data.
  • an objective function associated with the one or more features is defined to minimize the particular type of artifacts.
  • FIG 20 illustrates a flowchart of an example method 2000 for reducing metal artifacts in computerized tomography (CT) images.
  • the method 2000 includes reconstructing a raw CT image in an image domain from a CT projection domain (act 2010).
  • the CT projection includes a matrix of measurements in the projection domain obtained via a CT scan, and the raw CT image includes a matrix of pixels in an image domain that are reconstructed from the matrix of measurements in the projection domain.
  • the method 2000 also includes identifying one or more metal segments in the raw CT image (act 2020) and forward projects the one or more metal segments to generate one or more shadows in the projection domain (act 2030).
  • Measurements in the one or more shadows in the projection domain are then iteratively adjusted to reduce metal artifacts in the raw CT image (act 2040).
  • the adjusted measurements in the one or more shadows in the projection domain are then used to reconstruct an improved CT image (act 2050).
  • the reconstructing of the raw CT image and/or reconstructing the improved CT image are performed via a filtered back projection (FBP) method.
  • FBP filtered back projection
  • the iterative adjusting of the measurements in the one or more shadows in the projection domain is to iteratively reduce a total variation norm of the matrix of non-metal pixels in the image domain.
  • a gradient descent method is used to minimize the total variation norm of the matrix of non-metal pixels in the image domain.
  • the iterative adjusting of the measurements in the one or more shadows in the projection domain is to iteratively reduce a sum of squares of values of pixels that have negative values.
  • a gradient descent method is used to minimize the sum of squares of values of the pixels that have negative values.
  • the iterative adjusting the measurements in the one or more shadows in the projection domain is to iteratively reduce a combination of (1) a sum of squares of values of pixels that have negative values and (2) a total variation norm of the matrix of pixels in the image domain.
  • the combination is a weighted combination, in which the sum of squares of values of pixels that have negative values is given a first weight, and the total variation norm of the matrix of pixels in the image domain is given a second weight.
  • the gradient descent method is used to minimize the weighted combination.
  • Figure 21 illustrates a flowchart of an example method 2100 for using machine learning to identify one or more features associated with artifacts in CT images and reconstructing an improved CT image to reduce the artifacts.
  • the method 2100 includes providing a plurality of labeled CT images as training data (act 2110).
  • the labeled CT images are labeled as having a particular type of artifact or not having the particular type of artifact.
  • the method further includes providing a machine learning network (act 2120), such as (but not limited to) a neural network, or a convolutional neural network.
  • the method 2100 further includes identifying one or more features associated with the particular type of artifact using the machine learning network and the plurality of labeled CT images (act 2130).
  • the method 2100 further includes receiving a CT projection in a projection domain (act 2140) and reconstructing a raw CT image in an image domain from the CT projection in the projection domain (act 2150).
  • the CT projection includes a matrix of measurements in a projection domain obtained via a CT scan, and the raw CT image includes a matrix of pixels in an image domain reconstructed from the matrix of measurements in the projection domain.
  • the method 2100 further includes identifying one or more artifact segments in the raw CT image (act 2160), and forward projecting the one or more artifact segments to generate one or more shadows in the projection domain (act 2170).
  • Measurements in the one or more shadows in the projection domain are iteratively adjusted to reduce the particular type of artifact in the raw CT image (act 2180).
  • the adjusted measurements in the one or more shadows in the projection domain are then used to reconstruct an improved CT image in the image domain (act 2190).
  • Figure 22 illustrates a schematic of a computer system 2200 for reducing artifacts in computerized tomography.
  • the computer system 2200 includes one or more processors 2230 and computer memory 2240 that are both accessible through a software application 2220.
  • the computer system 2200 is in communication in a beam wave device 2210.
  • the beam wave device may comprise an x-ray machine or any other image device.
  • the computer system 2200 is configured to reduce artifacts in computerized tomography.
  • the methods described above may be practiced by a computer system (e.g., the computer system 2200 of Figure 22), including one or more processors (e.g., 2230 of Figure 22) and computer-readable media such as computer memory (e.g., computer memory 2240 of Figure 22).
  • the computer memory may store computer- executable instructions that, when executed by one or more processors, cause various functions to be performed, such as the acts recited in the embodiments.
  • Computing system functionality can be enhanced by a computing systems’ ability to be interconnected to other computing systems via network connections.
  • Network connections may include, but are not limited to, connections via wired or wireless Ethernet, cellular connections, or even computer to computer connections through serial, parallel, USB, or other connections. The connections allow a computing system to access services at other computing systems and to quickly and efficiently receive application data from other computing systems.
  • cloud computing may be systems or resources for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, services, etc.) that can be provisioned and released with reduced management effort or service provider interaction.
  • configurable computing resources e.g., networks, servers, storage, applications, services, etc.
  • a cloud model can be composed of various characteristics (e.g., on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, etc.), service models (e.g., Software as a Service (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”), and deployment models (e.g., private cloud, community cloud, public cloud, hybrid cloud, etc.).
  • service models e.g., Software as a Service (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”)
  • deployment models e.g., private cloud, community cloud, public cloud, hybrid cloud, etc.
  • Cloud and remote-based service applications are prevalent. Such applications are hosted on public and private remote systems such as clouds and usually offer a set of web-based services for communicating back and forth with clients.
  • computers are intended to be used by direct user interaction with the computer.
  • computers have input hardware and software user interfaces to facilitate user interaction.
  • a modem general-purpose computer may include a keyboard, mouse, touchpad, camera, etc., for allowing a user to input data into the computer.
  • various software user interfaces may be available.
  • Disclosed embodiments may comprise or utilize a special purpose or general- purpose computer, including computer hardware, as discussed in greater detail below. Disclosed embodiments also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. Such computer-readable media can be any available media that can be accessed by a general- purpose or special-purpose computer system. Computer-readable media that store computer-executable instructions are physical storage media. Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the invention can comprise at least two distinctly different kinds of computer-readable media: physical computer-readable storage media and transmission computer-readable media.
  • Physical computer-readable storage media includes RAM, ROM, EEPROM, CD-ROM or other optical disk storage (such as CDs, DVDs, etc.), magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general-purpose or special purpose computer.
  • a “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices.
  • a network or another communications connection can include a network and/or data links which can be used to carry program code in the form of computer-executable instructions or data structures and which can be accessed by a general-purpose or special purpose computer. Combinations of the above are also included within the scope of computer-readable media.
  • program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission computer-readable media to physical computer-readable storage media (or vice versa).
  • program code means in the form of computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and/or to less volatile computer-readable physical storage media at a computer system.
  • NIC network interface module
  • computer-readable physical storage media can be included in computer system components that also (or even primarily) utilize transmission media.
  • Computer-executable instructions comprise, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions.
  • the computer- executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code.
  • the invention may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, pagers, routers, switches, and the like.
  • the invention may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks.
  • program modules may be located in both local and remote memory storage devices.
  • the functionality described herein can be performed, at least in part, by one or more hardware logic components.
  • illustrative types of hardware logic components include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

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Abstract

L'invention concerne un système informatique qui permet de réduire des artéfacts métalliques dans des images de tomodensitométrie (CT) et qui est configuré de sorte à reconstruire une image CT brute à partir d'une projection CT. La projection CT comprend une matrice de mesures dans un domaine de projection obtenu par l'intermédiaire d'un balayage CT, et l'image CT brute comprend une matrice de pixels dans un domaine d'image reconstruit à partir de la matrice de mesures dans le domaine de projection. Le système informatique identifie des segments métalliques dans l'image CT brute et projette vers l'avant les segments métalliques pour générer des ombres dans le domaine de projection. Le système informatique ajuste ensuite de manière itérative les mesures dans les ombres dans le domaine de projection afin de réduire les artéfacts métalliques dans l'image CT brute et reconstruit une image CT améliorée sur la base des mesures ajustées dans les ombres et des mesures non dans les ombres du domaine de projection.
PCT/US2021/036284 2020-06-08 2021-06-08 Réduction d'artefacts dans des clichés de tomodensitométrie WO2021252410A1 (fr)

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TWI808817B (zh) * 2022-06-27 2023-07-11 臺北醫學大學 X光片內組織尺寸的量測方法
CN116823977A (zh) * 2023-03-16 2023-09-29 闽都创新实验室 一种微纳结构透射电子断层图像重构的深度学习方法
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Publication number Priority date Publication date Assignee Title
TWI808817B (zh) * 2022-06-27 2023-07-11 臺北醫學大學 X光片內組織尺寸的量測方法
CN116823977A (zh) * 2023-03-16 2023-09-29 闽都创新实验室 一种微纳结构透射电子断层图像重构的深度学习方法
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CN117152291A (zh) * 2023-09-12 2023-12-01 天津师范大学 一种基于原对偶算法的非凸加权变分金属伪影去除方法
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