Fast Enhanced CT Metal Artifact Reduction Using Data Domain Deep Learning

Ghani, Muhammad Usman; Karl, W.C.

doi:10.1109/tci.2019.2937221

Cited by 109 publications

(56 citation statements)

References 35 publications

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“…Then, to reconstruct from low-quality measurements, we apply R θ to generate high-quality measurements and reconstruct using a conventional method. This is sometimes called data domain learning and has been used for metal artifact removal in X-ray CT [97]. Another, similar, variation on the setting is learning to regress from a reconstruction from one measurement type to a reconstruction from another, for example, to infer one MRI scan type from another [98,99].…”

Section: Other Designsmentioning

confidence: 99%

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

McCann

Unser

2019

FNT in Signal Processing

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Section: Other Designsmentioning

confidence: 99%

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

McCann

Unser

2019

FNT in Signal Processing

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“…[21], a review was provided for the state-of-art technologies in metal artifact reduction, and the limitations of these technologies were also pointed out. Most recently, machine leaning methods are explored to battle the metal artifacts in CT [22][23][24][25]. In ref.…”

Section: Introductionmentioning

confidence: 99%

“…[22], an unsupervised deep neural network artifact disentanglement network was proposed to decouple the metal artifacts and the CT images for clinical applications. Reference [23] suggested a conditional generative adversarial network CGAN for data domain sinogram [24] reported a convolutional neural network based metal artifact reduction (CNN-MAR) framework. It was an artifact reduction framework able to distinguish tissue structures from artifacts and fuse the meaningful information to yield a CNN image.…”

Section: Introductionmentioning

confidence: 99%

“…In the second category [21][22][23][24][25], the beam hardening effects are "learned" from a large set of measurements with and without metals. The learned model is automatically achieved after the training phase.…”

Section: Introductionmentioning

confidence: 99%

“…[ 22 ], an unsupervised deep neural network artifact disentanglement network was proposed to decouple the metal artifacts and the CT images for clinical applications. Reference [ 23 ] suggested a conditional generative adversarial network CGAN for data domain sinogram completion. Reference [ 24 ] reported a convolutional neural network based metal artifact reduction (CNN-MAR) framework.…”

Section: Introductionmentioning

confidence: 99%

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Projection-domain iteration to estimate unreliable measurements

Zeng

2020

Vis. Comput. Ind. Biomed. Art

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Due to the beam-hardening effect of the broad energy spectrum of the X-ray source in computed tomography, the reconstructed images usually suffer from severe artifacts when metallic objects are being imaged. Metal artifact correction methods are usually sophisticated and not practical, especially in some non-medical applications, in which the linear attenuation coefficients are unknown. This paper suggests a simple and effective algorithm to estimate the unreliable measurements. The proposed algorithm is an iterative algorithm, in which the iteration is performed in the projection domain, while the objective function is set up in the image domain. The final image is reconstructed with the conventional filtered backprojection algorithm. The feasibility of the proposed method is verified with airport bags that contain some unknown metals.

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A component method to delineate surgical spine implants for proton Monte Carlo dose calculation

Chang

Zhou

Harms

et al. 2022

J Applied Clin Med Phys

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Purpose Metallic implants have been correlated to local control failure for spinal sarcoma and chordoma patients due to the uncertainty of implant delineation from computed tomography (CT). Such uncertainty can compromise the proton Monte Carlo dose calculation (MCDC) accuracy. A component method is proposed to determine the dimension and volume of the implants from CT images. Methods The proposed component method leverages the knowledge of surgical implants from medical supply vendors to predefine accurate contours for each implant component, including tulips, screw bodies, lockers, and rods. A retrospective patient study was conducted to demonstrate the feasibility of the method. The reference implant materials and samples were collected from patient medical records and vendors, Medtronic and NuVasive. Additional CT images with extensive features, such as extended Hounsfield units and various reconstruction diameters, were used to quantify the uncertainty of implant contours. Results For in vivo patient implant estimation, the reference and the component method differences were 0.35, 0.17, and 0.04 cm 3 for tulips, screw bodies, and rods, respectively. The discrepancies by a conventional threshold method were 5.46, 0.76, and 0.05 cm 3 , respectively. The mischaracterization of implant materials and dimensions can underdose the clinical target volume coverage by 20 cm 3 for a patient with eight lumbar implants. The tulip dominates the dosimetry uncertainty as it can be made from titanium or cobalt–chromium alloys by different vendors. Conclusions A component method was developed and demonstrated using phantom and patient studies with implants. The proposed method provides more accurate implant characterization for proton MCDC and can potentially enhance the treatment quality for proton therapy. The current proof‐of‐concept study is limited to the implant characterization for lumbar spine. Future investigations could be extended to cervical spine and dental implants for head‐and‐neck patients where tight margins are required to spare organs at risk.

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Fast Enhanced CT Metal Artifact Reduction Using Data Domain Deep Learning

Cited by 109 publications

References 35 publications

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Projection-domain iteration to estimate unreliable measurements

A component method to delineate surgical spine implants for proton Monte Carlo dose calculation

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