Reducing metal artifacts by restricting negative pixels

Zeng, G.L.; Zeng, Megan

doi:10.1186/s42492-021-00083-z

Cited by 4 publications

(8 citation statements)

References 13 publications

(14 reference statements)

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“…This paper is inspired by the two observations that the metal artifacts often result in dark/bright streaks radiating from the metals [ 25 ] and negative image pixels around the metals [ 26 ]. The streaking artifacts increase the TV norm of the non-metal regions of the image.…”

Section: Methodsmentioning

confidence: 99%

“…The attenuation coefficients cannot be negative. These two effects were dealt with in two papers, separately [ 16 , 26 ]. The goal of this current paper is to combine these two methods into one.…”

Section: Methodsmentioning

confidence: 99%

“…The negative undershoots may result in negative image pixels. Our proposed algorithm is to minimize the TV of the metal-removed image [ 25 ] and the energy of the negative image pixels [ 26 ]. The algorithm variables are the metal affected sinogram values.…”

Section: Introductionmentioning

confidence: 99%

“…We must point out that it is not straightforward to reflect the negative image pixel constraint in the sinogram domain. It is not equivalent to setting the associated sinogram values to zero or positive [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

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A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

Zeng

2022

Vis. Comput. Ind. Biomed. Art

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Metal objects in X-ray computed tomography can cause severe artifacts. The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods. This paper proposes a projection-domain algorithm to reduce the metal artifacts. In this algorithm, the unknowns are the metal-affected projections, while the objective function is set up in the image domain. The data fidelity term is not utilized in the objective function. The objective function of the proposed algorithm consists of two terms: the total variation of the metal-removed image and the energy of the negative-valued pixels in the image. After the metal-affected projections are modified, the final image is reconstructed via the filtered backprojection algorithm. The feasibility of the proposed algorithm has been verified by real experimental data.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

Zeng

2022

Vis. Comput. Ind. Biomed. Art

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“…Another version of inpainting is more successful. [12][13][14] This version is different from the previous version in that the newer version estimate the projections with the metals in the object. It does not average the neighboring values.…”

Section: Introductionmentioning

confidence: 99%

Neural network guided sinogram‐domain iterative algorithm for artifact reduction

Zeng

2023

Medical Physics

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BackgroundArtifact reduction or removal is a challenging task when the artifact creation physics are not well modeled mathematically. One of such situations is metal artifacts in x‐ray CT when the metallic material is unknown, and the x‐ray spectrum is wide.PurposeA neural network is used to act as the objective function for iterative artifact reduction when the artifact model is unknown.MethodsA hypothetical unpredictable projection data distortion model is used to illustrate the proposed approach. The model is unpredictable, because it is controlled by a random variable. A convolutional neural network is trained to recognize the artifacts. The trained network is then used to compute the objective function for an iterative algorithm, which tries to reduce the artifacts in a computed tomography (CT) task. The objective function is evaluated in the image domain. The iterative algorithm for artifact reduction is in the projection domain. A gradient descent algorithm is used for the objective function optimization. The associated gradient is calculated with the chain rule.ResultsThe learning curves illustrate the decreasing treads of the objective function as the number of iterations increases. The images after the iterative treatment show the reduction of artifacts. A quantitative metric, the Sum Square Difference (SSD), also indicates the effectiveness of the proposed method.ConclusionThe methodology of using a neural network as an objective function has potential value for situations where a human developed model is difficult to describe the underlying physics. Real‐world applications are expected to be benefit from this methodology.

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Metal artifact reduction method based on single spectral CT (MARSS)

Zhu,

Zhou,

Zhang

et al. 2024

Medical Physics

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BackgroundFor patients with metal implants, computed tomography (CT) imaging results suffer from metal artifacts, which seriously affect image evaluation and even lead to misdiagnosis. Because spectral CT technology has the advantage of quantitative imaging, basis material decomposition, and so on, the current metal artifact reduction methods are utilizing spectral information to reduce metal artifacts with good results. However, they usually require projection data from multiple spectra or energy‐windows, which is difficult to realize in conventional CT.PurposeTo satisfy the status quo, the aim of this work is to propose a metal artifact reduction (MAR) method based on single spectral CT (MARSS). By incorporating prior information, the average density of some base materials, and a constrained image reconstruction model is established. It forces the solution spaces of the materials to be discrete and finite, making the model easier to solve.MethodsThe MARSS method uses the idea of discrete tomography to establish a constrained reconstruction model. By incorporating priori knowledge, the constraint forces the solution spaces of some materials to be discrete, which can effectively downsize the solution space and reduce the ill‐posedness of this problem. Then, an iteration algorithm is developed to solve this model. This algorithm iterates alternately between reconstruction and discretization. It ensures that the solution spaces are discrete while the polychromatic projection of the reconstructed image converges to that of the scanned object.ResultsThe MRASS method significantly reduces artifacts and restores structures near metal to a large extent. Unlike the comparison MAR methods, it effectively prevents the introduction of new artifacts and distortion of the structure.ConclusionsThe MARSS method can achieve MAR based on single spectral CT. Subjective and quantitative evaluation of the results show that the method significantly improves image quality compared to competing methods.

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Reducing metal artifacts by restricting negative pixels

Cited by 4 publications

References 13 publications

A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

Neural network guided sinogram‐domain iterative algorithm for artifact reduction

Metal artifact reduction method based on single spectral CT (MARSS)

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