Projection-domain iteration to estimate unreliable measurements

Vis. Comput. Ind. Biomed. Art

2021

Self Cite

When the object contains metals, its x-ray computed tomography (CT) images are normally affected by streaking artifacts. These artifacts are mainly caused by the x-ray beam hardening effects, which deviate the measurements from their true values. One interesting observation of the metal artifacts is that certain regions of the metal artifacts often appear as negative pixel values. Our novel idea in this paper is to set up an objective function that restricts the negative pixel values in the image. We must point out that the naïve idea of setting the negative pixel values in the reconstructed image to zero does not give the same result. This paper proposes an iterative algorithm to optimize this objective function, and the unknowns are the metal affected projections. Once the metal affected projections are estimated, the filtered backprojection algorithm is used to reconstruct the final image. This paper applies the proposed algorithm to some airport bag CT scans. The bags all contain unknown metallic objects. The metal artifacts are effectively reduced by the proposed algorithm.

Section: Discussionmentioning

confidence: 99%

Reducing metal artifacts by restricting negative pixels

Vis. Comput. Ind. Biomed. Art

2021

Self Cite

“…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%

“…There are other methods to be considered. Bayesian algorithms, for example, the total variation (TV) norm minimization can be considered [ 21 – 25 ]. The metal artifacts appear as bright or dark streaks, radiated from the metals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Vis. Comput. Ind. Biomed. Art

2022

Self Cite

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.

“…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

2023

Medical Physics

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.