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Photon Starvation Artifact Reduction by Shift-Variant Processing
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AI
DOI：10.1109/ACCESS.2022.3142775.png)
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摘要
En 中文
The x-ray computed tomography (CT) images with low dose are noisy and may contain photon starvation artifacts. The artifacts are location and direction dependent. Therefore, the common shift-invariant denoising filters do not work well. The state-of-the-art methods to process the low-dose CT images are image reconstruction based; they require the raw projection data. In many situations, the raw CT projections are not accessible. This paper suggests a method to denoise the low-dose CT image using the pseudo projections generated by the application of a forward projector on the low-dose CT image. The feasibility of the proposed method is demonstrated by real clinical data.
Keyword:
Nonlinear filters
Maximum likelihood detection
Image reconstruction
Computed tomography
X-ray imaging
Noise measurement
Photonics
Image processing
image reconstruction
biomedical imaging
computed tomography
filters
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
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Domain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging域渐进三维残差卷积网络改善低剂量CT成像
PREAI
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