ArticleQuantitative imaging in medicine and surgery2019
Shading correction for volumetric CT using deep convolutional neural network and adaptive filter.
Article in Quantitative imaging in medicine and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A review of deep learning-based three-dimensional medical image registration methods.Quantitative imaging in medicine and surgery · 2021Review
- A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT.Frontiers in oncology · 2021Article
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9 authors.
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Abstract
backgroundShading artifact may lead to CT number inaccuracy, image contrast loss and spatial non-uniformity (SNU), which is considered as one of the fundamental limitations for volumetric CT (VCT) application. To correct the shading artifact, a novel approach is proposed using deep learning and an adaptive filter (AF).
methodsFirstly, we apply the deep convolutional neural network (DCNN) to train a human tissue segmentation model. The trained model is implemented to segment the tissue. According to the general knowledge that CT number of the same human tissue is approximately the same, a template image without shading artifact can be generated using segmentation and then each tissue is filled with the corresponding CT number of a specific tissue. By subtracting the template image from the uncorrected image, the residual image with image detail and shading artifact are generated. The shading artifact is mainly low-frequency signals while the image details are mainly high-frequency signals. Therefore, we proposed an adaptive filter to separate the shading artifact and image details accurately. Finally, the estimated shading artifacts are deleted from the raw image to generate the corrected image.
resultsOn the Catphan©504 study, the error of CT number in the corrected image's region of interest (ROI) is reduced from 109 to 11 HU, and the image contrast is increased by a factor of 1.46 on average. On the patient pelvis study, the error of CT number in selected ROI is reduced from 198 to 10 HU. The SNU calculated from the ROIs decreases from 24% to 9% after correction.
conclusionsThe proposed shading correction method using DCNN and AF may find a useful application in future clinical practice.
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