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Directly Filtering the Sparse-View CT Images by BM3D BM3D直接过滤稀疏视图CT图像
SL clinical medicine : research Pub Date : 2022-10-07 DOI: 10.1117/12.2646426
G. Zeng
{"title":"Directly Filtering the Sparse-View CT Images by BM3D","authors":"G. Zeng","doi":"10.1117/12.2646426","DOIUrl":"https://doi.org/10.1117/12.2646426","url":null,"abstract":"The x-ray computed tomography (CT) images with sparse-view data acquisition contain severe angular aliasing artifacts. The common denoising filters do not work well. The state-of-the-art methods to process the sparse-view CT images are deep learning based; they require a large amount of training data pairs. This paper considers a situation where no training data sets are available. All we have is one sparse scan of a patient. This paper attempts to use a BM3D filter to reduce the artifacts by introducing an artifact power spectral density function, which is calculated with computer simulations. The results in this paper show that the proposed method is not effective enough for practice applications. However, some insights may lead us to further investigations.","PeriodicalId":74805,"journal":{"name":"SL clinical medicine : research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76021901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Directly Filtering the Sparse-View CT Images by BM3D. BM3D直接过滤稀疏视图CT图像。
SL clinical medicine : research Pub Date : 2022-01-01
Gengsheng L Zeng
{"title":"Directly Filtering the Sparse-View CT Images by BM3D.","authors":"Gengsheng L Zeng","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The x-ray Computed Tomography (CT) images with sparse-view data acquisition contain severe angular aliasing artifacts. The common denoising filters do not work well if they are used to reduce the artifacts. The state-of-the-art methods to process the sparse-view CT images are deep-learning based; they require a large amount of training data pairs. This paper considers a situation where no clinical training data sets are available. All we have is one sparse scan of a patient. This paper attempts to use a BM3D filter to reduce the artifacts by using an artifact power spectral density function, which is calculated with computer simulations. The results in this paper show that the proposed method is promising in computer simulations. The proposed method has been applied to patient data, and we observe that the sparse-view artifacts are reduced, especially in the central region of the image, but the artifact reduction is not as effective at the peripheral if the control parameter in the BM3D filter is not properly chosen.</p>","PeriodicalId":74805,"journal":{"name":"SL clinical medicine : research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9768441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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