DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Eunhee | ko |
dc.contributor.author | Min, Junhong | ko |
dc.contributor.author | Ye, Jong Chul | ko |
dc.date.accessioned | 2017-11-08T05:04:58Z | - |
dc.date.available | 2017-11-08T05:04:58Z | - |
dc.date.created | 2017-10-30 | - |
dc.date.created | 2017-10-30 | - |
dc.date.created | 2017-10-30 | - |
dc.date.issued | 2017-10 | - |
dc.identifier.citation | MEDICAL PHYSICS, v.44, no.10, pp.e360 - e375 | - |
dc.identifier.issn | 0094-2405 | - |
dc.identifier.uri | http://hdl.handle.net/10203/226827 | - |
dc.description.abstract | Purpose: Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high-quality reconstruction method from low-dose X-ray CT data has become a major research topic in the CT community. Conventional model-based de-noising approaches are, however, computationally very expensive, and image-domain de-noising approaches cannot readily remove CT-specific noise patterns. To tackle these problems, we want to develop a new low-dose X-ray CT algorithm based on a deep-learning approach. Method: We propose an algorithm which uses a deep convolutional neural network (CNN) which is applied to the wavelet transform coefficients of low-dose CT images. More specifically, using a directional wavelet transform to extract the directional component of artifacts and exploit the intra- and inter- band correlations, our deep network can effectively suppress CT-specific noise. In addition, our CNN is designed with a residual learning architecture for faster network training and better performance. Results: Experimental results confirm that the proposed algorithm effectively removes complex noise patterns from CT images derived from a reduced X-ray dose. In addition, we show that the wavelet-domain CNN is efficient when used to remove noise from low-dose CT compared to existing approaches. Our results were rigorously evaluated by several radiologists at the Mayo Clinic and won second place at the 2016 :"Low-Dose CT Grand Challenge." Conclusions: To the best of our knowledge, this work is the first deep-learning architecture for low-dose CT reconstruction which has been rigorously evaluated and proven to be effective. In addition, the proposed algorithm, in contrast to existing model-based iterative reconstruction (MBIR) methods, has considerable potential to benefit from large data sets. Therefore, we believe that the proposed algorithm opens a new direction in the area of low-dose CT research. (C) 2017 American Association of Physicists in Medicine | - |
dc.language | English | - |
dc.publisher | WILEY | - |
dc.subject | STATISTICAL IMAGE-RECONSTRUCTION | - |
dc.subject | COMPUTED-TOMOGRAPHY | - |
dc.subject | SPARSE | - |
dc.subject | DOMAIN | - |
dc.subject | REPRESENTATIONS | - |
dc.subject | REGULARIZATION | - |
dc.subject | ALGORITHM | - |
dc.title | A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction | - |
dc.type | Article | - |
dc.identifier.wosid | 000412901300003 | - |
dc.identifier.scopusid | 2-s2.0-85031306870 | - |
dc.type.rims | ART | - |
dc.citation.volume | 44 | - |
dc.citation.issue | 10 | - |
dc.citation.beginningpage | e360 | - |
dc.citation.endingpage | e375 | - |
dc.citation.publicationname | MEDICAL PHYSICS | - |
dc.identifier.doi | 10.1002/mp.12344 | - |
dc.contributor.localauthor | Ye, Jong Chul | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | low-dose x-ray CT | - |
dc.subject.keywordAuthor | wavelet transform | - |
dc.subject.keywordPlus | STATISTICAL IMAGE-RECONSTRUCTION | - |
dc.subject.keywordPlus | COMPUTED-TOMOGRAPHY | - |
dc.subject.keywordPlus | SPARSE | - |
dc.subject.keywordPlus | DOMAIN | - |
dc.subject.keywordPlus | REPRESENTATIONS | - |
dc.subject.keywordPlus | REGULARIZATION | - |
dc.subject.keywordPlus | ALGORITHM | - |
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