Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study [딥러닝 알고리즘을 이용한 저선량 디지털 유방 촬영 영상의 복원: 예비 연구]
DC Field | Value | Language |
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dc.contributor.author | Ha, S.M. | - |
dc.contributor.author | Kim, H.H. | - |
dc.contributor.author | Kang, E. | - |
dc.contributor.author | Seo, B.K. | - |
dc.contributor.author | Choi, N. | - |
dc.contributor.author | Kim, T.H. | - |
dc.contributor.author | Ku, Y.J. | - |
dc.contributor.author | Ye, J.C. | - |
dc.date.accessioned | 2023-03-08T10:46:55Z | - |
dc.date.available | 2023-03-08T10:46:55Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.issn | 1738-2637 | - |
dc.identifier.issn | 2288-2928 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/62317 | - |
dc.description.abstract | Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution (p < 0.001); diagnostic quality for calcifications (p < 0.001); and for masses, asymmetry, or architectural distortion (p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality (p = 0.547), lesion visibility (p = 0.120), and contrast (p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction. © 2021 The Korean Society of Radiology | - |
dc.format.extent | 16 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Korean Radiological Society | - |
dc.title | Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study [딥러닝 알고리즘을 이용한 저선량 디지털 유방 촬영 영상의 복원: 예비 연구] | - |
dc.title.alternative | Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study | - |
dc.type | Article | - |
dc.identifier.doi | 10.3348/jksr.2020.0152 | - |
dc.identifier.bibliographicCitation | Journal of the Korean Society of Radiology, v.83, no.2, pp 344 - 359 | - |
dc.identifier.kciid | ART002826767 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85124833831 | - |
dc.citation.endPage | 359 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 344 | - |
dc.citation.title | Journal of the Korean Society of Radiology | - |
dc.citation.volume | 83 | - |
dc.type.docType | Article | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Artificial Intelligence | - |
dc.subject.keywordAuthor | Breast Neoplasm | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Mammography | - |
dc.subject.keywordAuthor | Radiation | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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