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]open accessRadiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study
- Authors
- Ha, S.M.; Kim, H.H.; Kang, E.; Seo, B.K.; Choi, N.; Kim, T.H.; Ku, Y.J.; Ye, J.C.
- Issue Date
- Jul-2021
- Publisher
- Korean Radiological Society
- Keywords
- Artificial Intelligence; Breast Neoplasm; Deep Learning; Mammography; Radiation
- Citation
- Journal of the Korean Society of Radiology, v.83, no.2, pp 344 - 359
- Pages
- 16
- Journal Title
- Journal of the Korean Society of Radiology
- Volume
- 83
- Number
- 2
- Start Page
- 344
- End Page
- 359
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/62317
- DOI
- 10.3348/jksr.2020.0152
- ISSN
- 1738-2637
2288-2928
- 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
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