Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study
- Author:
Su Min HA
1
;
Hak Hee KIM
;
Eunhee KANG
;
Bo Kyoung SEO
;
Nami CHOI
;
Tae Hee KIM
;
You Jin KU
;
Jong Chul YE
Author Information
- Publication Type:Original Article
- From:Journal of the Korean Radiological Society 2022;83(2):344-359
- CountryRepublic of Korea
- Language:English
-
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.
