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Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study
- Source :
- 대한영상의학회지, Vol 83, Iss 2, Pp 344-359 (2022)
- Publication Year :
- 2022
- Publisher :
- The Korean Society of Radiology, 2022.
-
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.
Details
- Language :
- English, Korean
- ISSN :
- 22882928
- Volume :
- 83
- Issue :
- 2
- Database :
- Directory of Open Access Journals
- Journal :
- 대한영상의학회지
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.75c0c3738dcd4c73806e8f620265ac74
- Document Type :
- article
- Full Text :
- https://doi.org/10.3348/jksr.2020.0152