J Korean Soc Radiol.  2022 Mar;83(2):344-359. 10.3348/jksr.2020.0152.

Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study

Affiliations
  • 1Department of Radiology, Research Institute of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea
  • 2Department of Radiology, Research Institute of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
  • 3Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
  • 4Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
  • 5Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Korea
  • 6Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
  • 7Department of Radiology, Ajou University Hospital, Ajou University School of Medicine, Suwon, Korea
  • 8Department of Radiology, Catholic Kwangdong University International St. Mary’s Hospital, Catholic Kwandong University, Incheon, Korea

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.

Keyword

Deep Learning; Artificial Intelligence; Radiation; Mammography; Breast Neoplasm
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