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KMID : 1103720220830020344
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Journal of the Korean Society of Radiology
2022 Volume.83 No. 2 p.344 ~ p.359
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Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study
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Ha Su-Min
Kim Hak-Hee
Kang Eun-Hee
Seo Bo-Kyoung
Choi Na-Mi
Kim Tae-Hee
Ku You-Jin
Ye Jong-Chul
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Abstract
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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.
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KEYWORD
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Deep Learning, Artificial Intelligence, Radiation, Mammography, Breast Neoplasm
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