ArticleIEEE transactions on medical imaging2021
Deep Convolutional Neural Network With Adversarial Training for Denoising Digital Breast Tomosynthesis Images.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 40 citations in OpenAlex.
- Assessment of an unsupervised denoising approach based on Noise2Void in digital mammography.Scientific reports · 2025Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Clinical Application of Artificial Intelligence in Digital Breast Tomosynthesis.Journal of the Korean Society of Radiology · 2025Review
- The analysis of art design under improved convolutional neural network based on the Internet of Things technology.Scientific reports · 2024Article
- Pre-Reconstruction Processing with the Cycle-Consist Generative Adversarial Network Combined with Attention Gate to Improve Image Quality in Digital Breast Tomosynthesis.Diagnostics (Basel, Switzerland) · 2024Article
- X-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis.Physics in medicine and biology · 2024Article
- Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach.Physics in medicine and biology · 2023Article
- Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images.Medical physics · 2023Article
- Task-based assessment of digital mammography microcalcification detection with deep learning denoising algorithmss usingJournal of medical imaging (Bellingham, Wash.) · 2023Article
- Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography.Artificial intelligence in medicine · 2023Article
- Denoising digital breast tomosynthesis projections using deep learning with synthetic data as training set.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Image Augmentation Techniques for Mammogram Analysis.Journal of imaging · 2022Review
- Evaluation of a Generative Adversarial Network to Improve Image Quality and Reduce Radiation-Dose during Digital Breast Tomosynthesis.Diagnostics (Basel, Switzerland) · 2022Article
- Usefulness of a Metal Artifact Reduction Algorithm in Digital Tomosynthesis Using a Combination of Hybrid Generative Adversarial Networks.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Digital breast tomosynthesis (DBT) is a quasi-three-dimensional imaging modality that can reduce false negatives and false positives in mass lesion detection caused by overlapping breast tissue in conventional two-dimensional (2D) mammography. The patient dose of a DBT scan is similar to that of a single 2D mammogram, while acquisition of each projection view adds detector readout noise. The noise is propagated to the reconstructed DBT volume, possibly obscuring subtle signs of breast cancer such as microcalcifications (MCs). This study developed a deep convolutional neural network (DCNN) framework for denoising DBT images with a focus on improving the conspicuity of MCs as well as preserving the ill-defined margins of spiculated masses and normal tissue textures. We trained the DCNN using a weighted combination of mean squared error (MSE) loss and adversarial loss. We configured a dedicated x-ray imaging simulator in combination with digital breast phantoms to generate realistic in silico DBT data for training. We compared the DCNN training between using digital phantoms and using real physical phantoms. The proposed denoising method improved the contrast-to-noise ratio (CNR) and detectability index (d') of the simulated MCs in the validation phantom DBTs. These performance measures improved with increasing training target dose and training sample size. Promising denoising results were observed on the transferability of the digital-phantom-trained denoiser to DBT reconstructed with different techniques and on a small independent test set of human subject DBT images.
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