Deep Convolutional Neural Network With Adversarial Training for Denoising Digital Breast Tomosynthesis Images.
Deep Convolutional Neural Network With Adversarial Training for Denoising Digital Breast Tomosynthesis Images.
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DOI：
10.1109/tmi.2021.3066896
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发表时间：
2021-07
影响因子：
10.6
通讯作者：
Chan HP
中科院分区：
文献类型：
--
作者：
Gao M;Fessler JA;Chan HP
数字乳腺断层合成摄影（DBT）是一种准三维成像模式，可减少传统二维（2D）乳腺X射线摄影中重叠乳腺组织导致的肿块病变检测中的假阴性和假阳性。DBT扫描的患者剂量类似于单个2D乳房X线照片的剂量，而每个投影视图的采集增加了探测器读出噪声。噪声传播到重建的DBT体积，可能会模糊乳腺癌的细微体征，如微钙化（MC）。这项研究开发了一种深度卷积神经网络（DCNN）框架，用于对DBT图像进行去噪，重点是提高MC的显著性，并保留毛刺肿块和正常组织纹理的边界不清。我们使用均方误差（MSE）损失和对抗性损失的加权组合来训练DCNN。我们配置了一个专用的X射线成像模拟器与数字乳房模型相结合，以生成逼真的计算机DBT数据进行训练。我们比较了使用数字幻影和使用真实的物理幻影之间的DCNN训练。所提出的去噪方法提高了验证体模DBT中模拟MC的对比噪声比（CNR）和可检测性指数（d '）。这些性能指标随着训练靶剂量和训练样本量的增加而改善。有前途的去噪结果，观察到的数字幻影训练的去噪DBT重建与不同的技术和一个小的独立的测试集的人类主题DBT图像的可转移性。
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.