Using Virtual Digital Breast Tomosynthesis for De-Noising of Low-Dose Projection Images
Using Virtual Digital Breast Tomosynthesis for De-Noising of Low-Dose Projection Images
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DOI：
10.1109/isbi.2019.8759408
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Pranjal Sahu;Hailiang Huang;Wei Zhao;Hong Qin
中科院分区：
文献类型：
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作者：
Pranjal Sahu;Hailiang Huang;Wei Zhao;Hong Qin
数字乳腺断层合成摄影（DBT）提供了乳房体积的准3D印象，从而更好地可视化肿块。然而，断层合成的一个严重缺点是，与乳房X射线摄影术相比，每个投影得到较低的X射线剂量，导致较高的量子噪声，这严重妨碍了钙化的可见性。为了解决这个问题，我们提出了一个基于对抗损失的卷积神经网络模型。我们使用从虚拟临床试验中获得的合成数据来训练深度网络。不像早期的作品，测试模型的幻影，我们进行了实验，在临床环境中获得的真实的样本以及。我们的方法显示出令人鼓舞的结果去噪的预测。去噪后的投影显示出与乳房X线照片更高的感知相似性和上级信噪比。重建体积还增强了钙化可见性。我们的工作显示了利用合成数据训练深度网络以实现去噪目的的可行性。
Digital Breast Tomosynthesis (DBT) provides a quasi-3D impression of the breast volume resulting in a better visualization of mass. However, one serious drawback of Tomosynthesis is that compared to Mammography, each projection gets lower x-ray dose resulting into higher quantum noise which seriously hampers the visibility of calcifications. To solve this problem we propose a Convolutional Neural Network model based on Adversarial loss. We train the deep network using synthetic data obtained from Virtual Clinical Trials. Unlike earlier works which tested model on phantoms only, we performed experiments on real samples obtained in clinical settings as well. Our approach shows encouraging results in de-noising the projections. De-noised projections show higher perceptual similarity with mammograms and superior signal-to-noise ratio. The reconstructed volume also enhances calcification visibility. Our work shows the viability of utilizing synthetic data for training the deep network for de-noising purposes.