Effect of denoising on the quality of reconstructed images in digital breast tomosynthesis
Effect of denoising on the quality of reconstructed images in digital breast tomosynthesis
复制标题
数字乳腺断层合成中去噪对重建图像质量的影响
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Andrew D. A. Maidment
中科院分区：
文献类型：
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作者：
M. Vieira;P. Bakic;Andrew D. A. Maidment
数字乳腺断层合成摄影（DBT）中的单个投影图像必须在低辐射水平下采集，这会显著增加图像噪声。本文研究了去噪算法和Anscombe变换对DBT图像量子噪声去除的影响。Anscombe变换是一种方差稳定变换，它将依赖于信号的量子噪声转换为近似与信号无关的高斯加性噪声。因此，这种变换允许使用传统的去噪算法，设计用于加性高斯噪声，量子噪声的减少，通过在Anscombe域中的图像上工作。在这项工作中，去噪是由一个自适应维纳滤波器，以前开发的2D乳腺X射线摄影，这是应用于一组合成的DBT图像生成的3D拟人软件乳房体模。还生成了没有噪声的理想图像，以提供地面实况参考。去噪分别应用于DBT投影和重建切片。使用客观图像质量指标（如峰值信噪比（PSNR）和平均结构相似性指数（SSIM））评估图像质量的相对改善。结果表明，当使用Anscombe变换时，去噪效果更好，并且在重建前对每个投影图像进行去噪时，去噪效果更好;在这种情况下，观察到PSNR平均增加9.1 dB，SSIM测量值平均增加58.3%。当对重建图像应用去噪时，通过使用Anscombe变换没有观察到显著的改善，这表明重建算法修改了DBT图像的噪声特性。
Individual projection images in Digital Breast Tomosynthesis (DBT) must be acquired with low levels of radiation, which significantly increases image noise. This work investigates the influence of a denoising algorithm and the Anscombe transformation on the reduction of quantum noise in DBT images. The Anscombe transformation is a variance-stabilizing transformation that converts the signal-dependent quantum noise to an approximately signalindependent Gaussian additive noise. Thus, this transformation allows for the use of conventional denoising algorithms, designed for additive Gaussian noise, on the reduction of quantum noise, by working on the image in the Anscombe domain. In this work, denoising was performed by an adaptive Wiener filter, previously developed for 2D mammography, which was applied to a set of synthetic DBT images generated using a 3D anthropomorphic software breast phantom. Ideal images without noise were also generated in order to provide a ground-truth reference. Denoising was applied separately to DBT projections and to the reconstructed slices. The relative improvement in image quality was assessed using objective image quality metrics, such as peak signal-to-noise ratio (PSNR) and mean structural similarity index (SSIM). Results suggest that denoising works better for tomosynthesis when using the Anscombe transformation and when denoising was applied to each projection image before reconstruction; in this case, an average increase of 9.1 dB in PSNR and 58.3% in SSIM measurements was observed. No significant improvement was observed by using the Anscombe transformation when denoising was applied to reconstructed images, suggesting that the reconstruction algorithm modifies the noise properties of the DBT images.
影响因子：
3.8
作者：
Pokrajac, David D.;Maidment, Andrew D. A.;Bakic, Predrag R.
通讯作者：
Bakic, Predrag R.
影响因子：
3.8
作者：
Reiser, I.;Nishikawa, R. M.
通讯作者：
Nishikawa, R. M.
影响因子：
3.8
作者：
Bakic, Predrag R.;Zhang, Cuiping;Maidment, Andrew D. A.
通讯作者：
Maidment, Andrew D. A.