An adaptive multiscale anisotropic diffusion regularized image reconstruction method for digital breast tomosynthesis
An adaptive multiscale anisotropic diffusion regularized image reconstruction method for digital breast tomosynthesis
复制标题
数字乳腺断层合成的自适应多尺度各向异性扩散正则化图像重建方法
DOI：
10.1007/s13246-018-0700-5
复制
发表时间：
2018-12-01
影响因子：
--
通讯作者：
Gao，Xin
中科院分区：
文献类型：
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作者：
Liu，Yangchuan;Zhang，Cishen;Gao，Xin
数字乳腺断层合成摄影（DBT）作为断层摄影的一种特殊情况，可以从少视角、有限角度的投影数据中实现准三维图像重建，用于乳腺病变的检测。对于DBT图像重建，需要迭代算法来抑制由于欠采样引起的伪影，并且需要自适应正则化来保留肿块和钙化的边缘。提出了一种新的基于多尺度Tikhonov全变分（MTTV）的凸集正则化投影（POCS）重建方法。被称为自适应多尺度各向异性扩散的正则化能够在相当大的程度上保留边缘，并选择性地抑制噪声而不引入伪影。所提出的方法被称为MTTV-POCS，并使用3D数字乳腺和Shepp-Logan体模以及从先进的DBT机器获取的两个临床体积图像进行定量评估。实验结果表明，该方法在峰值信噪比（PSNR）和结构相似性指数（SSIM）指标上优于自适应最陡下降POCS（ASD-POCS）和选择性扩散正则化同时代数重建技术（SD-SART）.结果表明，该方法适用于DBT的高质量图像重建。
As a special case of tomography, digital breast tomosynthesis (DBT) can realize quasi-3D image reconstruction for breast lesion detection from few-view and limited-angle projection data. For DBT image reconstruction, iterative algorithms are needed to suppress artifacts due to undersampling, and adaptive regularizations are necessary for preserving the edges of masses and calcifications. This paper presents a novel reconstruction method by regularizing projection onto convex sets (POCS) with multiscale Tikhonov-total variation (MTTV). The regularization, known as adaptive multiscale anisotropic diffusion, is able to preserve edges to a considerable extent and selectively suppress noise without introducing artifacts. The proposed method is referred to as MTTV–POCS and is evaluated quantitatively using 3D numerical breast and Shepp-Logan phantoms as well as two clinical volume images acquired from an advanced DBT machine. Experimental results show that the proposed method has better performance in metrics of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) over two existing methods: adaptive-steepest-descent-POCS (ASD-POCS) and selective-diffusion regularized simultaneous algebraic reconstruction technique (SD-SART). As indicated by the results, the proposed method is applicable to DBT for high-quality image reconstruction.