Breast cancer is the most frequently diagnosed cancer in women worldwide. Digital breast tomosynthesis (DBT), which is based on limited-angle tomography, was developed to solve tissue overlapping problems associated with traditional breast mammography. However, due to the problems associated with tube movement during the process of data acquisition, stationary DBT (s-DBT) was developed to allow the X-ray source array to stay stationary during the DBT scanning process. In this work, we evaluate four widely used and investigated DBT image reconstruction algorithms, including the commercial Feldkamp-Davis-Kress algorithm (FBP), the simultaneous iterative reconstruction technique (SIRT), the simultaneous algebraic reconstruction technique (SART) and the total variation regularized SART (SART-TV) for an s-DBT imaging system that we set up in our own laboratory for studies using a semi-elliptical digital phantom and a rubber breast phantom to determine the most superior algorithm for s-DBT image reconstruction among the four algorithms. Several quantitative indexes for image quality assessment, including the peak signal-noise ratio (PSNR), the root mean square error (RMSE) and the structural similarity (SSIM), are used to determine the best algorithm for the imaging system that we set up. Image resolutions are measured via the calculation of the contrast-to-noise ratio (CNR) and artefact spread function (ASF). The experimental results show that the SART-TV algorithm gives reconstructed images with the highest PSNR and SSIM values and the lowest RMSE values in terms of image accuracy and similarity, along with the highest CNR values calculated for the selected features and the best ASF curves in terms of image resolution in the horizontal and vertical directions. Thus, the SART-TV algorithm is proven to be the best algorithm for use in s-DBT image reconstruction for the specific imaging task in our study.

中文翻译：

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使用碳纳米管 X 射线源阵列对固定式数字乳房断层合成系统的重建算法进行评估。

乳腺癌是全世界女性中最常被诊断出的癌症。数字乳房断层合成 (DBT) 基于有限角度断层扫描，旨在解决与传统乳房 X 光检查相关的组织重叠问题。然而，由于在数据采集过程中与管运动相关的问题，开发了固定 DBT（s-DBT）以允许 X 射线源阵列在 DBT 扫描过程中保持静止。在这项工作中，我们评估了四种广泛使用和研究的 DBT 图像重建算法，包括商业 Feldkamp-Davis-Kress 算法 (FBP)、同时迭代重建技术 (SIRT)、我们在自己的实验室中建立的 s-DBT 成像系统的同时代数重建技术 (SART) 和总变分正则化 SART (SART-TV) 用于研究，使用半椭圆数字模型和橡胶乳房模型来确定四种算法中s-DBT图像重建最优秀的算法。图像质量评估的几个定量指标，包括峰值信噪比 (PSNR)、均方根误差 (RMSE) 和结构相似性 (SSIM)，用于确定我们建立的成像系统的最佳算法. 图像分辨率是通过计算对比度噪声比 (CNR) 和伪影扩散函数 (ASF) 来测量的。实验结果表明，SART-TV 算法在图像精度和相似度方面给出了具有最高 PSNR 和 SSIM 值和最低 RMSE 值的重建图像，以及为所选特征计算的最高 CNR 值和最佳 ASF 曲线。水平和垂直方向的图像分辨率。因此，SART-TV 算法被证明是用于我们研究中特定成像任务的 s-DBT 图像重建的最佳算法。