Breast cancer (BC) is the most commonly diagnosed non‐skin cancer in women. To achieve early and accurate diagnosis, three‐dimensional (3D) cone‐beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) modalities are used. Importantly, the comparison of reconstruction accuracy of both CBBCT and DBT has rarely been investigated, thus constituting a research gap. This study systematically investigated the performances of the CBBCT and DBT for different reconstruction algorithms using both BR3D breast phantom and breast images. We acquired clinical breast images and scanned the BR3D phantom for additional breast images. These acquired images were used to simulate projection data using predefined CBBCT and DBT geometries. The simulated projections were reconstructed using five different reconstruction algorithms. To evaluate the reconstruction accuracy, we calculated average image quality assessment (IQA) indices, including peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and others, across different algorithms and modalities. The pooled PSNR, SSIM, and RMSE for DBT and CBBCT images are (31.6265 ± 0.8725), (0.9353 ± 0.0077), and (0.0270 ± 0.0025) and (29.7007 ± 0.9249), (0.9136 ± 0.0130), and (0.0342 ± 0.0040), which implies that the overall IQA indices of DBT are superior to CBBCT; therefore, DBT tends to reveal more BC detectability as the diagnosis outcome would largely depend on good quality images. The results show that DBT gives an improved result for all algorithms compared to CBBCT, although further experimental trials may be needed to establish the findings fully. The findings suggest that using DBT may enhance the accuracy of BC diagnosis compared to CBBCT due to its superior image quality in clinical practice, emphasizing the importance of selecting optimal reconstruction algorithms for improved diagnostic outcomes.

中文翻译：

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锥形束乳腺计算机断层扫描与数字乳腺断层合成诊断乳腺癌的比较分析：重建算法的综合研究

乳腺癌（BC）是女性中最常诊断的非皮肤癌。为了实现早期准确的诊断，使用三维（3D）锥形束乳腺计算机断层扫描（CBBCT）和数字乳腺断层合成（DBT）模式。重要的是，CBBCT 和 DBT 重建精度的比较很少被研究，因此构成了研究空白。本研究使用 BR3D 乳房模型和乳房图像系统地研究了 CBBCT 和 DBT 对于不同重建算法的性能。我们获取了临床乳房图像并扫描 BR3D 体模以获取其他乳房图像。这些采集的图像用于使用预定义的 CBBCT 和 DBT 几何形状来模拟投影数据。使用五种不同的重建算法重建模拟投影。为了评估重建精度，我们计算了不同算法的平均图像质量评估（IQA）指数，包括峰值信噪比（PSNR）、结构相似性指数（SSIM）、均方根误差（RMSE）等和方式。 DBT 和 CBBCT 图像的合并 PSNR、SSIM 和 RMSE 为 (31.6265 ± 0.8725)、(0.9353 ± 0.0077) 和 (0.0270 ± 0.0025) 和 (29.7007 ± 0.9249)、(0.9136 ± 0.0130) 和 (0.0342 ± 0.004) 0 ），这意味着DBT的整体IQA指数优于CBBCT；因此，DBT 往往会揭示更多的 BC 可检测性，因为诊断结果在很大程度上取决于高质量的图像。结果表明，与 CBBCT 相比，DBT 为所有算法提供了改进的结果，尽管可能需要进一步的实验试验来充分证实结果。研究结果表明，由于 DBT 在临床实践中具有卓越的图像质量，与 CBBCT 相比，使用 DBT 可以提高 BC 诊断的准确性，强调选择最佳重建算法以改善诊断结果的重要性。