Department of Biomedical Engineering, University at Buffalo North Campus, Buffalo, NY 14260, USA.
Department of Computer Science and Engineering, University at Buffalo North Campus, Buffalo, NY 14260, USA.
Sensors (Basel). 2022 Oct 12;22(20):7725. doi: 10.3390/s22207725.
Linear-array-based photoacoustic computed tomography (PACT) has been widely used in vascular imaging due to its low cost and high compatibility with current ultrasound systems. However, linear-array transducers have inherent limitations for three-dimensional imaging due to the poor elevation resolution. In this study, we introduced a deep learning-assisted data process algorithm to enhance the image quality in linear-array-based PACT. Compared to our earlier study where training was performed on 2D reconstructed data, here, we utilized 2D and 3D reconstructed data to train the two networks separately. We then fused the image data from both 2D and 3D training to get features from both algorithms. The numerical and in vivo validations indicate that our approach can improve elevation resolution, recover the true size of the object, and enhance deep vessels. Our deep learning-assisted approach can be applied to translational imaging applications that require detailed visualization of vascular features.
基于线阵的光声计算机断层扫描（PACT）由于其成本低且与当前超声系统高度兼容，已广泛应用于血管成像。然而，由于在垂直方向上的分辨率较差，线阵换能器在三维成像方面存在固有局限性。在这项研究中，我们引入了一种深度学习辅助的数据处理算法，以提高基于线阵的 PACT 的图像质量。与我们之前在二维重建数据上进行训练的研究相比，在这里，我们分别使用二维和三维重建数据来训练两个网络。然后，我们融合了来自二维和三维训练的数据，以从两种算法中获取特征。数值和体内验证表明，我们的方法可以提高垂直分辨率，恢复物体的真实尺寸，并增强深层血管。我们的深度学习辅助方法可以应用于需要详细可视化血管特征的转化成像应用。
