Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
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DOI：
10.3390/app9132615
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Hongming Shan;Ge Wang;Yang Yang-Yang
中科院分区：
文献类型：
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作者：
Hongming Shan;Ge Wang;Yang Yang-Yang
光声断层扫描（PAT）是一种新兴的非侵入性混合模式，由不断渴望卓越的成像性能驱动。然而，图像质量取决于声反射，这可能会损害诊断性能。为了解决这一挑战，我们建议将深度神经网络整合到传统的迭代算法中，以加速和改善反射伪影的校正。基于计算机断层扫描（CT）的模拟PAT数据集，这种网络加速重建方法在存在噪声的峰值信噪比（PSNR）和结构相似性（SSIM）方面优于两种最先进的迭代算法。与耗时且繁琐的传统迭代算法相比，所提出的网络也显示出相当高的计算效率。
Photo-Acoustic Tomography (PAT) is an emerging non-invasive hybrid modality driven by a constant yearning for superior imaging performance. The image quality, however, hinges on the acoustic reflection, which may compromise the diagnostic performance. To address this challenge, we propose to incorporate a deep neural network into conventional iterative algorithms to accelerate and improve the correction of reflection artifacts. Based on the simulated PAT dataset from computed tomography (CT) scans, this network-accelerated reconstruction approach is shown to outperform two state-of-the-art iterative algorithms in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) in the presence of noise. The proposed network also demonstrates considerably higher computational efficiency than conventional iterative algorithms, which are time-consuming and cumbersome.