| 卢孟阳,李博艺,朱志斌,刘成成,刘欣,他得安.基于无监督学习的高分辨光声重建[J].声学技术,2022,41(3):369~375 |
| 基于无监督学习的高分辨光声重建 |
| High resolution optoacoustic reconstruction based on unsupervised learning |
| 投稿时间：2022-05-20 修订日期：2022-06-18 |
| DOI：10.16300/j.cnki.1000-3630.2022.03.009 |
| 中文关键词: 光声断层成像 无监督学习 点扩散函数 高分辨重建 |
| 英文关键词: optoacoustic tomography (OAT) unsupervised learning point spread function (PSF) high-resolution reconstruction |
| 基金项目:国家自然科学基金(12034005、61871263)。 |
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| 摘要点击次数: 538 |
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| 中文摘要: |
| 光声断层成像(Optoacoustic Tomography,OAT)是一种新兴的生物医学成像技术，在基础医学研究与临床实践中具有重要作用。针对现有光声断层成像空间分辨率较低的问题，提出了一种结合物理点扩散函数(Point Spread Function,PSF)模型和卷积神经网络(Convolutional Neural Network,CNN)的新型高分辨光声重建网络方法(Physical Attention U-Net,Phys-AU-Net)。该方法采用无监督学习策略，结合物理PSF模型和基于注意力机制的U-Net网络。其中，物理PSF模型用于完成对衍射受限机制的模拟，基于注意力机制的U-Net网络用于实现对高密度重叠吸收体图像的特征提取。在二者共同作用下，Phys-AU-Net突破了声衍射极限对于OAT成像空间分辨率的限制。实验结果表明，Phys-AU-Net能够有效实现对声衍射受限光声断层图像的高分辨重建，其性能相较于U-Net网络具有较大程度提升，在结构相似性指标(Structural Similarity,SSIM)方面提升了43.5%，在峰值信噪比(Peak Signal to Noise Ratio,PSNR)方面提升了11.2%，对临床研究及诊断具有重要意义。 |
| 英文摘要: |
| Optoacoustic tomography (OAT) is a new biomedical imaging technology, which plays an important role in medical research and clinical practice. Considering the problems of low resolution in optoacoustic tomography, a new high-resolution reconstruction network (Phys-AU-Net) combining physical point spread function (PSF) and convolutional neural network (CNN) is proposed in this paper. Briefly, the proposed method adopts an unsupervised learning strategy, and combines a point spread function (PSF) model and the U-Net based on the attention mechanism. Among these, the PSF model is used to simulate the diffraction limited mechanism, and the U-Net network based on attention mechanism is used to complete the feature extraction of high-density images. The experimental results show that compared with U-Net, the Phys-AU-Net improves the structural similarity (SSIM) by 43.5% and the peak signal to noise ratio (PSNR) by 11.2%, which provides a great potential in clinical research and diagnosis. |
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