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基于主成分分析的高速、稀疏采样三维光声计算机断层成像术在体研究。

High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis.

机构信息

Qufu Normal University, School of Information Science and Engineering & Institute of Network Computing, 80 Yantai Road North, Rizhao 276826, China.

Washington University in St. Louis, Department of Biomedical Engineering, Optical Imaging Laboratory, One Brookings Drive, St. Louis, Missouri 63130, United States.

出版信息

J Biomed Opt. 2016 Jul 1;21(7):76007. doi: 10.1117/1.JBO.21.7.076007.

Abstract

Photoacoustic computed tomography (PACT) has emerged as a unique and promising technology for multiscale biomedical imaging. To fully realize its potential for various preclinical and clinical applications, development of systems with high imaging speed, reasonable cost, and manageable data flow are needed. Sparse-sampling PACT with advanced reconstruction algorithms, such as compressed-sensing reconstruction, has shown potential as a solution to this challenge. However, most such algorithms require iterative reconstruction and thus intense computation, which may lead to excessively long image reconstruction times. Here, we developed a principal component analysis (PCA)-based PACT (PCA-PACT) that can rapidly reconstruct high-quality, three-dimensional (3-D) PACT images with sparsely sampled data without requiring an iterative process. In vivo images of the vasculature of a human hand were obtained, thus validating the PCA-PACT method. The results showed that, compared with the back-projection (BP) method, PCA-PACT required ∼50% fewer measurements and ∼40% less time for image reconstruction, and the imaging quality was almost the same as that for BP with full sampling. In addition, compared with compressed sensing-based PACT, PCA-PACT had approximately sevenfold faster imaging speed with higher imaging accuracy. This work suggests a promising approach for low-cost, 3-D, rapid PACT for various biomedical applications.

摘要

光声计算机断层扫描(PACT)作为一种用于多尺度生物医学成像的独特而有前途的技术已经出现。为了充分实现其在各种临床前和临床应用中的潜力,需要开发具有高速成像、合理成本和可管理数据流的系统。使用先进的重建算法(如压缩感知重建)进行稀疏采样的 PACT 已经显示出解决这一挑战的潜力。然而,大多数此类算法需要迭代重建,因此需要大量计算,这可能导致图像重建时间过长。在这里,我们开发了一种基于主成分分析(PCA)的 PACT(PCA-PACT),它可以使用稀疏采样数据快速重建高质量的三维(3-D)PACT 图像,而无需迭代过程。我们获得了人体手部血管的体内图像,从而验证了 PCA-PACT 方法。结果表明,与反向投影(BP)方法相比,PCA-PACT 所需的测量次数减少了约 50%,图像重建时间减少了约 40%,成像质量与完全采样的 BP 几乎相同。此外,与基于压缩感知的 PACT 相比,PCA-PACT 的成像速度快约 7 倍,成像精度更高。这项工作为各种生物医学应用提供了一种有前途的低成本、3-D、快速 PACT 方法。

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