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首页> 外文会议>Optics in health care and biomedical optics VI >Novel Optimization Method for Multi-dimensional Breast Photoacoustic Tomography
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Novel Optimization Method for Multi-dimensional Breast Photoacoustic Tomography

机译：多维乳房光声层析成像的新优化方法

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摘要

Photoacoustic tomography (PAT) is an effective optical biomedical imaging method which is characterized with nonionizing and noninvasive, presenting good soft tissue contrast with excellent spatial resolution. To build a multidimensional breast PAT image, more ultrasound sensors are needed, which brings difficulties to data acquisition. The time complexity for multi-dimensional breast PAT image reconstruction also rises tremendously. Compressive sensing (CS) theory breaks the restriction of Nyquist sampling theorem and is capable to rebuild signals with fewer measurements. In this contribution, we propose an effective optimization method for multi-dimensional breast PAT, which combines the theory of CS and an unevenly, adaptively distributing data acquisition algorithm. With this method, the quality of our reconstructed breast PAT images are better than those using existing multi-dimensional breast PAT system. To build breast PAT images with the same quality, the required number of ultrasound transducers is decreased by using our proposed method. We have verified our method on simulation data and achieved expected results in both two dimensional and three dimensional PAT image reconstruction. In the future, our method can be applied to various aspects of biomedical PAT imaging such as early stage tumor detection and in vivo imaging and monitoring.
机译：光声层析成像（PAT）是一种有效的光学生物医学成像方法，其特征在于非电离和非侵入性，表现出良好的软组织对比度和出色的空间分辨率。为了建立多维的乳房PAT图像，需要更多的超声传感器，这给数据采集带来了困难。多维乳房PAT图像重建的时间复杂度也大大提高。压缩感测（CS）理论打破了Nyquist采样定理的限制，并能够以更少的测量量重建信号。在这项贡献中，我们提出了一种有效的多维乳房PAT的优化方法，该方法结合了CS理论和不均匀分布的自适应数据采集算法。通过这种方法，我们重建的乳房PAT图像的质量优于使用现有多维乳房PAT系统的图像。为了建立具有相同质量的乳房PAT图像，使用我们提出的方法可以减少所需的超声换能器数量。我们已经在模拟数据上验证了我们的方法，并且在二维和三维PAT图像重建中均取得了预期的结果。将来，我们的方法可以应用于生物医学PAT成像的各个方面，例如早期肿瘤检测以及体内成像和监测。

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