标题： 用于超声断层扫描高质量图像重建的确定性压缩采样 显示英文标题

标题： Deterministic Compressive Sampling for High-Quality Image Reconstruction of Ultrasound Tomography

摘要： 一种众所周知的诊断成像方式，称为超声断层扫描，被迅速开发用于检测尺寸小于入射压力波波长的非常小的肿瘤，且无需电离辐射，与当前金标准X射线乳腺摄影相比。 基于反散射技术，超声断层扫描使用一些材料特性，如声学对比度或衰减来检测小目标。 基于一阶玻恩近似的扭曲玻恩迭代方法（DBIM）是一种高效的衍射断层扫描方法。 压缩感知（CS）技术被应用于超声断层扫描的检测几何配置中，作为提高图像重建质量的强大工具。 然而，这种配置在实践中很难实现。 受确定性CS更容易硬件实现的启发，在本文中，我们提出了检测几何配置中的混沌测量，并使用L1正则化实现图像重建过程。 所提出方法的仿真结果证明了所提出方法的高性能，与传统方法相比，归一化误差大约减少了90%。 此外，在相同质量下，使用所提出的方法可以将测量次数减少一半，并且只需两次迭代。 显示英文摘要

摘要： A well-known diagnostic imaging modality, termed ultrasound tomography, was quickly developed for the detection of very small tumors whose sizes are smaller than the wavelength of the incident pressure wave without ionizing radiation, compared to the current gold-standard X-ray mammography. Based on inverse scattering technique, ultrasound tomography uses some material properties such as sound contrast or attenuation to detect small targets. The Distorted Born Iterative Method (DBIM) based on first-order Born approximation is an efficient diffraction tomography approach. Compressed Sensing (CS) technique was applied to the detection geometry configuration of ultrasound tomography as a powerful tool for improved image reconstruction quality. However, this configuration is very difficult to implement in practice. Inspired of easier hardware implementation of deterministic CS, in this paper, we propose the chaos measurements in the detection geometry configuration and the image reconstruction process is implemented using L1 regularization. The simulation results of the proposed method have demonstrated the high performance of the proposed approach, the normalized error is approximately 90% reduced, compared to the conventional approach. Furthermore, with the same quality, we can save half of number of measurements and only use two iterations when using the proposed method.