利用压缩重采样和瞬时信噪比的医学超声斑点减少

R. Mammone, L. Barinov, A. Jairaj, W. Hulbert, C. Podilchuk
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引用次数: 4

摘要

医学超声是一种有价值的医学诊断和指导介入手术的成像技术。然而,由于亚分辨率散射体的存在,超声成像受到散斑噪声的影响,这是所有相干成像技术的固有特征。与x射线或核磁共振成像相比,散斑噪声会降低对比度分辨率,这是导致超声波整体有效分辨率较低的原因。在乳房成像的情况下,超声斑点可以掩盖小细节,如低对比肿瘤或微钙化,这可能是乳腺癌的早期迹象。这一限制阻止了超声波取代乳房x光检查成为乳腺癌筛查的金标准。传统的斑点减少技术试图在保留边缘和其他重要特征的同时去除斑点噪声,但是在去除斑点噪声和模糊组织结构和细节之间总是存在权衡。我们介绍了一种新的斑点减少和对比度增强的超声成像方法,这是由压缩采样背后的基本思想驱动的。我们还介绍了一种估计瞬时信噪比的方法,以便从主要是噪声的区域中识别出主要是信号的区域,以便在抑制噪声的同时保留信号。我们已经在实验室中展示了在12dB量级的信噪比的改善,并改善了临床数据的可视化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speckle reduction of medical ultrasound using Compressive Re-Sampling and instantaneous SNR
Medical Ultrasonography is a valuable imaging technology for medical diagnostics and to guide interventional procedures. However, ultrasound imaging suffers from speckle noise, an inherent characteristic of all coherent imaging techniques due to the presence of sub-resolution scatterers. Speckle noise produces a reduction in contrast resolution which is responsible for the overall lower effective resolution of ultrasound compared to x-ray or MRI imaging. In the case of breast imaging, ultrasound speckle can mask small details such as low contrast tumors or microcalcifications, which may be an early indication of breast cancer. This limitation prevents ultrasound from displacing mammography as the gold standard for breast cancer screening. Traditional speckle reduction techniques attempt to remove speckle noise while preserving edges and other important features but there is always a tradeoff between removing the speckle noise and blurring tissue structure and details. We introduce a novel speckle reduction and contrast enhancement method for ultrasound imaging that is motivated by the fundamental ideas behind compressive sampling. We also introduce a way to estimate instantaneous SNR in order to identify the areas that are mostly signal from the areas that are mostly noise in order to preserve the signal while suppressing the noise. We have shown improvements in SNR on the order of 12dB in the lab and improved visualization of clinical data.
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