A Machine Learning Alternative to Beamforming to Improve Ultrasound Image Quality for Interventional Access to the Kidney

项目摘要 尽管当今超声成像在医院中广泛流行，但是超声的临床实用性仍然不高。 制导受到杂乱和混响伪像的严重阻碍，这些伪像模糊了感兴趣的结构， 解剖学测量。杂乱在超重和肥胖的个体中尤其成问题， 在北美，有7860万成年人和1280万儿童。同样，介入手术- 十个需要插入一个或多个金属工具，这会产生混淆乐器混响伪像 位置、方向和几何形状，同时模糊附近的组织，因此额外地阻碍了超声成像。 年龄质量尽管伪影是有问题的，但超声继续存在主要是因为它的最大作用 优势（即，移动性、成本、非电离辐射、实时可视化和多平面视图）进行比较 现有的图像引导选项，但如果没有有问题的伪影，它会更有用。 我们的长期项目目标是使用最先进的机器学习技术来提供干预性的 为放射科医生提供无伪影的超声图像。我们将首先开发一个新的框架替代方案， 涉及超声波束形成过程，该过程去除针尖混响和声学杂波， 当将针引导至肾脏以移除疼痛的肾脏时，近场组织中的多径散射 石头我们的第一个目标是测试卷积神经网络（CNN），它输入原始通道数据并输出 人类可读的图像，没有由多径散射和混响引起的伪像。次要目标 CNN的核心是学习创建这些新的基于CNN的图像所需的最少数量的参数。 我们的第二个目标是用来自实验体模和离体的超声数据来验证训练的算法 组织.我们的第三个目标是将我们的评价扩展到活体猪肾的超声图像。这项工作是 首先，建议绕过整个波束成形过程，用机器学习和计算机代替它。 视觉技术，以消除传统上有问题的噪声伪影，并创建一个全新的类型， 无伪影、高对比度、高分辨率、基于超声的图像，用于指导介入手术。 这项工作结合了成像科学家，计算机科学家和介入性RA的专业知识， 生物学家探索一个未开发的，研究不足的领域，只是最近才通过改进可行 在计算能力方面，计算机视觉能力的进步，以及关于主要来源的新知识， 图像退化。通过我们与该部门的临床合作， 约翰霍普金斯医院的放射科主任在NIH开拓者奖的支持下，我们的团队将 第一个开发这些工具和功能，以消除介入超声中的噪声伪影， 打开了超声成像新范式的大门，这将直接贝内数百万患有 更清晰、更易于解读的超声图像。后续的R 01资金将定制我们的创新，以增加- 常规应用特定的超声程序（例如，乳腺活检、癌症检测、自主手术）。

Project Summary Despite the widespread prevalence of ultrasound imaging in hospitals today, the clinical utility of ultrasound guidance is severely hampered by clutter and reverberation artifacts that obscure structures of interest and com- plicate anatomical measurements. Clutter is particularly problematic in overweight and obese individuals, who account for 78.6 million adults and 12.8 million children in North America. Similarly, interventional procedures of- ten require insertion of one or more metal tools, which generate reverberation artifacts that obfuscate instrument location, orientation, and geometry, while obscuring nearby tissues, thus additionally hampering ultrasound im- age quality. Although artifacts are problematic, ultrasound continues to persist primarily because of its greatest strengths (i.e., mobility, cost, non-ionizing radiation, real-time visualization, and multiplanar views) in comparison to existing image-guidance options, but it would be significantly more useful without problematic artifacts. Our long-term project goal is to use state-of-the-art machine learning techniques to provide interventional radiologists with artifact-free ultrasound-based images. We will initially develop a new framework alternative to the ultrasound beamforming process that removes needle tip reverberations and acoustic clutter caused by multipath scattering in near-field tissues when guiding needles to the kidney to enable removal of painful kidney stones. Our first aim will test convolutional neural networks (CNNs) that input raw channel data and output human readable images with no artifacts caused by multipath scattering and reverberations. A secondary goal of the CNNs is to learn the minimum number of parameters required to create these new CNN-based images. Our second aim will validate the trained algorithms with ultrasound data from experimental phantom and ex vivo tissue. Our third aim will extend our evaluation to ultrasound images of in vivo porcine kidneys. This work is the first to propose bypassing the entire beamforming process and replacing it with machine learning and computer vision techniques to remove traditionally problematic noise artifacts and create a fundamentally new type of artifact-free, high-contrast, high-resolution, ultrasound-based image for guiding interventional procedures. This work combines the expertise of an imaging scientist, a computer scientist, and an interventional ra- diologist to explore an untapped, understudied area that is only recently made feasible through improvements in computing power, advances in computer vision capabilities, and new knowledge about dominant sources of image degradation. Translation to in vivo cases is enabled by our clinical collaboration with the Department of Radiology at the Johns Hopkins Hospital. With support from the NIH Trailblazer Award, our team will be the first to develop these tools and capabilities to eliminate noise artifacts in interventional ultrasound, opening the door to a new paradigm in ultrasound image formation, which will directly benefit millions of patients with clearer, easier-to-interpret ultrasound images. Subsequent R01 funding will customize our innovation to addi- tional application-specific ultrasound procedures (e.g., breast biopsies, cancer detection, autonomous surgery).