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

项目摘要 尽管超声成像在今天的医院中广泛流行，但超声的临床应用 制导受到杂波和混响伪影的严重阻碍，这些伪影模糊了感兴趣的结构和通信- 复杂的解剖学测量。杂乱在超重和肥胖的人中尤其成问题，他们 占北美的7860万成年人和1280万儿童。同样，介入性程序- 其中10个需要插入一个或多个金属工具，这些工具会产生混响伪影，从而混淆乐器 位置、方向和几何形状，同时遮挡附近的组织，从而额外阻碍超声成像。 年龄素质。尽管人工制品是有问题的，但超声波继续存在的主要原因是它最大的 比较优势(即移动性、成本、非电离辐射、实时可视化和多平面视图) 现有的图像制导选项，但它将明显更有用的fi没有问题的伪影。 我们的长期项目目标是使用最先进的机器学习技术提供介入性 放射科医生使用基于超声的无伪影图像。我们将初步开发一种新的框架替代方案 用于消除针尖混响和由以下因素引起的声学杂波的超声波束形成过程 近fi-ELD组织在导针至肾脏时的多路径散射 石头。我们的fiFirst目标将测试输入原始通道数据和输出的卷积神经网络(CNN 人类可读的图像，没有由多路径散射和混响造成的伪影。次要目标 CNN的一个关键是学习创建这些基于CNN的新图像所需的最低参数数量。 我们的第二个目标是使用来自实验体模和体外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估扩展到活体猪肾脏的超声图像。这项工作是 fi首次提议绕过整个波束形成过程，代之以机器学习和计算机 视觉技术，以消除传统上有问题的噪声伪影，并创建一种全新的 无伪影、高对比度、高分辨率、基于超声的图像，用于指导介入操作。 这项工作结合了成像科学家、计算机科学家和介入性研究人员的专业知识-- 营养学家探索一个尚未开发、研究不足的领域，该领域最近才通过改进而变得可行 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 图像质量下降。通过我们与该部门的临床合作，能够将病例转化为活体病例 约翰·霍普金斯医院的放射学博士。在NIH开拓者奖的支持下，我们的团队将 fi第一次开发这些工具和能力来消除介入性超声中的噪声伪影，打开 通往超声成像新范式的大门，这将直接使数以百万计的fi患者受益 更清晰、更容易解释的超声波图像。后续的R01资金将定制我们的创新以添加- 传统应用-特定的fic超声程序(例如，乳房活检、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).