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

波束成形的机器学习替代方案可提高肾脏介入治疗的超声图像质量

• 批准号: 9913520
• 负责人: Muyinatu A. Lediju Bell
• 金额: $ 23.5万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9913520
• 关键词: Abdomen; Acoustics; Adolescent; Adult; Affect; Age; Algorithms; Anatomy; Architecture; Area; Award; Back; Biopsy; Breast biopsy; Bypass; Cancer Detection; Cardiac; Child; Clinical; Collaborations; Computer Vision Systems; Computer software; Computers; Custom; Cyst; Data; Diagnosis; Diagnostic; Elements; Environment; Evaluation; Excision; Family suidae; Fatty Liver; Funding; Geometry; Goals; Hospitals; Human; Image; Image-Guided Surgery; Imaging Phantoms; Individual; Intervention; Interventional Ultrasonography; Kidney; Kidney Calculi; Knowledge; Learning; Liver diseases; Location; Machine Learning; Measurement; Measures; Metals; Methodology; Methods; Modeling; Morphologic artifacts; Needles; Network-based; Noise; Nonionizing Radiation; North America; Obesity; Operative Surgical Procedures; Output; Overweight; Pain; Patients; Prevalence; Procedures; Process; Radiology Specialty; Readability; Resolution; Retroperitoneal Space; Scientist; Signal Transduction; Source; Structure; Surgical Instruments; Techniques; Testing; Thick; Time; Tissues; Training; Translations; Ultrasonography; United States; United States National Institutes of Health; Variant; Visualization; Work; algorithm training; base; clinical effect; convolutional neural network; cost; deep learning; fetal; image guided; image guided intervention; imaging scientist; improved; in vivo; innovation; instrument; interest; lens; machine learning algorithm; metallicity; novel; radiologist; signal processing; tool

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).

项目摘要 尽管当今医院的超声成像的普遍性频率较大，但超声波的临床实用性 混乱和重新产物严重阻碍了指导 PLATE解剖测量。在超重和肥胖的个人中，混乱尤其有问题 在北美，占7860万成人和1,280万儿童。同样，介入程序 十个需要插入一种或多种金属工具，该工具产生重新构造的伪造仪器 位置，方向和几何形状，同时遮盖了附近的组织，从而妨碍了超声检查 年龄质量。尽管文物是有问题的，但由于其最大的 相比 对于现有的图像引导选项，但如果没有问题的伪像，它将更有用。 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介入 具有无伪影超声图像的放射科医生。我们最初将开发一个新的框架替代方案 进行超声梁的过程，该过程消除了针头的回响和由 将针头引导到肾脏时，多径散射在近部组织中 石头。我们的第一个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，没有由多径散射和恢复引起的伪影。次要目标 CNN的最小参数数量是创建这些新的基于CNN的图像所需的最小参数。 我们的第二个目标将通过实验性幻影和Ex Vivo验证训练有素的算法 组织。我们的第三个目标将我们的评估扩展到体内猪肾脏的超声图像。这项工作是 首先提出绕过整个波束形成过程并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像，并创建一种新型的新型 无伪影，高对比度，高分辨率，基于超声的图像，用于指导介入程序。 这项工作结合了成像科学家，计算机科学家的专业知识以及介入的RA- 培训医生要探索一个未开发的，被理解的领域，直到最近才通过改进才能可行 在计算能力，计算机视觉能力的进步以及有关主要来源的新知识 图像退化。我们与部门的临床合作启用了转换为体内病例 约翰·霍普金斯医院放射学。在NIH开拓者奖的支持下，我们的团队将是 第一个开发这些工具和功能以消除介入超声波的噪声伪像的功能，打开 超声图像形成中新范式的门，这将直接受益于数百万患者 清晰，更容易解释的超声图像。随后的R01资金将自定义我们的创新 特定于特定的超声操作（例如乳房活检，癌症检测，自主手术）。