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Lung-specific ultrasound beamforming for diagnostic imaging

用于诊断成像的肺部特异性超声波束形成

基本信息

批准号：
10673127
负责人：
Gianmarco Pinton
金额：
$ 19.03万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10673127
关键词：
Acoustics Acute Acute Respiratory Distress Syndrome Address Admission activity Adoption Air Alveolar Anatomy Base Sequence COVID-19 Calibration Chronic Chronic Phase Clinical Communities Complex Custom Data Data Set Diagnosis Diagnostic Imaging Disease Electrons Family suidae Human body Image Image Analysis Imaging Device Left Link Lung Lung diseases Machine Learning Maps Measurement Measures Methods Miniaturization Modality Modeling Monitor Morphologic artifacts Penetration Physics Pleura Pleural Pleural effusion disorder Pneumothorax Public Health Pulmonary Pathology Sensitivity and Specificity Severities Source Structure Structure of parenchyma of lung Sum Syndrome System Techniques Tissue imaging Tissues Ultrasonography X-Ray Computed Tomography X-Ray Medical Imaging accurate diagnosis convolutional neural network cost design diagnostic value experimental study imaging modality imaging system improved in silico in vivo interstitial lung basal segment lung imaging point of care portability rational design simulation soft tissue tool ultrasound

项目摘要

PROJECT SUMMARY Accurate diagnosis and monitoring of lung disease, including the urgent need arising from Covid-19, could be widely addressed by ultrasound imaging. The standard modalities that diagnose and monitor lung disease are X-ray imaging and computed tomography (CT) due to their extensive diagnostic capabilities. Ultrasound may not be normally thought of as a primary lung imaging modality, however in the hands of an expert user it has a sensitivity and specificity ranging from 90% to 100% relative to CT. For non-expert users the interpretation of lung ultrasound images can be complex because ultrasound cannot penetrate the soft-tissue/air interface. Thus, lung ultrasound relies on the interpretation of imaging "artefacts" that appear to come from deep inside the air space of the lung, but are actually complex reverberations from the pleural interface. These reflections carry information about the underlying lung pathology. This indirect imaging and clinical interpretation approach is fundamentally different from imaging in soft tissue, where echos come directly from the structures being imaged. Nevertheless, delay-and-sum beamforming methods currently used in ultrasound systems are identical for lung imaging and soft tissue imaging. The lack of understanding of the fundamental acoustics at the complex soft-tissue/air interface remains an impediment to the rational design of ultrasound imaging sequences that can relate directly to lung acoustics and would be more sensitive to disease. To overcome this challenge, we propose to develop and validate new ultrasound imaging and beamforming methods using a physics-based approach that establishes a quantitative link between ultrasound imaging and the disease state of the lungs. We hypothesize that ultrasound beamforming techniques that are designed specifically for the lung and its complex reverberation physics will generate higher quality images, improved clinical interpretability, and diagnostic capabilities. We will develop acoustical simulation tools and simulations of the human body and lung disease that are experimentally calibrated to accurately represent the relevant reverberation physics, such as A-line and B-line artefacts. Spatial coherence beamformers, which rely on reverberation as a source of contrast and machine learning beamformers will be designed and optimized to detect lung disease. These beamformers will be implemented on a programmable scanner and compared to conventional B-mode imaging. If successful, this proposal will yield ultrasound imaging methods that are more sensitive to lung disease, with clearer clinical interpretability, that can be deployed in current ultrasound imaging systems.

项目概要肺部疾病的准确诊断和监测，包括 Covid-19 引起的迫切需求，可以超声成像广泛解决了这一问题。诊断和监测肺部疾病的标准方法是 X 射线成像和计算机断层扫描 (CT) 具有广泛的诊断能力。超声波可能不会通常被认为是主要的肺部成像方式，但在专家用户手中，它具有相对于 CT 的敏感性和特异性范围为 90% 至 100%。对于非专家用户来说，肺部超声图像的解释可能很复杂，因为超声不能穿透软组织/空气界面。因此，肺部超声检查依赖于对成像“伪影”的解释看似来自肺部深处的空气，但实际上是来自肺部的复杂混响胸膜界面。这些反射携带有关潜在肺部病理学的信息。这种间接成像临床解释方法与软组织成像根本不同，软组织成像会产生回声直接来自正在成像的结构。尽管如此，目前使用的延迟求和波束形成方法超声系统中的肺部成像和软组织成像是相同的。缺乏对问题的理解复杂的软组织/空气界面的基本声学仍然是合理设计的障碍超声成像序列可以直接与肺声学相关并且对疾病更敏感。为了克服这一挑战，我们建议开发和验证新的超声成像和波束形成使用基于物理的方法在超声成像和肺部的疾病状态。我们假设设计的超声波束形成技术专门针对肺部及其复杂的混响物理将生成更高质量的图像，改进临床可解释性和诊断能力。我们将开发声学模拟工具和模拟经过实验校准以准确表示相关的人体和肺部疾病混响物理，例如 A 线和 B 线伪影。空间相干波束形成器，它依赖于混响作为对比源，机器学习波束形成器将被设计和优化以检测肺部疾病。这些波束形成器将在可编程扫描仪上实现，并与传统的 B 型成像。如果成功，该提案将产生更先进的超声成像方法对肺部疾病敏感，具有更清晰的临床解释性，可应用于当前的超声成像系统。