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.

项目摘要准确诊断和监测肺部疾病，包括19ci-19产生的紧急需求，可能是通过超声成像广泛解决。诊断和监测肺病的标准方式是 X射线成像和计算机断层扫描（CT）由于其广泛的诊断功能。超声可能不会通常可以将其视为主要的肺成像方式，但是在专家用户的手中，它具有相对于CT，灵敏度和特异性范围从90％到100％。对于非专家用户，肺超声图像的解释可能很复杂，因为超声无法穿透软组织/空气界面。因此，肺超声依赖于成像“人工制品”的解释这似乎来自肺部空间内部的深处，但实际上是来自胸膜界面。这些反思带有有关潜在肺病理学的信息。这种间接成像临床解释方法从根本上与软组织中的成像有所不同，而软组织的成像直接来自所成像的结构。然而，当前使用在超声系统中，用于肺成像和软组织成像相同。缺乏对复杂的软组织/空气界面的基本声学仍然是理性设计的障碍可以直接与肺声学有关的超声成像序列，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成形使用基于物理学的方法建立了超声成像与肺的疾病状态。我们假设设计的超声波梁形成技术专门针对肺及其复杂的混响物理学将产生更高质量的图像，改进临床解释性和诊断能力。我们将开发声学模拟工具和模拟经过实验校准以准确代表相关的人体和肺部疾病混响物理学，例如A线和B线手工艺。空间连贯的光束形式，依靠将混响作为对比度和机器学习边界器的来源，将被设计和优化检测肺部疾病。这些波束形式将在可编程扫描仪上实现，并与常规的B模式成像。如果成功，该建议将产生更多的超声成像方法对肺部疾病的敏感，具有更清晰的临床可解释性，可以部署在当前的超声成像中系统。