Backscatter ultrasound physics for image segmentation and biological tissue characterization

用于图像分割和生物组织表征的反向散射超声物理

• 批准号: RGPIN-2016-05212
• 负责人: Cloutier, Guy
• 金额: $ 2.91万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615562
• 关键词: Backscatter; ultrasound; physics; image; segmentation

BACKGROUND: Clinical ultrasound (US) systems include B-mode for structure imaging, Doppler modes for flow characterization, and elastography modes for tissue deformation and elasticity assessments. Other methods using radiofrequency (RF) backscatter echoes have also been developed for tissue characterization of numerous organs. FRAMEWORK and OBJECTIVE: Modern quantitative US (QUS) RF-based methods for tissue characterization mainly rely on two main strategies. One approach consists in modeling the frequency-dependent backscatter coefficient (spectroscopy analysis) to describe microstructural properties of tissues; whereas a second approach is to use 1st and 2nd order statistics of the echo envelope to define speckle properties. None of these approaches has been implemented yet on clinical scanners. The framework of this proposal consists in unifying these different concepts to propose diagnostic parameters with a physical interpretation. This will be done by considering a frequency dependent structure factor and mixtures of homodyned K-distributions of echo statistics. By unifying QUS concepts, we aim identifying complementary parameters with specific signatures of tissue microstructures for the purpose of pathological tissue segmentation and characterization. Targeted applications are imaging of atherosclerotic plaques, blood clots, breast tumours and diseased tendons. NOVELTY and IMPACT: US is arguably the hardest medical imaging modality upon which to perform segmentation (and tissue characterization) as image contrast and structure definition are much worse than competing magnetic resonance and computed tomography imaging technologies. Accordingly, most computer-based methods for US segmentation require anatomical, geometrical, temporal and/or image physics priors. In this grant, we propose novel image physics concepts in the framework of a Bayesian segmentation and tissue characterization model that can include anatomical, geometrical and temporal priors to dynamically track organs within an image sequence. This project is technically challenging and based on fundamental acoustic physics, and should directly impact human health as targeted applications are broad and cover active clinical areas of research of my laboratory (clinical data acquisitions are supported by other active grants). CONCLUSION: US imaging represents today the largest grow in term of number of units in the radiology and medical physics fields because it is non-invasive, hand-held, relatively inexpensive compared to other imaging technologies, and also because it can be used on the bed size. Depending on the application and modality used, US is also sensitive and specific. The development of QUS-based methods, as proposed in this grant program, should impact this momentum and provide clinicians with new imaging modalities for better diagnostic and therapy follow-up.

背景技术背景：临床超声（US）系统包括用于结构成像的B模式、用于血流表征的多普勒模式以及用于组织变形和弹性评估的弹性成像模式。还开发了使用射频（RF）反向散射回波的其他方法用于许多器官的组织表征。 框架和目的：用于组织定征的现代定量US（QUS）RF方法主要依赖于两种主要策略。一种方法包括在建模的频率相关的反向散射系数（光谱分析），以描述组织的微观结构特性;而第二种方法是使用第一和第二阶统计的回波包络，以定义散斑特性。这些方法都还没有在临床扫描仪上实现。该建议的框架包括统一这些不同的概念，提出诊断参数与物理解释。这将通过考虑依赖于频率的结构因子和回声统计的零差K分布的混合来完成。通过统一QUS概念，我们的目标是识别具有组织微结构特定特征的互补参数，以实现病理组织分割和表征的目的。目标应用是动脉粥样硬化斑块、血凝块、乳腺肿瘤和病变肌腱的成像。 新奇和影响：US可以说是最难进行分割（和组织定征）的医学成像模式，因为图像对比度和结构清晰度比竞争的磁共振和计算机断层扫描成像技术差得多。因此，用于US分割的大多数基于计算机的方法需要解剖、几何、时间和/或图像物理先验。在这项授权中，我们提出了新的图像物理概念的框架内的贝叶斯分割和组织表征模型，可以包括解剖，几何和时间先验动态跟踪器官的图像序列。该项目在技术上具有挑战性，基于基础声学物理学，并且应该直接影响人类健康，因为目标应用广泛，涵盖了我实验室研究的活跃临床领域（临床数据采集由其他积极赠款支持）。 结论：US成像代表了当今放射学和医学物理学领域中单位数量的最大增长，因为它是非侵入性的，手持式的，与其他成像技术相比相对便宜，并且还因为它可以在床大小上使用。根据应用和使用的方式，US也是敏感和特异性的。本资助计划中提出的基于QUS的方法的发展应该会影响这一势头，并为临床医生提供新的成像模式，以更好地进行诊断和治疗随访。