Making Sense of the Data Trove Hidden in Medical Ultrasound Signals

理解隐藏在医学超声信号中的数据宝库

• 批准号: RGPIN-2020-04612
• 负责人: Rivaz, Hassan
• 金额: $ 2.4万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716239
• 关键词: Making; Sense; Data; Trove; Hidden

Ultrasound is a safe, fast and cost-effective imaging modality. Although it is one of the most popular medical imaging modalities, its potential has not been fully explored. Specifically, current processing techniques that utilize temporal ultrasound data are limited, despite its very high frame-rate capability. In addition, raw ultrasound data is not suitable for visualization, and as such, is converted to gray-scale images that are commonly referred to as B-mode ultrasound. This conversion, however, is lossy and destroys most of the information in raw data. These two factors constitute a trove of underutilized information that my two research thrusts aim to explore. Thrust 1. Ultrasound elastography uses raw ultrasound signals acquired at high frame-rates to reveal clinically relevant mechanical properties of the tissue, which are often invisible in the B-mode ultrasound images. It is a clinically and commercially successful emerging field, and as such, it is a good example of what can be achieved by better exploiting ultrasound data. However, there are several technical challenges in estimating these mechanical properties from ultrasound signals, and the first thrust of my research program aims to tackle them using novel Machine Learning (ML) techniques to further enhance the performance of elastography. Thrust 2. Backscatter quantitative ultrasound uses raw ultrasound data to estimate tissue properties such as attenuation, backscattering coefficients and effective scatterer size. These properties are related to cell attributes, such as size and shape, and are very important biomarkers of disease. However, current ultrasound imaging technology does not provide these properties. An important unresolved challenge in quantitative ultrasound is its high estimation variance, which has hindered clinical utility of this approach. The second thrust of my research program focuses on solving these challenges using novel ML methods. The importance of improving the capabilities of ultrasound is fourfold. First, it can lead to better diagnosis and guidance of surgical interventions, where ultrasound is extensively used. Second, as ultrasound is an inexpensive imaging modality, it reduces the cost of healthcare in Canada. Third, as ultrasound is widely available, it can improve access to healthcare especially in remote regions of Canada where patients currently have to fly to larger cities for diagnosis. And fourth, extracting tissue properties such as elasticity and scattering properties makes assessing ultrasound images less subjective, and reduces the need to consult expert clinicians, potentially further reducing the healthcare cost and improving accessibility. With recent technological advancement in healthcare and ML, demand for professionals in the field has peaked. The proposed research program will contribute to training of highly qualified personnel to help Canada maintain a leading role in medical imaging and ML.

超声是一种安全，快速且具有成本效益的成像方式。尽管它是最受欢迎的医学成像方式之一，但其潜力尚未得到充分探索。具体而言，尽管框架速率功能非常高，但使用时间超声数据的当前处理技术仍有限。此外，RAW超声数据不适合可视化，因此将通常称为B模式超声的灰度图像转换为灰度图像。但是，这种转换是有损的，并且会破坏原始数据中的大多数信息。这两个因素构成了我的两个研究推力旨在探索的未充分利用的信息。 推力1。超声弹力图使用在高框架速率上获得的原始超声信号来揭示组织与临床相关的机械性能，这些机械性能在B模式超声图像中通常是看不见的。这是一个临床和商业上成功的新兴领域，因此，这是通过更好地利用超声数据来实现的好例子。但是，从超声信号估算这些机械性能方面存在一些技术挑战，而我的研究计划的第一个作用旨在使用新颖的机器学习（ML）技术来解决它们，以进一步提高弹性表的性能。 推力2。反向散射定量超声使用原始的超声数据来估计组织特性，例如衰减，反向散射系数和有效的散射器大小。这些特性与细胞属性有关，例如大小和形状，并且是非常重要的疾病生物标志物。但是，当前的超声成像技术无法提供这些属性。在定量超声波中，重要的尚未解决的挑战是其较高的估计方差，这阻碍了这种方法的临床实用性。我的研究计划的第二个力量重点是使用新型ML方法解决这些挑战。 提高超声功能的重要性是四倍。首先，它可以更好地诊断和指导外科干预措施，其中广泛使用了超声。其次，由于超声是一种廉价的成像方式，因此它降低了加拿大的医疗保健成本。第三，由于超声波广泛可用，它可以改善获得医疗保健的机会，尤其是在加拿大偏远地区，目前患者必须飞往较大的城市进行诊断。第四，提取组织特性（例如弹性和散射特性）使评估超声图像的主观性降低，并减少了咨询专家临床医生的需求，从而有可能进一步降低医疗保健成本并改善可及性。 随着医疗保健和ML的最新技术进步，该领域对专业人员的需求达到了顶峰。拟议的研究计划将有助于培训高素质的人员，以帮助加拿大在医学成像和ML中保持领先作用。