Making Sense of the Data Trove Hidden in Medical Ultrasound Signals

超声波是一种安全、快速和具有成本效益的成像方式。虽然它是最流行的医学成像模式之一，但其潜力尚未得到充分开发。具体地，利用时间超声数据的当前处理技术是有限的，尽管其非常高的帧速率能力。此外，原始超声数据不适合于可视化，并且因此被转换为通常被称为B模式超声的灰度图像。然而，这种转换是有损的，并且破坏了原始数据中的大部分信息。这两个因素构成了我的两个研究重点旨在探索的未充分利用的信息宝库。 一号推力超声弹性成像使用以高帧速率采集的原始超声信号来揭示组织的临床相关机械特性，这些机械特性通常在B模式超声图像中不可见。这是一个临床和商业上成功的新兴领域，因此，它是通过更好地利用超声数据可以实现的一个很好的例子。然而，从超声信号中估计这些机械特性存在一些技术挑战，我的研究计划的第一个目标是使用新的机器学习（ML）技术来解决这些问题，以进一步提高弹性成像的性能。2号推力反向散射定量超声使用原始超声数据来估计组织特性，例如衰减、反向散射系数和有效散射体尺寸。这些特性与细胞属性（如大小和形状）有关，是非常重要的疾病生物标志物。然而，当前的超声成像技术不提供这些特性。定量超声中一个重要的未解决的挑战是其高估计方差，这阻碍了该方法的临床实用性。我的研究计划的第二个重点是使用新颖的ML方法解决这些挑战。提高超声能力的重要性有四个方面。首先，它可以导致更好的诊断和指导手术干预，其中超声被广泛使用。其次，由于超声波是一种廉价的成像方式，它降低了加拿大的医疗保健成本。第三，由于超声波广泛使用，它可以改善医疗保健的可及性，特别是在加拿大的偏远地区，患者目前必须飞往大城市进行诊断。第四，提取组织特性（如弹性和散射特性）使评估超声图像的主观性降低，并减少了咨询专业临床医生的需求，从而可能进一步降低医疗成本并提高可及性。随着最近医疗保健和ML的技术进步，对该领域专业人员的需求已经达到顶峰。拟议的研究计划将有助于培养高素质的人才，以帮助加拿大在医学成像和ML领域保持领先地位。

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.