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、经济的成像方式。虽然它是最流行的医学成像方式之一，但其潜力尚未得到充分探索。具体来说，目前利用时间超声数据的处理技术是有限的，尽管它具有非常高的帧率能力。此外，原始超声数据不适合可视化，因此，被转换为灰度图像，通常被称为b型超声。然而，这种转换是有损的，并且会破坏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。