Making Sense of the Data Trove Hidden in Medical Ultrasound Signals

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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.