Period: 2011-current
Collaboration with Philips Research North America
Info:
Ultrasound (US) imaging has been used in the medical field for over 20 years for applications ranging from fetus monitoring to tumor ablation, hence spanning both the diagnostic and therapeutic worlds. Its versatility and cost-effectiveness with respect to other imaging modalities, such as MRI and X-ray, make it a great tool in the medical imaging arsenal. However, in spite of recent developments and enhancements, US imaging still has several drawbacks. In order to compute a single image, a vast amount of data has to be acquired and processed by the scanner, impacting the frame rate of the machine, among other aspects.
Independently, an inverse problem technique, Compressive Sensing (CS), has emerged and gained a lot of interest from researchers over the past decade because of its ability to recover undersampled information under some mathematical assumptions. After being thoroughly developed and supported as a mathematical theory, it was successfully applied to MRI, effectively decreasing the acquisition time significantly. Naturally, researchers from other fields have been trying to adapt CS to their own problems.
This work aims to adapt CS to US imaging and to develop a method that decreases the amount of data acquired while maintaining the image quality, relying on physical models, wavelets, and processing power to fill in the gaps, so to speak.
