MSc SS Thesis Presentation

Improving Ultrafast Doppler Imaging using Subspace Tracking

Bastian Generowicz

Ultrafast Doppler imaging provides a new way to image blood motion at thousands of frames per second. It has gained popularity due to its high spatio-temporal resolution, which is required to distinguish blood motion from clutter signals caused by slow moving tissue. By conducting functional UltraSound (fUS) experiments on the brain using this method, we are able to better understand the underlying processes during brain activity through neurovascular coupling. fUS relies on optimized signal processing techniques to acquire and process high frame-rate images in real-time.

For my thesis I have set up the backbone to allow for fUS experiments as well as created the analysis framework required to analyse and interpret the incoming data. Furthermore, I have developed a more computationally efficient method of obtaining vascular images, based on the Projection Approximation Subspace Tracking (PAST) method. The PAST algorithm is able to display accurate representations of the blood subspace, while maintaining a lower computational complexity than the state-of-the-art method, making it suitable for Doppler imaging. When applied to functional ultrasound, the exponentially weighted PASTd method achieves similar Pearson Correlation coefficients compared to the current state-of-the-art method, over multiple functional experiments. These findings highlight the potential of applying PAST to Ultrafast Doppler imaging.

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