Functional ultra-sound (fUS) imaging is a powerful new modality that has emerged in the last decade and is capable of providing unprecedented functional images of the brain as sub-micrometer spatial and sub-neural-spiking temporal resolution. The CUBE multidisciplinary facility and the NeuroComputing Lab (NCL) of the Erasmus Medical Center, Rotterdam, are building novel fUS technology and are finally in possession of an imaging modality that can track in high detail the workings of the human brain. The unprecedented quality of fUS, which also has the potential to outperform mature modalities like fMRI, comes at the cost of large data sizes being generated. Currently, by using a 2D fUS acquisition probe, data is being generated at 6 GB/s.
To tackle this data deluge as well as other technical challenges, this MSc thesis topic aims at building an FPGA-based acquisition setup which will facilitate high ultrasound-channel counts (≥ 8 channels @ ≥ 12 bits/channel) at high sampling rates (10 – 30 MHz). The design-space exploration involves selecting the best-of-breed FPGA device to handle the data-rate needs, co-designing with a CPU (e.g. via SDAccel) to handle interfacing to the outside world and also deploying signal-processing and deep-learning algorithms for real-time fUS-image processing on-board. This research is being jointly conducted by the Erasmus Medical Center and the Delft University of Technology.
Keywords: FPGA acceleration, HPC, image processing, deep learning.
Prerequisites: FPGA design, signal processing (basic), C language, Vivado C language.
Optionally: Image processing, Machine learning, deep learning.
Contact: Christos Strydis