[2023] Memristor implementation of compressive-imaging algorithm for revolutionary brain imaging

3D-dimensional ultrasound is a very powerful imaging technique for the brain, but it requires transducers that accommodate thousands of sensors and complex hardware which restricts its use to specialized clinical applications in developed countries. Manufacturing this type of transducers is complex and very costly. We have already proposed a novel approach that uses a technique called compressive sensing and can generate 3D imaging. The key idea behind this technique is that a simple plastic coding mask placed in front of the ultrasound transducer can compress 3D information requiring far less sensors. This concept extends the imaging capabilities of commercial ultrasound probes from 2D to 3D, but also opens up the possibility for a new type of wearable imaging device for long-time monitoring/imaging of the brain. Key to deploying this technology is the “inverse calculation” of the ultrasound deformations the plastic mask creates while imaging a target (here: neural tissue in living brains). This calculation involves, as a first step, solving a massive linear system of equations, in the range of 30 GB of data per new imaging target. Currency work in the lab involves porting this algorithm onto state-of-the-art GPUs, however we are severely limited by memory-capacity and low-latency concerns, since the system is a streaming, real-time setup.

Assignment

The compressive-sensing algorithms have already been ported to an FPGA for accelerated processing. The next step involves further optimizing the existing kernel, exploring alternative implementations and, finally, porting it to a memristor-based platform.

Requirements

MSc student in Computer Engineering, Embedded Systems or Biomedical Engineering with a profile in signal processing (basic), hardware design, and optionally, FPGA design, deep learning (basic)

Contact

Last modified: 2022-10-04