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FPGA acceleration of ultrasound computed tomography for in vivo tumor screening

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Breast cancer comprises 22.9% of all cancers in women. Survival rates can be greatly improved with early detection of a growing tumor, which can be screened routinely and hazard-free with medical ultrasound (US). However, many tumors show a low US echogenicity, which make their detection with conventional B-mode imaging challenging. The tumor tissue is stiffer than the healthy tissue, which increases the speed of sound (SoS) and acoustic attenuation (AA). The computer vision laboratory at ETHZ developed and patented a hand-held ultrasound technology (, which is able to perform accurate tomographic reconstruction of SoS/AA, providing high-contrast tumor images. In the next months, this technology will be evaluated in medical trials.

A major highlight of conventional US B-mode imaging is its capability to operate in real time, which allows for efficient screening of suspect tissue structures by smoothly adjusting the position and orientation of the US probe with respect to the body of the patient. However, the novel SoS/AA modalities require sophisticated software algorithms, which exceed the real time processing capabilities of conventional medical ultrasound systems. The acquired data is therefore currently processed off-line in a high-performance computing server. In order to optimize the screening procedure in medical trials, we now aim at incorporating portable computing platforms as an add-on to conventional ultrasound systems. This platforms will allow in-line processing of ultrasound SoS/AA data during medical screening.

The topic of the offered semester/master thesis is the translation and optimization of signal processing from high-level programming languages (Matlab®, C/C++) into an FPGA architecture. The main focus is on advanced ultrasound beamforming and displacement tracking algorithms, which are essential building blocks of SoS/AA imaging. The algorithms will be implemented in a FPGA development board (Xilinx Kintex UltraScale), and benchmarked in terms of numerical accuracy and speedup achievement. Finally, a real time processing pipeline will be assembled with ultrasound raw data acquired with our medical ultrasound systems (Ultrasonix and Verasonics), and experimentally validated with medical phantoms.


Semester/Master Thesis
Supervision: Pascal Alexander Hager (IIS)
Supervision: Sergio Sanabria (Computer Vision Lab ETHZ)


Luca Benini
Orcun Göksel (Computer Vision Lab ETHZ)


40% Theory, Algorithm and Simulation
60% Architecture & Implementation (VHDL)


MatLab, VHDL (VLSI1)


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