Hardware acceleration of artificial X-ray image generation

Master thesis (2023)

Authors

H.J.M.T. Knops Electrical Engineering, Mathematics and Computer Science

Contributors

Z. Al-Ars Computer Engineering - EEMCS (mentor)
R.F. Remis Tera-Hertz Sensing - EEMCS (graduation committee member)
Rob de Jong Philips Healthcare Nederland (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Per Knops
More Info
expand_more

Published Date

29-11-2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

X-ray imaging systems play an important role in the diagnostic process of various medical conditions. Generating an accurate artificial X-ray image has multiple advantages. It allows for flexible configurations during generation. The resulting images can reduce testing time and cost, help the training of surgeons, and increase the amount of data for artificial intelligence model training. The generation of an X-ray image involves the simulation of a raytracing algorithm through a data model. In this research, a naive approach to this problem is examined. It was found that this approach can be improved by implementing model parallelization, data caching, and data compression. The resulting algorithm is simulated and validated in a software environment. This is then implemented for both an Ultrascale+ and a Versal FPGA. The results show that the algorithm can achieve real-time X-ray image generation, matching the performance of currently used detectors, provided that the required memory performance is achieved.