Hardware acceleration of artificial X-ray image generation
H.J.M.T. Knops (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Z. Al-Ars – Mentor (TU Delft - Computer Engineering)
R.F. Remis – Graduation committee member (TU Delft - Tera-Hertz Sensing)
Rob de Jong – Coach (Philips Healthcare Nederland)
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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.