Hardware Accelerated Synthetic X-Ray Medical Image Generation Using HBM-Based FPGAs

Abstract

Synthetic image generation involves the creation of artificially generated images that are indistinguishable from real ones. Conventional simulation-based image synthesis approaches suffer from intensive computational and memory throughput demands associated with physically accurate ray tracing through volumetric datasets. In this work, we propose an FPGA-based accelerator architecture capable of handling the computations required to simulate physically accurate X-ray images in real time. In addition, an algorithm is developed that can calculate the path of an X-ray through a phantom representing a physical model. To ensure real-time performance, a parallel accelerator architecture is proposed using a chain of accelerator kernels combined with High Bandwidth Memory architecture, which can simulate many rays concurrently, addressing the computational and memory throughput demands associated with simulationbased X-ray image generation. Performance evaluation of the simulation on an AMD Alveo U50 Data Accelerator card shows that an average speed-up of 12 x over CPU-based implementations is possible, and allows for realtime image synthesis at a frame rate of 60 images/s. These findings highlight the advantages of FPGA acceleration for deterministic, high-speed synthetic image generation.