Synthetic X-Ray Image Generation Using FPGA-Based Hardware Acceleration

Abstract

Synthetic image generation involves the creation of artificially generated images that are indistinguishable from real ones. This field is an answer to challenges in the world of data acquisition, where the need for data is outpacing the availability. In cooperation with Philips Medical Systems, the generation of synthetic X-ray images is studied. Using datasets derived from such images, equipment testing and physician training can be improved. Additionally, training data can be generated for machine learning purposes.

The generation of synthetic X-ray images has been an area of research since at least 1994. The images have traditionally been generated using ray-tracing techniques on CPUs or GPUs. While effective, these methods are computationally expensive and demand high memory bandwidths. More recently, machine learning techniques have been explored for X-ray image generation. These approaches are promising. However, they require large labelled datasets which are often unavailable and the quality of the results is difficult to predict.

The aim of this thesis is to investigate whether hardware acceleration using a field programmable gate array (FPGA) can solve the challenges other methods face. Specifically, it discusses an architecture that can handle the large amount of computations in parallel. The memory architecture required to handle the high bandwidth demands is also explained. The performance of the proposed architecture is studied to see whether it is a viable solution.

By simulating the traversal of rays through a voxelized model, an attenuation map was computed which can be used to determine X-ray intensities on a detector. The design separates computational tasks between a host machine and an FPGA, with an optimized High Bandwidth Memory architecture to maximize data throughput. Results demonstrated that the simulation produced realistic images with minimal error (2.26\% - 3.00\% deviation from CPU results), and performance is dependant on the detector resolution, achieving frame rates between 123 and 378 frames per second which are well above the goal of 60 frames per second. If more performance is required, upsampling can be used to speed up image generation by 33\% at an increased error of 0.6\% for an upsampling factor of two. These findings highlight the advantages of FPGA acceleration for deterministic, high-speed synthetic image generation without the need for large labelled datasets as required by machine learning algorithms.