High throughput data interfacing

for real-time medical imaging applications

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Current high-end medical X-ray intervention devices provide a tremendous amount of high-definition images per second. Combined with the additional inputs from the numerous auxiliary devices, processing and compositing the data in real-time quickly becomes an arduous engineering challenge. Furthermore, the critical nature of this application domain allows no room for error, whereas the process has to be simultaneously flexible enough to permit the unhindered work of the corresponding medical professional. Such medical imaging applications presently rely on complete hardware implementations of their processing pipelines and compositor engines, as opposed to earlier adopted paradigms of hardware-accelerated solutions. Said engines are usually realised in FPGA platforms due to their massively parallel computational capabilities and exceptional connectivity. However, given the vast amount of input streams arriving at the compositor's pipeline, switching and routing to available processing resources has remained a persistent issue. Present commercial solutions prove to be either too costly, too slow or too inflexible for developers to consider. On the other hand, academic literature centred around the subject is severely lacking. This thesis proposes a unique hardware architecture solution to mitigate the aforementioned problem. The proposed architecture, developed in collaboration with Philips Healthcare and TU Delft, exhibits an average end-to-end latency of 100 ns and a throughput of 7.2 Gb/s. Additionally, these results were realised under marginal resource utilisation, proving that such a switch can fit in the FPGA device and, subsequently, be developed as an integrated part of the compositor engine. Finally, the limited amount of reserved memory resources allowed for near-linear scalability of the proposed design should an increased amount of I/O devices be added; as well as dynamic portability should migration to smaller devices be a concern.