Edge Computing and Hardware Acceleration for HSI in Clinical Workflows

Hyperspectral imaging (HSI) enables real-time, non-invasive tissue characterization by capturing biochemical signatures across hundreds of spectral bands simultaneously. However, the computational demands of processing vast hyperspectral datasets create significant bottlenecks for clinical deployment. This chapter examines how edge computing and hardware acceleration technologies—including Field-Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs), and specialized neural accelerators—enable real-time HSI processing at the point of care. We analyze architectural trade-offs, implementation strategies, and clinical workflow integration approaches that reduce processing times from minutes to milliseconds. Recent advances in embedded platforms achieve throughput rates exceeding 250 MSamples/s while maintaining power consumption below 15W, making intraoperative HSI guidance practically achievable.

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