Research in magnetohydrodynamics (MHD) aims to understand the complex interactions of electrically conductive fluids and magnetic fields. A promising approach for investigating complex instationary flow phenomena are lab-scale experiments with low-melting alloys. They require a noninvasive flow instrumentation for opaque liquids with a high spatiotemporal resolution, a low velocity uncertainty and a long measurement duration. Ultrasound Doppler velocimetry can achieve multiplane, multicomponential flow imaging with multiple linear ultrasound arrays. However the average raw data output amounts to <span classCombining double low line"inline-formula">1.2 GBs-1</span> at a frame rate of <span classCombining double low line"inline-formula">33 Hz</span> in a typical configuration for 200 transducers. This usually prevents long-duration measurements when offline signal processing is used. In this paper, we propose an online signal-processing chain for pulsed-wave Doppler velocimetry that is tailored to the specific requirements of flow imaging for lab-scale experiments. The trade-off between measurement uncertainty and computational complexity is evaluated for different algorithmic variants in relation to the Cramér-Rao bound. By utilizing selected approximations and parameter choices, a prepossessing could be efficiently implemented on a field-programmable gate array (FPGA), enabling a typical reduction of the data bandwidth of 6.5:1 and online flow visualization. We validated the performance of the signal processing on a test rig, yielding a velocity standard deviation that is a factor of 3 above the theoretical limit despite a low computational complexity. Potential applications for this signal processing include multihour flow measurements during a crystal-growth process and closed-loop velocity feedback for model experiments.