  Deep learning-based lossless compression methods offer substantial advantages
in compressing medical volumetric images. Nevertheless, many learning-based
algorithms encounter a trade-off between practicality and compression
performance. This paper introduces a hardware-friendly streaming lossless
volumetric compression framework, utilizing merely one-thousandth of the model
weights compared to other learning-based compression frameworks. We propose a
gated recurrent convolutional neural network that combines diverse
convolutional structures and fusion gate mechanisms to capture the inter-slice
dependencies in volumetric images. Based on such contextual information, we can
predict the pixel-by-pixel distribution for entropy coding. Guided by
hardware/software co-design principles, we implement the proposed framework on
Field Programmable Gate Array to achieve enhanced real-time performance.
Extensive experimental results indicate that our method outperforms traditional
lossless volumetric compressors and state-of-the-art learning-based lossless
compression methods across various medical image benchmarks. Additionally, our
method exhibits robust generalization ability and competitive compression speed
