Our proposed architecture’s trade-off point.

Medical volume data are rapidly increasing, growing from gigabytes to petabytes, which presents significant challenges in organisation, storage, transmission, manipulation, and rendering. To address the challenges, we propose an end-to-end architecture for data compression, leveraging advanced deep learning technologies. This architecture consists of three key modules: downsampling, implicit neural representation (INR), and super-resolution (SR). We employ a trade-off point method to optimise each module’s performance and achieve the best balance between high compression rates and reconstruction quality. Experimental results on multi-parametric MRI data demonstrate that our method achieves a high compression rate of up to 97.5% while maintaining superior reconstruction accuracy, with a Peak Signal-to-Noise Ratio (PSNR) of 40.05 dB and Structural Similarity Index (SSIM) of 0.96. This approach significantly reduces GPU memory requirements and processing time, making it a practical solution for handling large medical datasets.

医学体数据量正呈快速增长之势，从吉字节量级跃升至拍字节量级，这在数据组织、存储、传输、处理与渲染等方面均带来了严峻挑战。为应对上述挑战，本研究提出一种依托先进深度学习技术的端到端（end-to-end）数据压缩架构。该架构包含三大核心模块：下采样模块、隐式神经表示（Implicit Neural Representation, INR）模块以及超分辨率（Super-Resolution, SR）模块。本研究采用权衡点法对各模块性能进行优化，实现高压缩率与重构质量间的最优平衡。基于多参数磁共振成像（Magnetic Resonance Imaging, MRI）数据的实验结果表明，本方法在保持优异重构精度的同时可实现高达97.5%的压缩率，其峰值信噪比（Peak Signal-to-Noise Ratio, PSNR）为40.05 dB，结构相似性指数（Structural Similarity Index, SSIM）达0.96。该方法可显著降低图形处理器（Graphics Processing Unit, GPU）的显存占用与处理耗时，为大规模医学数据集的处理提供了切实可行的解决方案。