With the rapid advancements in medical data acquisition and production, increasingly richer representations exist to characterize medical information. However, such large-scale data do not usually meet computing resource constraints or algorithmic complexity, and can only be processed after compression or reduction, at the potential loss of information. In this work, we consider specific Gaussian mixture models (HD-GMM), tailored to deal with high dimensional data and to limit information loss by providing component-specific lower dimensional representations. We also design an incremental algorithm to compute such representations for large data sets, overcoming hardware limitations of standard methods. Our procedure is illustrated in a magnetic resonance fingerprinting study, where it achieves a 97% dictionary compression for faster and more accurate map reconstructions.

翻译：随着医学数据采集与生产的快速发展，表征医学信息的表达形式日益丰富。然而，此类大规模数据通常难以满足计算资源限制或算法复杂度要求，往往需经压缩或降维处理后才能进行后续分析，但这一过程可能导致信息损失。在本研究中，我们考虑采用特定类型的高斯混合模型（HD-GMM），该模型专为处理高维数据而设计，通过提供分量特定的低维表示来限制信息损失。我们还设计了一种增量算法，用于为大规模数据集计算此类表示，从而克服标准方法的硬件限制。我们的方法在磁共振指纹图谱研究中得到验证，实现了97%的字典压缩率，从而获得更快、更准确的图谱重建结果。