Magnetic Resonance Fingerprinting (MRF) is an emerging technology with the potential to revolutionize radiology and medical diagnostics. In comparison to traditional magnetic resonance imaging (MRI), MRF enables the rapid, simultaneous, non-invasive acquisition and reconstruction of multiple tissue parameters, paving the way for novel diagnostic techniques. In the original matching approach, reconstruction is based on the search for the best matches between in vivo acquired signals and a dictionary of high-dimensional simulated signals (fingerprints) with known tissue properties. A critical and limiting challenge is that the size of the simulated dictionary increases exponentially with the number of parameters, leading to an extremely costly subsequent matching. In this work, we propose to address this scalability issue by considering probabilistic mixtures of high-dimensional elliptical distributions, to learn more efficient dictionary representations. Mixture components are modelled as flexible ellipitic shapes in low dimensional subspaces. They are exploited to cluster similar signals and reduce their dimension locally cluster-wise to limit information loss. To estimate such a mixture model, we provide a new incremental algorithm capable of handling large numbers of signals, allowing us to go far beyond the hardware limitations encountered by standard implementations. We demonstrate, on simulated and real data, that our method effectively manages large volumes of MRF data with maintained accuracy. It offers a more efficient solution for accurate tissue characterization and significantly reduces the computational burden, making the clinical application of MRF more practical and accessible.

翻译：磁共振指纹识别（MRF）是一项新兴技术，具有革新放射学与医学诊断的潜力。与传统磁共振成像（MRI）相比，MRF能够实现对多种组织参数的快速、同步、无创采集与重建，为新型诊断技术开辟了道路。在原始的匹配方法中，重建基于在体采集信号与已知组织特性的高维模拟信号（指纹）字典间的最佳匹配搜索。一个关键且具有限制性的挑战在于：模拟字典的规模随参数数量呈指数级增长，导致后续匹配过程计算代价极高。本研究通过采用高维椭圆分布的概率混合模型来学习更高效的字典表示，以解决这一可扩展性问题。混合分量被建模为低维子空间中的灵活椭圆形态，用于聚类相似信号并在局部聚类层面降低其维度以限制信息损失。为估计此类混合模型，我们提出一种能够处理海量信号的新型增量算法，该算法可突破标准实现方案遇到的硬件限制。我们在模拟数据与真实数据上证明，本方法在保持精度的前提下能有效处理大规模MRF数据，为精确组织表征提供了更高效的解决方案，显著降低了计算负担，使MRF的临床应用更具实用性与可及性。