Multidimensional relaxation-diffusion correlation (REDCO) NMR is an assumption-free method that measures how water is distributed within materials. Although highly informative, REDCO had never been used in clinical MRI applications because of the large amount of data it requires, leading to infeasible scan times. A recently suggested novel experimental design and processing framework, marginal distributions constrained optimization (MADCO), was used here to accelerate and improve the reconstruction of such MRI correlations. MADCO uses the 1D marginal distributions as a priori information, which provide powerful constraints when 2D spectra are reconstructed, while their estimation requires an order of magnitude less data than conventional 2D approaches. In this work we experimentally examined the impact the complexity of the correlation distribution has on the accuracy and robustness of the estimates. MADCO and a conventional method were compared using two $T_1\mathrm{–}D$ phantoms that differ in the proximity of their peaks, leading to a relatively simple case as opposed to a more challenging one. The phantoms were used to vet the achievable data compression using MADCO under these conditions. MADCO required $\sim$43 and $\sim$30 less data than the conventional approach for the simple and complex spectra, respectively, making it potentially feasible for preclinical and even clinical applications.

多维弛豫扩散相关 (REDCO) NMR 是一种无假设方法，用于测量水在材料内的分布情况。尽管 REDCO 信息丰富，但从未用于临床 MRI 应用，因为它需要大量数据，导致扫描时间不可行。最近提出的一种新颖的实验设计和处理框架，即边缘分布约束优化（MADCO），用于加速和改进此类 MRI 相关性的重建。MADCO 使用一维边缘分布作为先验信息，在重建二维光谱时提供强大的约束，而其估计所需的数据比传统的二维方法少一个数量级。在这项工作中，我们通过实验检验了相关分布的复杂性对估计的准确性和鲁棒性的影响。使用两种方法对 MADCO 和传统方法进行了比较${\mathrm{时间}}_1\mathrm{–}D$模型的峰值接近程度不同，从而导致相对简单的情况，而不是更具挑战性的情况。这些模型用于审查在这些条件下使用 MADCO 可实现的数据压缩。需要 MADCO$～$43 和$～$对于简单和复杂的光谱，其数据分别比传统方法少 30 个，这使得它对于临床前甚至临床应用具有潜在的可行性。