标题： CoverBLIP：用于MR指纹恢复的可扩展迭代匹配滤波 显示英文标题

标题： CoverBLIP: scalable iterative matched filtering for MR Fingerprint recovery

摘要： 当前针对MRF重建问题高维度性的解决方案依赖于一个线性压缩步骤，以减少匹配计算并提高快速但不可扩展的搜索方案（如KD树）的效率。 然而，当应用于非线性数据结构（如可能具有增加的动态复杂性和数据集增长的Bloch响应流形）时，这些方法通常会引入不利的估计精度妥协。 为解决这一不足，我们提出了一种不精确的迭代重建方法，称为Cover BLoch响应迭代投影（CoverBLIP）。 迭代方法提高了其非迭代对应方法的准确性，并且对某些加速的近似更新具有额外的鲁棒性，而不会影响最终的准确性。 利用这些结果，我们使用一种基于Cover树的ANNS算法来加速匹配滤波，该算法具有对抗维度灾难的鲁棒性。 显示英文摘要

摘要： Current proposed solutions for the high dimensionality of the MRF reconstruction problem rely on a linear compression step to reduce the matching computations and boost the efficiency of fast but non-scalable searching schemes such as the KD-trees. However such methodologies often introduce an unfavourable compromise in the estimation accuracy when applied to nonlinear data structures such as the manifold of Bloch responses with possible increased dynamic complexity and growth in data population. To address this shortcoming we propose an inexact iterative reconstruction method, dubbed as the Cover BLoch response Iterative Projection (CoverBLIP). Iterative methods improve the accuracy of their non-iterative counterparts and are additionally robust against certain accelerated approximate updates, without compromising their final accuracy. Leveraging on these results, we accelerate matched-filtering using an ANNS algorithm based on Cover trees with a robustness feature against the curse of dimensionality.