磁共振指纹的深度学习几何
Geometry of Deep Learning for Magnetic Resonance Fingerprinting
摘要：
当前流行的磁共振指纹（MRF）恢复方法受到字典匹配（DM）步骤的繁重存储和计算要求的挑战，这是由于在多参数定量MRI应用中指纹字典的大小和复杂性不断增长。在本文中，我们研究了一种深度学习方法来解决这些缺点。再加上降维的第一层，所提出的MRF-Net能够通过节省DM基线所需的60多次内存和计算来重建定量地图。细网格流形枚举，即MRF字典仅用于训练网络，而不是在图像重建期间。我们表明，MRF-Net提供了Bloch响应流形投影的分段仿射近似，而不是记忆字典，网络有效地聚类这个流形，并学习一组分层匹配滤波器，用于仿射回归每个段中的NMR特征。
英文摘要：
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a dictionary-matching (DM) step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications. In this paper we study a deep learning approach to address these shortcomings. Coupled with a dimensionality reduction first layer, the proposed MRF-Net is able to reconstruct quantitative maps by saving more than 60 times in memory and computations required for a DM baseline. Fine-grid manifold enumeration i.e. the MRF dictionary is only used for training the network and not during image reconstruction. We show that the MRF-Net provides a piece-wise affine approximation to the Bloch response manifold projection and that rather than memorizing the dictionary, the network efficiently clusters this manifold and learns a set of hierarchical matched-filters for affine regression of the NMR characteristics in each segment.
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