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首页> 外文会议>Asilomar Conference on Signals, Systems and Computers >Physics-driven deep training of dictionary-based algorithms for MR image reconstruction
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Physics-driven deep training of dictionary-based algorithms for MR image reconstruction

机译：物理驱动的基于字典的MR图像重建算法的深度训练

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摘要

Techniques involving learned dictionaries can outperform conventional approaches involving (nontrained) analytical sparsifying models for MR image reconstruction. Inspired by iterative dictionary learning-based reconstruction methods, we propose a novel efficient image reconstruction framework involving multiple iterations (or layers). Each layer involves applying a transformation to image patches, thresholding, and then reconstructing the patches in a dictionary, followed by an update of the image using observed k-space measurements. We train the transforms, thresholds, and dictionaries within the multi-layer algorithm to minimize reconstruction errors. Our experiments demonstrate that for highly undersampled k-space data, such trained reconstruction algorithms provide high quality results.
机译：涉及学到的词典的技术可以胜过涉及MR图像重建的（非训练性的）分析稀疏模型的传统方法。受基于迭代字典学习的重建方法的启发，我们提出了一种新颖的高效图像重建框架，该框架涉及多个迭代（或层次）。每一层都涉及对图像斑块进行变换，阈值化，然后在字典中重构斑块，然后使用观察到的k空间测量值对图像进行更新。我们在多层算法中训练变换，阈值和字典，以最大程度地减少重构误差。我们的实验表明，对于高度欠采样的k空间数据，此类经过训练的重构算法可提供高质量的结果。

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