Content-aware compressive magnetic resonance image reconstruction.
Content-aware compressive magnetic resonance image reconstruction.
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DOI：
10.1016/j.mri.2018.06.008
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发表时间：
2018-10
影响因子：
2.5
通讯作者：
Meyer CH
中科院分区：
文献类型：
--
作者：
Weller DS;Salerno M;Meyer CH
本文描述了一种自适应的方法来正则化磁共振成像中基于模型的重建，以考虑局部结构或图像内容。结合小波和总变差稀疏性等常见模型，这种内容感知正则化避免了过度平滑或损害图像特征，同时抑制了加速成像的噪声和不相干混叠。为了评估这种正则化方法，实验从单通道和多通道，笛卡尔和非笛卡尔，大脑和心脏数据重建图像。这些重建结合了联合收割机常见的分析形式正则化器和自动校准并行成像（如适用）。在大多数情况下，结果表明，在结构相似性和峰值信号误差比相对于完全采样图像的广泛改善。这些结果表明，这种内容感知正则化可以保留局部图像结构，如边缘，同时提供优于稀疏促进或稀疏重加权正则化的去噪能力上级。
This paper describes an adaptive approach to regularizing model-based reconstructions in magnetic resonance imaging to account for local structure or image content. In conjunction with common models like wavelet and total variation sparsity, this content-aware regularization avoids oversmoothing or compromising image features while suppressing noise and incoherent aliasing from accelerated imaging. To evaluate this regularization approach, the experiments reconstruct images from single- and multi-channel, Cartesian and non-Cartesian, brain and cardiac data. These reconstructions combine common analysis-form regularizers and autocalibrating parallel imaging (when applicable). In most cases, the results show widespread improvement in structural similarity and peak-signal-to-error ratio relative to the fully sampled images. These results suggest that this content-aware regularization can preserve local image structures such as edges while providing denoising power superior to sparsity-promoting or sparsity-reweighted regularization.