Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning.
Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning.
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DOI：
10.1016/j.neuroimage.2022.119248
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发表时间：
2022-08-01
期刊：
影响因子：
5.7
通讯作者：
Akcakaya, Mehmet
中科院分区：
文献类型：
--
作者：
Zhang, Chi;Moeller, Steen;Demirel, Omer Burak;Ugurbil, Kamil;Akcakaya, Mehmet
并行成像是临床上最常用的磁共振成像（MRI）加速技术，部分原因是其易于纳入常规采集。在基于k空间的并行成像重建中，使用线性卷积对子采样的k空间数据进行内插。在高加速率下，这些方法具有固有的噪声放大和降低的图像质量。另一方面，非线性深度学习方法在高加速度下提供了更好的图像质量，但不同扫描的训练数据库的可用性及其可解释性阻碍了它们的适应性。在这项工作中，我们提出了k空间插值（RAKI）的鲁棒神经网络的扩展，称为残差RAKI（rRAKI），它使用混合线性和非线性方法实现了特定于扫描的机器学习重建。在rRAKI中，非线性CNN与通过跳过连接实现的线性卷积联合训练。实际上，线性部分提供了基线重建，而并行运行的非线性CNN进一步减少了线性部分产生的伪影和噪声。与纯非线性方法相比，重建的线性和非线性方面之间的明确划分也有助于提高可解释性。在公开可用的fastMRI数据集以及高分辨率解剖成像上进行了实验，比较了GRAPPA及其变体、压缩感知、RAKI、K空间中的扫描特异性降低（SPARK）和拟议的rRAKI。此外，还进行了高加速同步多层（SMS）功能性MRI重建，其中将申报的rRAKI与读出SENSE-GRAPPA和RAKI进行了比较。我们的研究结果表明，所提出的rRAKI方法大大提高了图像质量相比，传统的并行成像，并提供更清晰的图像相比，SPARK和SPAR 1-SPIRiT。此外，rRAKI显示出改进的保存随时间变化的动态相比，并行成像和RAKI在高度加速的SMS功能磁共振成像。
Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are interpolated using linear convolutions. At high acceleration rates these methods have inherent noise amplification and reduced image quality. On the other hand, non-linear deep learning methods provide improved image quality at high acceleration, but the availability of training databases for different scans, as well as their interpretability hinder their adaptation. In this work, we present an extension of Robust Artificial-neural-networks for k-space Interpolation (RAKI), called residual-RAKI (rRAKI), which achieves scan-specific machine learning reconstruction using a hybrid linear and non-linear methodology. In rRAKI, non-linear CNNs are trained jointly with a linear convolution implemented via a skip connection. In effect, the linear part provides a baseline reconstruction, while the non-linear CNN that runs in parallel provides further reduction of artifacts and noise arising from the linear part. The explicit split between the linear and non-linear aspects of the reconstruction also help improve interpretability compared to purely non-linear methods. Experiments were conducted on the publicly available fastMRI datasets, as well as high-resolution anatomical imaging, comparing GRAPPA and its variants, compressed sensing, RAKI, Scan Specific Artifact Reduction in K-space (SPARK) and the proposed rRAKI. Additionally, highly-accelerated simultaneous multi-slice (SMS) functional MRI reconstructions were also performed, where the proposed rRAKI was compred to Read-out SENSE-GRAPPA and RAKI. Our results show that the proposed rRAKI method substantially improves the image quality compared to conventional parallel imaging, and offers sharper images compared to SPARK and ℓ1-SPIRiT. Furthermore, rRAKI shows improved preservation of time-varying dynamics compared to both parallel imaging and RAKI in highly-accelerated SMS fMRI.
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影响因子：
3.3
作者：
Hammernik K;Klatzer T;Kobler E;Recht MP;Sodickson DK;Pock T;Knoll F
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DOI：
10.1109/jstsp.2020.3003170
发表时间：
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影响因子：
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