Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data.
Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data.
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DOI：
10.1002/mrm.28378
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发表时间：
2020-12
影响因子：
3.3
通讯作者：
Akçakaya M
中科院分区：
文献类型：
--
作者：
Yaman B;Hosseini SAH;Moeller S;Ellermann J;Uğurbil K;Akçakaya M
开发一种在没有完全采样数据集数据库的情况下训练物理引导MRI重建神经网络的策略。通过数据欠采样（SSDU）进行的自监督学习用于物理引导的深度学习（DL）重建将可用测量值划分为两个不相交的集合，其中一个用于展开网络中的数据一致性单元，另一个用于定义训练损失。将建议的无完全采样数据的训练与使用地面真实数据的完全监督训练以及使用公开可用的fastMRI膝关节数据库的传统压缩感知和并行成像方法进行比较。相同的物理指导神经网络用于建议的SSDU和监督训练。SSDU训练还应用于不同加速率下的前瞻性2倍加速高分辨率脑数据集，并与并行成像进行比较。在加速率为4的五个不同的膝盖序列的结果表明，所提出的自监督方法与监督学习密切相关，同时显著优于传统的压缩感知和并行成像，其特征在于定量指标和临床读者研究。前瞻性子采样大脑数据集的结果表明，由于缺乏地面真实参考，无法使用监督学习，所提出的自监督方法可以在高加速率下成功地进行重建（4，6和8）。图像读数表明改进的视觉重建质量与所提出的方法相比，并行成像在采集加速。所提出的SSDU方法允许在没有完全采样数据的情况下训练物理引导的DL-MRI重建，同时实现与在完全采样数据上训练的监督DL-MRI相当的结果。
To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully-sampled datasets. Self-supervised learning via data under-sampling (SSDU) for physics-guided deep learning (DL) reconstruction partitions available measurements into two disjoint sets, one of which is used in the data consistency units in the unrolled network and the other is used to define the loss for training. The proposed training without fully-sampled data is compared to fully-supervised training with ground-truth data, as well as conventional compressed sensing and parallel imaging methods using the publicly available fastMRI knee database. The same physics-guided neural network is used for both proposed SSDU and supervised training. The SSDU training is also applied to prospectively 2-fold accelerated high-resolution brain datasets at different acceleration rates, and compared to parallel imaging. Results on five different knee sequences at acceleration rate of 4 shows that proposed self-supervised approach performs closely with supervised learning, while significantly outperforming conventional compressed sensing and parallel imaging, as characterized by quantitative metrics and a clinical reader study. The results on prospectively sub-sampled brain datasets, where supervised learning cannot be employed due to lack of ground-truth reference, show that the proposed self-supervised approach successfully perform reconstruction at high acceleration rates (4, 6 and 8). Image readings indicate improved visual reconstruction quality with the proposed approach compared to parallel imaging at acquisition acceleration. The proposed SSDU approach allows training of physics-guided DL-MRI reconstruction without fully-sampled data, while achieving comparable results with supervised DL-MRI trained on fully-sampled data.
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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
通讯作者：
Knoll F
DOI：
10.1109/jstsp.2020.3003170
发表时间：
2020-10
影响因子：
7.5
作者：
Hosseini SAH;Yaman B;Moeller S;Hong M;Akçakaya M
通讯作者：
Akçakaya M
影响因子：
3.3
作者：
Doneva, Mariya;Boernert, Peter;Mertins, Alfred
通讯作者：
Mertins, Alfred
影响因子：
3.3
作者：
Griswold, MA;Jakob, PM;Haase, A
通讯作者：
Haase, A
影响因子：
3.3
作者：
Akcakaya, Mehmet;Basha, Tamer A.;Goddu, Beth;Goepfert, Lois A.;Kissinger, Kraig V.;Tarokh, Vahid;Manning, Warren J.;Nezafat, Reza
通讯作者：
Nezafat, Reza