Aggarwal Hemant Kumar, Mani Merry P, Jacob Mathews
University of Iowa, Iowa, USA.
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:671-674. doi: 10.1109/isbi.2018.8363663. Epub 2018 May 24.
We introduce a model-based image reconstruction framework, where we use a deep convolution neural network (CNN) based regularization prior. We rely on a recursive algorithm, which alternates between a CNN based denoising step and enforcement of data consistency. Unrolling the recursive algorithm yields a deep network that is trained using backpropagation. The unique aspect of this method is the use of the same CNN weights at each iteration, which makes the resulting structure consistent with the model-based formulation. Also, this approach reduces the number of trainable parameters, which hence lower the amount of training data needed. The use of a forward model also reduces the size of the network and enables the exploitation additional prior information available from calibration data. The use of the framework for multichannel MRI reconstruction provides improved reconstructions, compared to other state-of-the-art methods.
我们引入了一个基于模型的图像重建框架，其中我们使用基于深度卷积神经网络（CNN）的正则化先验。我们依赖于一种递归算法，该算法在基于CNN的去噪步骤和数据一致性的强制执行之间交替。展开递归算法会产生一个使用反向传播训练的深度网络。该方法的独特之处在于在每次迭代中使用相同的CNN权重，这使得所得结构与基于模型的公式一致。此外，这种方法减少了可训练参数的数量，从而减少了所需的训练数据量。前向模型的使用还减小了网络的大小，并能够利用从校准数据中获得的额外先验信息。与其他现有技术方法相比，将该框架用于多通道MRI重建可提供更好的重建效果。
