Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI
This work addresses the challenge of faster and more accurate MRI reconstruction for medical imaging applications, though it is incremental as it builds on existing unsupervised learning approaches.
The authors tackled the problem of accelerating 2D radial cine MRI reconstruction by proposing an unsupervised adaptive neural network regularization method (ALONE), which outperformed existing ground truth-free methods like Total Variation and Dictionary Learning in all quantitative measures and significantly sped up the reconstruction process.
In this work, we propose an iterative reconstruction scheme (ALONE - Adaptive Learning Of NEtworks) for 2D radial cine MRI based on ground truth-free unsupervised learning of shallow convolutional neural networks. The network is trained to approximate patches of the current estimate of the solution during the reconstruction. By imposing a shallow network topology and constraining the $L_2$-norm of the learned filters, the network's representation power is limited in order not to be able to recover noise. Therefore, the network can be interpreted to perform a low dimensional approximation of the patches for stabilizing the inversion process. We compare the proposed reconstruction scheme to two ground truth-free reconstruction methods, namely a well known Total Variation (TV) minimization and an unsupervised adaptive Dictionary Learning (DIC) method. The proposed method outperforms both methods with respect to all reported quantitative measures. Further, in contrast to DIC, where the sparse approximation of the patches involves the solution of a complex optimization problem, ALONE only requires a forward pass of all patches through the shallow network and therefore significantly accelerates the reconstruction.