Abstract
Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing under-sampled MR images, offering the potential to reduce scan times. Classical techniques minimize a regularized least-squares cost function using an expensive iterative optimization procedure. Recently, deep learning models have been developed that model the iterative nature of classical techniques by unrolling iterations in a neural network. While exhibiting superior performance, these methods require large quantities of ground-truth images and have shown to be non-robust to unseen data. In this paper, we explore a novel strategy to train an unrolled reconstruction network in an unsupervised fashion by adopting a loss function widely-used in classical optimization schemes. We demonstrate that this strategy achieves lower loss and is computationally cheap compared to classical optimization solvers while also exhibiting superior robustness compared to supervised models. Code is available at https://github.com/alanqrwang/HQSNet.
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Notes
- 1.
In this paper, we assume a single coil acquisition.
- 2.
For forward models that do not permit an analytical solution of Eq. (7b) (e.g. multi-coil MRI), one can replace the data-consistency layer with an iterative optimization scheme (e.g. conjugate gradient as in [1]). In addition, the iteration-specific weights \(\theta _k\) in Eq. (7a) can be replaced by a shared set of weights \(\theta \), which enforces the model to learn a global regularization prior for all iterations.
- 3.
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Acknowledgements
This research was funded by NIH grants R01LM012719, R01AG053949; and, NSF CAREER 1748377, and NSF NeuroNex Grant1707312.
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Wang, A.Q., Dalca, A.V., Sabuncu, M.R. (2020). Neural Network-Based Reconstruction in Compressed Sensing MRI Without Fully-Sampled Training Data. In: Deeba, F., Johnson, P., Würfl, T., Ye, J.C. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2020. Lecture Notes in Computer Science(), vol 12450. Springer, Cham. https://doi.org/10.1007/978-3-030-61598-7_3
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