Abstract
Reconstructing under-sampled k-space measurements in Compressed Sensing MRI (CS-MRI) is classically solved by minimizing a regularized least-squares cost function. In the absence of fully-sampled training data, this optimization approach can still be amortized via a neural network that minimizes the cost function over a dataset of under-sampled measurements. Here, a crucial design choice is the regularization function(s) and corresponding weight(s). In this paper, we introduce HyperRecon – a novel strategy of using a hypernetwork to generate the parameters of a main reconstruction network as a function of the regularization weight(s), resulting in a regularization-agnostic reconstruction model. At test time, for a given under-sampled image, our model can rapidly compute reconstructions with different amounts of regularization. We propose and empirically demonstrate an efficient and data-driven way of maximizing reconstruction performance given limited hypernetwork capacity. Our code will be made publicly available upon acceptance.
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Notes
- 1.
In this paper, we assume a single coil acquisition.
- 2.
For baselines, the \(18 \times 18\) grid was linearly interpolated to \(100 \times 100\) to match the hypernetwork landscapes.
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Acknowledgements
This work was supported by NIH grants R01LM012719 (MS), R01AG053949 (MS), 1R01AG064027 (AD), the NSF NeuroNex grant 1707312 (MS), and the NSF CAREER 1748377 grant (MS).
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Wang, A.Q., Dalca, A.V., Sabuncu, M.R. (2021). HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks. In: Haq, N., Johnson, P., Maier, A., Würfl, T., Yoo, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2021. Lecture Notes in Computer Science(), vol 12964. Springer, Cham. https://doi.org/10.1007/978-3-030-88552-6_1
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