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Most current approaches to undersampled multi-coil MRI reconstruction focus on learning the reconstruction model for a fixed, equidistant acquisition trajectory. In this paper, we study the problem of joint learning of the reconstruction model together with acquisition policies. To this end, we extend the End-to-End Variational Network with learnable acquisition policies that can adapt to different data points. We validate our model on a coil-compressed version of the large scale undersampled multi-coil \fastMRI dataset using two undersampling factors: $4\times$ and $8\times$. Our experiments show that we are able to outperform the learnable non-adaptive and handcrafted equidistant strategies for both acceleration factors, with an observed improvement up to $\sim 3\%$ in SSIM, suggesting that potentially-adaptive $k$-space acquisition trajectories can improve reconstructed image quality for larger acceleration factors. However, and perhaps surprisingly, our best performing policies learn to be explicitly non-adaptive.
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Thursday 7th July
Poster Session 2.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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