{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/on-learning-adaptive-acquisition-policies-for","title":"On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction","arxiv_id":"2203.16392","date":"2022-03-30","proceeding":null,"authors":["Tim Bakker","Matthew Muckley","Adriana Romero-Soriano","Michal Drozdzal","Luis Pineda"],"abstract":"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 on-par performance with the learnable non-adaptive and handcrafted equidistant strategies at $4\\times$, and an observed improvement of more than $2\\%$ in SSIM at $8\\times$ acceleration, 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.","url_abs":"https://arxiv.org/abs/2203.16392v1","url_pdf":"https://arxiv.org/pdf/2203.16392v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"on-learning-adaptive-acquisition-policies-for","repo_url":"https://github.com/facebookresearch/fastMRI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"mri-reconstruction","task_name":"MRI Reconstruction"},{"task_slug":"ssim","task_name":"SSIM"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}