{"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/a-deep-cascade-of-convolutional-neural-1","title":"A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction","arxiv_id":"1703.00555","date":"2017-03-01","proceeding":null,"authors":["Jo Schlemper","Jose Caballero","Joseph V. Hajnal","Anthony Price","Daniel Rueckert"],"abstract":"The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.\nInspired by recent advances in deep learning, we propose a framework for\nreconstructing MR images from undersampled data using a deep cascade of\nconvolutional neural networks to accelerate the data acquisition process. We\nshow that for Cartesian undersampling of 2D cardiac MR images, the proposed\nmethod outperforms the state-of-the-art compressed sensing approaches, such as\ndictionary learning-based MRI (DLMRI) reconstruction, in terms of\nreconstruction error, perceptual quality and reconstruction speed for both\n3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the\nmethod proposed is approximately twice as small, allowing to preserve\nanatomical structures more faithfully. Using our method, each image can be\nreconstructed in 23 ms, which is fast enough to enable real-time applications.","url_abs":"http://arxiv.org/abs/1703.00555v1","url_pdf":"http://arxiv.org/pdf/1703.00555v1.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":"a-deep-cascade-of-convolutional-neural-1","repo_url":"https://github.com/cjandrioli/Deep-MRI-Reconstruction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"a-deep-cascade-of-convolutional-neural-1","repo_url":"https://github.com/js3611/Deep-MRI-Reconstruction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"a-deep-cascade-of-convolutional-neural-1","repo_url":"https://github.com/myyaqubpython/https-github.com-cq615-Deep-MRI-Reconstruction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"a-deep-cascade-of-convolutional-neural-1","repo_url":"https://github.com/sainzmac/Deep-MRI-Reconstruction-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"dictionary-learning","task_name":"Dictionary Learning"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1703.00555","atlas_url":"https://app.syntology.ai/?focus=1703.00555","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}