{"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/motion-compensated-dynamic-mri-reconstruction","title":"Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow Estimation","arxiv_id":"1707.07089","date":"2017-07-22","proceeding":null,"authors":["Ningning Zhao","Daniel O'Connor","Adrian Basarab","Dan Ruan","Peng Hu","Ke Sheng"],"abstract":"This paper proposes a novel framework to reconstruct the dynamic magnetic\nresonance images (DMRI) with motion compensation (MC). Due to the inherent\nmotion effects during DMRI acquisition, reconstruction of DMRI using motion\nestimation/compensation (ME/MC) has been studied under a compressed sensing\n(CS) scheme. In this paper, by embedding the intensity-based optical flow (OF)\nconstraint into the traditional CS scheme, we are able to couple the DMRI\nreconstruction with motion field estimation. The formulated optimization\nproblem is solved by a primal-dual algorithm with linesearch due to its\nefficiency when dealing with non-differentiable problems. With the estimated\nmotion field, the DMRI reconstruction is refined through MC. By employing the\nmulti-scale coarse-to-fine strategy, we are able to update the\nvariables(temporal image sequences and motion vectors) and to refine the image\nreconstruction alternately. Moreover, the proposed framework is capable of\nhandling a wide class of prior information (regularizations) for DMRI\nreconstruction, such as sparsity, low rank and total variation. Experiments on\nvarious DMRI data, ranging from in vivo lung to cardiac dataset, validate the\nreconstruction quality improvement using the proposed scheme in comparison to\nseveral state-of-the-art algorithms.","url_abs":"http://arxiv.org/abs/1707.07089v3","url_pdf":"http://arxiv.org/pdf/1707.07089v3.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":"motion-compensated-dynamic-mri-reconstruction","repo_url":"https://github.com/ning22/Motion-Compensated-Dynamic-MRI-Reconstruction-with-Local-Affine-Optical-Flow-Estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"mri-reconstruction","task_name":"MRI Reconstruction"},{"task_slug":"motion-compensation","task_name":"Motion Compensation"},{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}