{"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/generalized-super-resolution-4d-flow-mri","title":"Generalized super-resolution 4D Flow MRI $\\unicode{x2013}$ using ensemble learning to extend across the cardiovascular system","arxiv_id":"2311.11819","date":"2023-11-20","proceeding":null,"authors":["Leon Ericsson","Adam Hjalmarsson","Muhammad Usman Akbar","Edward Ferdian","Mia Bonini","Brandon Hardy","Jonas Schollenberger","Maria Aristova","Patrick Winter","Nicholas Burris","Alexander Fyrdahl","Andreas Sigfridsson","Susanne Schnell","C. Alberto Figueroa","David Nordsletten","Alistair A. Young","David Marlevi"],"abstract":"4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was to explore the generalizability of SR 4D Flow MRI using a combination of heterogeneous training sets and dedicated ensemble learning. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinical level input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with ensemble learning extending utility across various clinical areas of interest.","url_abs":"https://arxiv.org/abs/2311.11819v2","url_pdf":"https://arxiv.org/pdf/2311.11819v2.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":"generalized-super-resolution-4d-flow-mri","repo_url":"https://github.com/leonericsson/ensemble4dflownet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[{"method_slug":"base","method_name":"BASE"},{"method_slug":"low-resolution-input","method_name":"Low-resolution input"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}