{"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/universal-deep-beamformer-for-variable-rate","title":"Universal Deep Beamformer for Variable Rate Ultrasound Imaging","arxiv_id":"1901.01706","date":"2019-01-07","proceeding":null,"authors":["Shujaat Khan","Jaeyoung Huh","Jong Chul Ye"],"abstract":"Ultrasound (US) imaging is based on the time-reversal principle, in which\nindividual channel RF measurements are back-propagated and accumulated to form\nan image after applying specific delays. While this time reversal is usually\nimplemented as a delay-and-sum (DAS) beamformer, the image quality quickly\ndegrades as the number of measurement channels decreases. To address this\nproblem, various types of adaptive beamforming techniques have been proposed\nusing predefined models of the signals. However, the performance of these\nadaptive beamforming approaches degrade when the underlying model is not\nsufficiently accurate. Here, we demonstrate for the first time that a single\nuniversal deep beamformer trained using a purely data-driven way can generate\nsignificantly improved images over widely varying aperture and channel\nsubsampling patterns. In particular, we design an end-to-end deep learning\nframework that can directly process sub-sampled RF data acquired at different\nsubsampling rate and detector configuration to generate high quality ultrasound\nimages using a single beamformer. Experimental results using B-mode focused\nultrasound confirm the efficacy of the proposed methods.","url_abs":"http://arxiv.org/abs/1901.01706v1","url_pdf":"http://arxiv.org/pdf/1901.01706v1.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":"universal-deep-beamformer-for-variable-rate","repo_url":"https://github.com/Shujaat123/Universal-Deep-Beamformer-for-Robust-Ultrasound-Imaging","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}