{"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/efficient-b-mode-ultrasound-image","title":"Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning","arxiv_id":"1712.06096","date":"2017-12-17","proceeding":null,"authors":["Yeo Hun Yoon","Shujaat Khan","Jaeyoung Huh","Jong Chul Ye"],"abstract":"In portable, three dimensional, and ultra-fast ultrasound imaging systems,\nthere is an increasing demand for the reconstruction of high quality images\nfrom a limited number of radio-frequency (RF) measurements due to receiver (Rx)\nor transmit (Xmit) event sub-sampling. However, due to the presence of side\nlobe artifacts from RF sub-sampling, the standard beamformer often produces\nblurry images with less contrast, which are unsuitable for diagnostic purposes.\nExisting compressed sensing approaches often require either hardware changes or\ncomputationally expensive algorithms, but their quality improvements are\nlimited. To address this problem, here we propose a novel deep learning\napproach that directly interpolates the missing RF data by utilizing redundancy\nin the Rx-Xmit plane. Our extensive experimental results using sub-sampled RF\ndata from a multi-line acquisition B-mode system confirm that the proposed\nmethod can effectively reduce the data rate without sacrificing image quality.","url_abs":"http://arxiv.org/abs/1712.06096v3","url_pdf":"http://arxiv.org/pdf/1712.06096v3.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":"efficient-b-mode-ultrasound-image","repo_url":"https://github.com/BISPL-JYH/Ultrasound_TMI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"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}