{"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/medical-image-imputation-from-image","title":"Medical Image Imputation from Image Collections","arxiv_id":"1808.05732","date":"2018-08-17","proceeding":null,"authors":["Adrian V. Dalca","Katherine L. Bouman","William T. Freeman","Natalia S. Rost","Mert R. Sabuncu","Polina Golland"],"abstract":"We present an algorithm for creating high resolution anatomically plausible\nimages consistent with acquired clinical brain MRI scans with large inter-slice\nspacing. Although large data sets of clinical images contain a wealth of\ninformation, time constraints during acquisition result in sparse scans that\nfail to capture much of the anatomy. These characteristics often render\ncomputational analysis impractical as many image analysis algorithms tend to\nfail when applied to such images. Highly specialized algorithms that explicitly\nhandle sparse slice spacing do not generalize well across problem domains. In\ncontrast, we aim to enable application of existing algorithms that were\noriginally developed for high resolution research scans to significantly\nundersampled scans. We introduce a generative model that captures fine-scale\nanatomical structure across subjects in clinical image collections and derive\nan algorithm for filling in the missing data in scans with large inter-slice\nspacing. Our experimental results demonstrate that the resulting method\noutperforms state-of-the-art upsampling super-resolution techniques, and\npromises to facilitate subsequent analysis not previously possible with scans\nof this quality. Our implementation is freely available at\nhttps://github.com/adalca/papago .","url_abs":"http://arxiv.org/abs/1808.05732v1","url_pdf":"http://arxiv.org/pdf/1808.05732v1.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":"medical-image-imputation-from-image","repo_url":"https://github.com/adalca/papago","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"medical-image-imputation-from-image","repo_url":"https://github.com/adalca/patchlib","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anatomy","task_name":"Anatomy"},{"task_slug":"image-imputation","task_name":"Image Imputation"},{"task_slug":"imputation","task_name":"Imputation"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.05732","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}