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They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging software tools for feature extraction are typically applied only to images without gadolinium. The objective of this work is to evaluate how image translation can be useful to exploit a highly heterogeneous data set containing both contrast-enhanced and non-contrast-enhanced images from a clinical data warehouse.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We propose and compare different 3D U-Net and conditional GAN models to convert contrast-enhanced T1-weighted (T1ce) into non-contrast-enhanced (T1nce) brain MRI. These models were trained using 230 image pairs and tested on 77 image pairs from the clinical data warehouse of the Greater Paris area.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Validation using standard image similarity measures demonstrated that the similarity between real and synthetic T1nce images was higher than between real T1nce and T1ce images for all the models compared. The best performing models were further validated on a segmentation task. We showed that tissue volumes extracted from synthetic T1nce images were closer to those of real T1nce images than volumes extracted from T1ce images.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>We showed that deep learning models initially developed with research quality data could synthesize T1nce from T1ce images of clinical quality and that reliable features could be extracted from the synthetic images, thus demonstrating the ability of such methods to help exploit a data set coming from a clinical data warehouse.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-024-01242-3","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T02:01:41Z","timestamp":1710900101000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI"],"prefix":"10.1186","volume":"24","author":[{"given":"Simona","family":"Bottani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elina","family":"Thibeau-Sutre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aur\u00e9lien","family":"Maire","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Str\u00f6er","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Didier","family":"Dormont","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Colliot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ninon","family":"Burgos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"APPRIMAGE\u00a0Study\u00a0Group","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"issue":"1","key":"1242_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2342-8-9","volume":"8","author":"RA Heckemann","year":"2008","unstructured":"Heckemann RA, Hammers A, Rueckert D, Aviv RI, Harvey CJ, Hajnal JV. 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The MR-004 reference ()\u00a0controls data processing for the purpose of studying, evaluating and\/or researching that does not involve human persons (in the sense of not involving an intervention or a prospective collection of research data in patients that would not be necessary for clinical evaluation, but which allows retrospective use of data previously acquired in patients). The goals of the clinical data warehouse are the development of decision support algorithms, the support of clinical trials and the promotion of multi-center studies. According to French regulation (i.e., the MR-004), and as authorized by the CNIL, patients\u2019 consent to use their data in the projects of the CDW can be waived as these data were acquired as part of the clinical routine care of the patients. At the same time, AP-HP committed to keep patients updated about the different research projects of the clinical data warehouse through a portal on the internet ()\u00a0and individual information is systematically provided to all the patients admitted to the AP-HP. In addition, a retrospective information campaign was conducted by the AP-HP in 2017: it involved around 500,000 patients who were contacted by e-mail and by postal mail to be informed of the development of the CDW. The project on which the proposed work is based is called APPRIMAGE, it is led by the ARAMIS team (current AP-HP PI: Didier Dormont; initial AP-HP PI: Anne Bertrand, deceased March 2nd 2018) at the Paris Brain Institute and it was approved by the Scientific and Ethics Board of the AP-HP (IRB00011591) in 2018 []. All methods were performed in accordance with the relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"67"}}