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However, variations in MRI acquisition parameters, such as inversion time (TI) and repetition time (TR), which are common in clinical settings, can compromise the reliability and sensitivity of these measurements. We fine\u2010tuned a deep\u2010learning\u2010based segmentation tool (DL+DiReCT) to reduce its dependence\u00a0to image contrast variations by training it\u00a0on simulated MPRAGE images derived from quantitative relaxation maps. Fine\u2010tuning markedly reduced contrast sensitivity, with the Pearson correlation coefficient decreasing from  to . Evaluation on a synthetic atrophy dataset demonstrated that our model accurately replicated atrophy trends with minimal underestimation, outperforming FreeSurfer and SynthSeg. When applied to a dataset of relapsing\u2013remitting multiple sclerosis (RRMS) patients, the fine\u2010tuned model showed a substantial reduction in contrast sensitivity and maintained stable performance after controlling for covariates such as age, sex, field strength, and Expanded Disability Status Scale (EDSS) score. Overall, the proposed approach achieves robust contrast invariance without sacrificing sensitivity to cortical atrophy, offering a practical improvement for longitudinal and multi\u2010center clinical studies.<\/jats:p>","DOI":"10.1002\/hbm.70560","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T04:21:57Z","timestamp":1780892517000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mitigating the Impact of MR Sequence Parameters: Increasing the Robustness of DL\u2010Based Cortical Thickness Estimates"],"prefix":"10.1002","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9316-3704","authenticated-orcid":false,"given":"Timo","family":"Blattner","sequence":"first","affiliation":[{"name":"Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology University of Bern, Inselspital, Bern University Hospital  Bern 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