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However, variation due to site and scanner differences can confound analyses, emphasizing the need for harmonization.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To evaluate scanner\u2010related differences in T1w and T2\u2010FLAIR images in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and assess the performance of publicly available image\u2010level harmonization tools.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n Scanner group analysis: 1143 ADNI3 subjects (233 GE, 173 Philips, 250 Siemens, with 487 Siemens subjects used as an independent reference group). Within\u2010subject comparison: paired multi\u2010vendor scan sessions from 8 subjects.<\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n 3.0T, T1w, and T2\u2010FLAIR MRI sequences.<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n Gray\/white matter contrast ratio (G\/W ratio), white matter hyperintensity (WMH) volume, and image feature similarity metrics (Fr\u00e9chet Inception Distance [FID], Learned Perceptual Image Patch Similarity [LPIPS]) were compared across scanner vendors before and after harmonization with statistical (ComBat) and deep learning (HACA3) algorithms.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n One\u2010way ANOVA and post hoc Games\u2010Howell tests were conducted to assess differences between scanner groups across image pipelines (baseline, post\u2010harmonization). Repeated\u2010measures ANOVA and post hoc paired\n t<\/jats:italic>\n \u2010tests with Bonferroni correction were used to evaluate similarity metric changes pre\u2010 and post\u2010harmonization for multi\u2010vendor subjects. We defined statistical significance as\n p<\/jats:italic>\n \u2009<\u20090.05.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n At baseline, significant image differences in G\/W ratio and WMH volumes between vendors were identified. Both ComBat and HACA3 harmonization improved G\/W ratio consistency for T1w and T2\u2010FLAIR imaging across vendors, particularly for GE T2\u2010FLAIRs. HACA3 led to the best similarity between scanner datasets: mean FID T1w\/T2\u2010FLAIR: 10.45\/14.62 (Baseline); 7.45\/11.71 (ComBat); 5.60\/8.91 (HACA3). Only HACA3 harmonization resulted in non\u2010significant differences between vendors for WMH volume.<\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n HACA3 deep learning harmonization outperformed a statistical method, ComBat, improving MR contrast consistency and feature similarity across vendors. However, difficulties in harmonizing T2\u2010FLAIRs highlight limitations in current multi\u2010contrast MR harmonization tools.<\/jats:p>\n <\/jats:sec>\n \n Evidence Level<\/jats:title>\n 3.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 1.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70221","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T11:03:07Z","timestamp":1767610987000},"page":"1257-1269","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Evaluation of Image\u2010Level Harmonization Methods for Multi\u2010Center\n MR<\/scp>\n Neuroimaging"],"prefix":"10.1002","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1202-2613","authenticated-orcid":false,"given":"Brandon C.","family":"Ho","sequence":"first","affiliation":[{"name":"Department of Radiology Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5479-2707","authenticated-orcid":false,"given":"Donghoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiology Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashwin","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Radiology Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Skylar","family":"Weiss","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurological Sciences Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hillary","family":"Vossler","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurological Sciences Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Mormino","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurological Sciences Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5781-8848","authenticated-orcid":false,"given":"Greg","family":"Zaharchuk","sequence":"additional","affiliation":[{"name":"Department of Radiology Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the Alzheimer's Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181cb3e25"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0579-z"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-024-01280-6"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2023.102285"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120125"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000001192"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxj037"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.08.047"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.11.024"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2019.05.041"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118569"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2046908"},{"key":"e_1_2_11_14_1","unstructured":"B. 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