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However, poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as \u201cmodalities\u201d here) offer distinct advantages in imaging different parts of the MTL, we developed a multi\u2010modality segmentation model using both 7T and 3T structural MRI to obtain robust segmentation in poor\u2010quality images. MRI modalities including 3T T1\u2010weighted, 3T T2\u2010weighted, 7T T1\u2010weighted and 7T T2\u2010weighted (7T\u2010T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimer's Disease Research Center. Among them, 7T\u2010T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T\u2010T2w. A model derived from nnU\u2010Net took these registered modalities as input and outputted subregion segmentation in 7T\u2010T2w space. 7T\u2010T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi\u2010modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. The multi\u2010modality model delivered good performance regardless of 7T\u2010T2w quality, while the single\u2010modality model under\u2010segmented subregions in poor\u2010quality images. The multi\u2010modality model generally demonstrated stronger discrimination of A\u2009+\u2009MCI versus A\u2010CU. Intra\u2010class correlation and Bland\u2013Altman plots demonstrate that the multi\u2010modality model had higher longitudinal segmentation consistency in all subregions while the single\u2010modality model had low consistency in poor\u2010quality images. The multi\u2010modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.<\/jats:p>","DOI":"10.1002\/hipo.70036","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T12:14:38Z","timestamp":1759839278000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automatic Segmentation of Medial Temporal Lobe Subregions in Multi\u2010Scanner, Multi\u2010Modality Magnetic Resonance Imaging of Variable Quality"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8870-3898","authenticated-orcid":false,"given":"Yue","family":"Li","sequence":"first","affiliation":[{"name":"Penn Image Computing and Science Laboratory, University of Pennsylvania  Philadelphia USA"},{"name":"Department of Radiology University of Pennsylvania  Philadelphia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7184-7028","authenticated-orcid":false,"given":"Long","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Digital Technology and Innovation Siemens Healthineers  Princeton USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pulkit","family":"Khandelwal","sequence":"additional","affiliation":[{"name":"Penn Image Computing and Science Laboratory, University of Pennsylvania  Philadelphia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura E. 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