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Quantitative MRI (qMRI) provides tissue-specific parameters and holds potential for capturing disease-related biomarkers. However, conventional analysis methods often rely on single-modality imaging or mean features, constraining their ability to capture HD's complex microstructural evolution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To assess the feasibility of multi-modal MRI combined with the MOLED sequence in HD patients and explore its value in early disease detection and staging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>22 HD patients (14 Pre-HD and 8\u2005M-HD) and 27 healthy controls were enrolled. MOLED-derived T2 and T2* maps, along with structural MRI, were acquired using two 3.0\u2005T scanners to assess inter-scanner consistency. The MOLED sequence incorporates ultrafast acquisition techniques to minimize motion artifacts and improve image quality. Histogram-based features (e.g., variance, skewness, and maximum) and volumes were extracted from eight deep brain regions. Multiple machine learning models were employed for classification analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The MOLED demonstrated good image consistency and reproducibility across scanners. Significant group differences were observed in the volumes of several basal ganglia regions and in variance-based features across multiple modalities. Machine learning models combining clinical and mapping features achieved the highest classification performance (maximum F1-macro = 0.846, Sensitivity-macro = 0.838).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>MOLED provides stable and complementary quantitative information for multi-modal MRI. Integrating multimodal multi-feature with machine learning enables a more comprehensive depiction of HD-related microstructural heterogeneity and disease progression.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/18796397251411608","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T18:17:56Z","timestamp":1767896276000},"page":"361-374","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Multimodal MRI integrating anti-motion multi-parametric mappings for investigating subcortical nuclei microstructural alterations in Huntington's disease"],"prefix":"10.1177","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2940-1452","authenticated-orcid":false,"given":"Mengying","family":"Zhu","sequence":"first","affiliation":[{"name":"Xiamen 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