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This review synthesises current data on late gadolinium enhancement (LGE), parametric mapping, strain imaging and artificial intelligence (AI), and contextualises these among emerging genetic risk stratification approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Recent Findings<\/jats:title>\n                    <jats:p>Left ventricular ejection fraction has been the cornerstone for guideline-based arrhythmic risk stratification in patients with DCM, despite limited precision. Observational data and meta-analyses have established the presence of replacement fibrosis through LGE-CMR as a powerful independent predictor of ventricular arrhythmias (VAs) and sudden cardiac death. Crucially, the absence of LGE identifies a low-risk cohort. Much recent focus has been on understanding whether more precise characterisation of scar can further enhance arrhythmic risk stratification, for example through scar quantification. Although data exists supporting a potential role for T1 mapping for arrhythmic risk prediction in this cohort, further research is required to support its widespread adoption. Emerging multimodal approaches using artificial intelligence and personalised computational \u201cdigital twins\u201d are areas of important interest, offering scope for integration of imaging, electrocardiographic and genomic data layers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Arrhythmic risk stratification in non-ischaemic cardiomyopathies remains challenging despite significant advances. Progressive refinement in precision has accompanied a shift from a restrictive, functional assessment towards an integrated, substrate-driven precision model. CMR represents the cornerstone of this evolution.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11886-026-02435-8","type":"journal-article","created":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T03:55:54Z","timestamp":1792727754000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Arrhythmic Risk Stratification in Non-Ischaemic Cardiomyopathy: The Role of Cardiac Magnetic Resonance Imaging"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3576-1198","authenticated-orcid":false,"given":"Ryan I.","family":"Sia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1164-5993","authenticated-orcid":false,"given":"Daniel J.","family":"Hammersley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,23]]},"reference":[{"key":"2435_CR1","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1111\/joim.12944","volume":"286","author":"D Reichart","year":"2019","unstructured":"Reichart D, Magnussen C, Zeller T, Blankenberg S. 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