{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:43:09Z","timestamp":1776076989196,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:00:00Z","timestamp":1775865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: This narrative review introduces the \u201cFrom Plaque to Perfusion\u201d framework, a clinically pragmatic approach that maps multimodality imaging technologies to critical decision points in the acute coronary syndrome (ACS) patient journey. By integrating non-invasive assessment, invasive procedural guidance, and post-event tissue characterisation, this framework provides a structured pathway for deep phenotyping of ACS. Artificial intelligence (AI) is highlighted as an essential enabling layer that enhances diagnostic precision, automates quantification, and supports scalable, data-driven care. Contemporary ACS management pathways, while effective, often leave residual clinical uncertainty. The diagnostic objective has evolved beyond confirming myocardial injury to comprehensively phenotyping the entire ACS cascade: defining the plaque substrate, identifying the culprit mechanism, and quantifying the myocardial consequence. This requires a systematic integration of advanced imaging modalities. Methods: This narrative review is based on a comprehensive literature search of major medical databases (PubMed\/MEDLINE, Scopus, Embase, Google Scholar) for high-level evidence, including randomized controlled trials, meta-analyses, and international expert consensus documents published between January 2010 and February 2026. Results: The \u201cFrom Plaque to Perfusion\u201d framework consists of three core stages. First, non-invasive assessment with coronary computed tomography angiography (CCTA), fractional flow reserve (FFR-CT), and PET-CT defines plaque substrate and vascular inflammation. Second, invasive precision in the catheterization laboratory, guided by optical coherence tomography (OCT) and intravascular ultrasound (IVUS), resolves the culprit mechanism and optimizes percutaneous coronary intervention (PCI). Third, post-event tissue characterization with cardiac magnetic resonance (CMR) quantifies myocardial injury and refines prognosis. AI-driven platforms are shown to enhance each stage by automating analysis, standardizing interpretation, and providing actionable metrics for clinical decisions, including complex scenarios like Myocardial Infarction with Non-Obstructive Coronary Arteries (MINOCA). Conclusions: The \u201cFrom Plaque to Perfusion\u201d framework, enabled by AI, reframes ACS imaging as an integrated, mechanism-driven pathway. This approach moves beyond isolated test interpretation toward a scalable model of precision, phenotype-led care that promises to improve diagnostic certainty and personalize patient management.<\/jats:p>","DOI":"10.3390\/jcm15082905","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T08:49:02Z","timestamp":1776070142000},"page":"2905","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Plaque to Perfusion: A Narrative Review of Multimodality Imaging in Acute Coronary Syndromes"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8188-4474","authenticated-orcid":false,"given":"Ahmed","family":"Shahin","sequence":"first","affiliation":[{"name":"Cardiology Department, Hampshire Hospitals NHS Foundation Trust, Basingstoke RG24 9NA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7009-7395","authenticated-orcid":false,"given":"Salaheldin","family":"Agamy","sequence":"additional","affiliation":[{"name":"Cardiology Department, Heartlands Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham B9 5SS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheref","family":"Zaghloul","sequence":"additional","affiliation":[{"name":"Cardiology Department, Royal Berkshire Hospital, Reading RG1 5AN, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ranin","family":"ElShafey","sequence":"additional","affiliation":[{"name":"Cardiology Department, Tanta University Hospitals, Tanta 31527, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maha","family":"Molda","sequence":"additional","affiliation":[{"name":"Radiology Department, East Cheshire NHS Foundation Trust, Macclesfield SK10 3BL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0710-2419","authenticated-orcid":false,"given":"Zahid","family":"Khan","sequence":"additional","affiliation":[{"name":"Townsville University Hospital, Townsville, QLD 4814, Australia"},{"name":"Department of Medicine and Dentistry, James Cook University, Townsville, QLD 4814, Australia"},{"name":"William Harvey Research Institute, Queen Mary University, London E1 4NS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1886-0119","authenticated-orcid":false,"given":"Luciano","family":"Candilio","sequence":"additional","affiliation":[{"name":"Royal Free London NHS Foundation Trust, London NW3 2QG, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3720","DOI":"10.1093\/eurheartj\/ehad191","article-title":"2023 ESC Guidelines for the management of acute coronary syndromes: Developed by the task force on the management of acute coronary syndromes of the European Society of Cardiology (ESC)","volume":"44","author":"Byrne","year":"2023","journal-title":"Eur. 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