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Recent advances in PET-MRI for Cardiac Sarcoidosis

Research output: Contribution to journalLiterature reviewpeer-review

Original languageEnglish
Accepted/In press28 Nov 2022
Published19 Dec 2022

Bibliographical note

The authors acknowledge financial support from: (1) BHF PG/18/59/33955 and RG/20/1/34802 (2) EPSRC EP/V044087/1, EP/P001009/1, EP/P032311/1, EP/P007619, (3) Wellcome EPSRC Centre for Medical Engineering (NS/A000049/1), (4) Millennium Institute for Intelligent Healthcare Engineering ICN2021_004, and (5) the Department of Health through the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award and NIHR Cardiovascular MIC. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission.


King's Authors


The diagnosis of cardiac sarcoidosis (CS) remains challenging. While only a small fraction of patients with systemic sarcoidosis present with clinically symptomatic CS, cardiac involvement has been associated with adverse outcomes, such as ventricular arrhythmia, heart block, heart failure and sudden cardiac death. Despite the clinical relevance of having an early and accurate diagnosis of CS, there is no gold-standard technique available for the assessment of CS. Non-invasive PET and MR imaging have shown promise in the detection of different histopathological features of CS. More recently, the introduction of hybrid PET-MR scanners has enabled the acquisition of these hallmarks in a single scan, demonstrating higher sensitivity and specificity for CS detection and risk stratification than with either imaging modality alone. This article describes recent developments in hybrid PET-MR imaging for improving the diagnosis of CS and discusses areas of future development that could make cardiac PET-MRI the preferred diagnostic tool for the comprehensive assessment of CS.

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