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Holtackers RJ, Emrich T, Botnar RM, Kooi ME, Wildberger JE, Kreitner KF. Late Gadolinium Enhancement Cardiac Magnetic Resonance Imaging: From Basic Concepts to Emerging Methods. ROFO-FORTSCHR RONTG 2022; 194:491-504. [PMID: 35196714 DOI: 10.1055/a-1718-4355] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND Late gadolinium enhancement (LGE) is a widely used cardiac magnetic resonance imaging (MRI) technique to diagnose a broad range of ischemic and non-ischemic cardiomyopathies. Since its development and validation against histology already more than two decades ago, the clinical utility of LGE and its span of applications have increased considerably. METHODS In this review we will present the basic concepts of LGE imaging and its diagnostic and prognostic value, elaborate on recent developments and emerging methods, and finally discuss future prospects. RESULTS Continuous developments in 3 D imaging methods, motion correction techniques, water/fat-separated imaging, dark-blood methods, and scar quantification improved the performance and further expanded the clinical utility of LGE imaging. CONCLUSION LGE imaging is the current noninvasive reference standard for the assessment of myocardial viability. Improvements in spatial resolution, scar-to-blood contrast, and water/fat-separated imaging further strengthened its position. KEY POINTS · LGE MRI is the reference standard for the noninvasive assessment of myocardial viability. · LGE MRI is used to diagnose a broad range of non-ischemic cardiomyopathies in everyday clinical practice.. · Improvements in spatial resolution and scar-to-blood contrast further strengthened its position. · Continuous developments improve its performance and further expand its clinical utility. CITATION FORMAT · Holtackers RJ, Emrich T, Botnar RM et al. Late Gadolinium Enhancement Cardiac Magnetic Resonance Imaging: From Basic Concepts to Emerging Methods. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1718-4355.
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Affiliation(s)
- Robert J Holtackers
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, the Netherlands.,Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, the Netherlands.,School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Tilman Emrich
- Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, Mainz, Germany.,Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - René M Botnar
- School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.,Pontificia Universidad Católica de Chile, Escuela de Ingeniería, Santiago, Chile
| | - M Eline Kooi
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, the Netherlands.,Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, the Netherlands
| | - Joachim E Wildberger
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, the Netherlands.,Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, the Netherlands
| | - K-F Kreitner
- Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Germany
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Emrich T, Halfmann M, Schoepf UJ, Kreitner KF. CMR for myocardial characterization in ischemic heart disease: state-of-the-art and future developments. Eur Radiol Exp 2021; 5:14. [PMID: 33763757 PMCID: PMC7990980 DOI: 10.1186/s41747-021-00208-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 01/22/2021] [Indexed: 01/25/2023] Open
Abstract
Ischemic heart disease and its sequelae are one of the major contributors to morbidity and mortality worldwide. Over the last decades, technological developments have strengthened the role of noninvasive imaging for detection, risk stratification, and management of patients with ischemic heart disease. Cardiac magnetic resonance (CMR) imaging incorporates both functional and morphological characterization of the heart to determine presence, acuteness, and severity of ischemic heart disease by evaluating myocardial wall motion and function, the presence and extent of myocardial edema, ischemia, and scarring. Currently established clinical protocols have already demonstrated their diagnostic and prognostic value. Nevertheless, there are emerging imaging technologies that provide additional information based on advanced quantification of imaging biomarkers and improved diagnostic accuracy, therefore potentially allowing reduction or avoidance of contrast and/or stressor agents. The aim of this review is to summarize the current state of the art of CMR imaging for ischemic heart disease and to provide insights into promising future developments.
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Affiliation(s)
- Tilman Emrich
- Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz; Langenbeckstraße 1, 55131, Mainz, Germany. .,German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, Mainz, Langenbeckstraße 1, 55131, Mainz, Germany. .,Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA.
| | - Moritz Halfmann
- Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz; Langenbeckstraße 1, 55131, Mainz, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, Mainz, Langenbeckstraße 1, 55131, Mainz, Germany
| | - U Joseph Schoepf
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | - Karl-Friedrich Kreitner
- Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz; Langenbeckstraße 1, 55131, Mainz, Germany
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Lopez-Perez A, Sebastian R, Izquierdo M, Ruiz R, Bishop M, Ferrero JM. Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia. Front Physiol 2019; 10:580. [PMID: 31156460 PMCID: PMC6531915 DOI: 10.3389/fphys.2019.00580] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 04/25/2019] [Indexed: 12/20/2022] Open
Abstract
In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data.
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Affiliation(s)
- Alejandro Lopez-Perez
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Universitat de València, Valencia, Spain
| | - M Izquierdo
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Ricardo Ruiz
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Martin Bishop
- Division of Imaging Sciences & Biomedical Engineering, Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Jose M Ferrero
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
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Mastrodicasa D, Elgavish GA, Schoepf UJ, Suranyi P, van Assen M, Albrecht MH, De Cecco CN, van der Geest RJ, Hardy R, Mantini C, Griffith LP, Ruzsics B, Varga-Szemes A. Nonbinary quantification technique accounting for myocardial infarct heterogeneity: Feasibility of applying percent infarct mapping in patients. J Magn Reson Imaging 2018; 48:788-798. [PMID: 29446527 DOI: 10.1002/jmri.25973] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 01/24/2018] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Binary threshold-based quantification techniques ignore myocardial infarct (MI) heterogeneity, yielding substantial misquantification of MI. PURPOSE To assess the technical feasibility of MI quantification using percent infarct mapping (PIM), a prototype nonbinary algorithm, in patients with suspected MI. STUDY TYPE Prospective cohort POPULATION: Patients (n = 171) with suspected MI referred for cardiac MRI. FIELD STRENGTH/SEQUENCE Inversion recovery balanced steady-state free-precession for late gadolinium enhancement (LGE) and modified Look-Locker inversion recovery (MOLLI) T1 -mapping on a 1.5T system. ASSESSMENT Infarct volume (IV) and infarct fraction (IF) were quantified by two observers based on manual delineation, binary approaches (2-5 standard deviations [SD] and full-width at half-maximum [FWHM] thresholds) in LGE images, and by applying the PIM algorithm in T1 and LGE images (PIMT1 ; PIMLGE ). STATISTICAL TEST IV and IF were analyzed using repeated measures analysis of variance (ANOVA). Agreement between the approaches was determined with Bland-Altman analysis. Interobserver agreement was assessed by intraclass correlation coefficient (ICC) analysis. RESULTS MI was observed in 89 (54.9%) patients, and 185 (38%) short-axis slices. IF with 2, 3, 4, 5SDs and FWHM techniques were 15.7 ± 6.6, 13.4 ± 5.6, 11.6 ± 5.0, 10.8 ± 5.2, and 10.0 ± 5.2%, respectively. The 5SD and FWHM techniques had the best agreement with manual IF (9.9 ± 4.8%) determination (bias 1.0 and 0.2%; P = 0.1426 and P = 0.8094, respectively). The 2SD and 3SD algorithms significantly overestimated manual IF (9.9 ± 4.8%; both P < 0.0001). PIMLGE measured significantly lower IF (7.8 ± 3.7%) compared to manual values (P < 0.0001). PIMLGE , however, showed the best agreement with the PIMT1 reference (7.6 ± 3.6%, P = 0.3156). Interobserver agreement was rated good to excellent for IV (ICCs between 0.727-0.820) and fair to good for IF (0.589-0.736). DATA CONCLUSION The application of the PIMLGE technique for MI quantification in patients is feasible. PIMLGE , with its ability to account for voxelwise MI content, provides significantly smaller IF than any thresholding technique and shows excellent agreement with the T1 -based reference. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.
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Affiliation(s)
- Domenico Mastrodicasa
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Neuroscience and Imaging, Section of Diagnostic Imaging and Therapy - Radiology Division, SS. Annunziata Hospital, "G. d'Annunzio" University, Chieti, Italy
| | - Gabriel A Elgavish
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Pal Suranyi
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Marly van Assen
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
- University of Groningen, University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, Groningen, The Netherlands
| | - Moritz H Albrecht
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Carlo N De Cecco
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Rob J van der Geest
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rayphael Hardy
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Cesare Mantini
- Department of Neuroscience and Imaging, Section of Diagnostic Imaging and Therapy - Radiology Division, SS. Annunziata Hospital, "G. d'Annunzio" University, Chieti, Italy
| | - L Parkwood Griffith
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Balazs Ruzsics
- Department of Cardiology, Royal Liverpool and Broadgreen University Hospital, Liverpool, UK
| | - Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
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