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  1. Article ; Online: Comparative Accuracy of Noninvasive Imaging Tests in Stable Chest Pain: Does It Matter?

    Plein, Sven

    Journal of the American College of Cardiology

    2023  Volume 82, Issue 19, Page(s) 1839–1841

    MeSH term(s) Humans ; Coronary Artery Disease ; Tomography, Emission-Computed, Single-Photon/methods ; Chest Pain/diagnosis ; Chest Pain/etiology ; Myocardial Perfusion Imaging/methods ; Coronary Angiography/methods ; Predictive Value of Tests ; Magnetic Resonance Imaging
    Language English
    Publishing date 2023-11-01
    Publishing country United States
    Document type Editorial ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 605507-2
    ISSN 1558-3597 ; 0735-1097
    ISSN (online) 1558-3597
    ISSN 0735-1097
    DOI 10.1016/j.jacc.2023.09.805
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book: Advanced cardiac imaging

    Nieman, Koen / Gaemperli, Oliver / Lancellotti, Patrizio / Plein, Sven

    (Woodhead publishing series in biomaterials ; number 99)

    2015  

    Author's details edited by Koen Nieman, Oliver Gaemperli, Patrizio Lancellotti and Sven Plein
    Series title Woodhead publishing series in biomaterials ; number 99
    Collection
    Keywords Heart Diseases / diagnosis ; Cardiac Imaging Techniques
    Language English
    Size xxi, 781 Seiten, Illustrationen, Diagramme
    Publisher Elsevier
    Publishing place Amsterdam
    Publishing country Netherlands
    Document type Book
    HBZ-ID HT018929817
    ISBN 978-1-78242-282-2 ; 9781782422945 ; 1-78242-282-X ; 1782422943
    Database Catalogue ZB MED Medicine, Health

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  3. Article ; Online: Automated Pixelwise Perfusion Mapping by CMR: When Many Small Steps Make a Giant Leap.

    Plein, Sven

    JACC. Cardiovascular imaging

    2018  Volume 11, Issue 5, Page(s) 708–710

    MeSH term(s) Coronary Circulation ; Image Interpretation, Computer-Assisted ; Magnetic Resonance Spectroscopy ; Myocardial Perfusion Imaging
    Language English
    Publishing date 2018-05-10
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 2491503-8
    ISSN 1876-7591 ; 1936-878X
    ISSN (online) 1876-7591
    ISSN 1936-878X
    DOI 10.1016/j.jcmg.2018.03.002
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Role of epicardial adipose tissue in diabetic cardiomyopathy through the lens of cardiovascular magnetic resonance imaging - a narrative review.

    Kotha, Sindhoora / Plein, Sven / Greenwood, John P / Levelt, Eylem

    Therapeutic advances in endocrinology and metabolism

    2024  Volume 15, Page(s) 20420188241229540

    Abstract: Accumulating evidence suggests that ectopic/visceral adiposity may play a key role in the pathogenesis of nonischaemic cardiovascular diseases associated with type 2 diabetes. Epicardial adipose tissue (EAT) is a complex visceral fat depot, covering 80% ... ...

    Abstract Accumulating evidence suggests that ectopic/visceral adiposity may play a key role in the pathogenesis of nonischaemic cardiovascular diseases associated with type 2 diabetes. Epicardial adipose tissue (EAT) is a complex visceral fat depot, covering 80% of the cardiac surface with anatomical and functional contiguity to the myocardium and coronary arteries. EAT interacts with the biology of the underlying myocardium by secreting a wide range of adipokines. Magnetic resonance imaging (MRI) is the reference modality for structural and functional imaging of the heart. The technique is now also emerging as the reference imaging modality for EAT quantification. With this narrative review, we (a) surveyed contemporary clinical studies that utilized cardiovascular MRI to characterize EAT (studies published 2010-2023); (b) listed the clinical trials monitoring the response to treatment in EAT size as well as myocardial functional and structural parameters and (c) discussed the potential pathophysiological role of EAT in the development of diabetic cardiomyopathy. We concluded that increased EAT quantity and its inflammatory phenotype correlate with early signs of left ventricle dysfunction and may have a role in the pathogenesis of cardiac disease in diabetes with and without coronary artery disease.
    Language English
    Publishing date 2024-03-10
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2554822-0
    ISSN 2042-0196 ; 2042-0188
    ISSN (online) 2042-0196
    ISSN 2042-0188
    DOI 10.1177/20420188241229540
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Coronary sinus flow measurement-a useful addition to first pass myocardial perfusion cardiovascular magnetic resonance?

    Plein, Sven

    European heart journal. Cardiovascular Imaging

    2017  Volume 18, Issue 8, Page(s) 860–861

    MeSH term(s) Coronary Circulation ; Coronary Sinus ; Magnetic Resonance Angiography ; Magnetic Resonance Imaging ; Magnetic Resonance Spectroscopy ; Myocardial Perfusion Imaging
    Language English
    Publishing date 2017-03-31
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 2638345-7
    ISSN 2047-2412 ; 2047-2404
    ISSN (online) 2047-2412
    ISSN 2047-2404
    DOI 10.1093/ehjci/jex026
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Response to: 'Correspondence on 'Cardiovascular effects of biological versus conventional synthetic disease-modifying antirheumatic drug therapy in treatment-naive, early rheumatoid arthritis'' by Georgiadis

    Plein, Sven / Buch, Maya H

    Annals of the rheumatic diseases

    2021  Volume 82, Issue 4, Page(s) e90

    MeSH term(s) Humans ; Antirheumatic Agents/therapeutic use ; Arthritis, Rheumatoid/drug therapy ; Etanercept/therapeutic use ; Treatment Outcome ; Drug Therapy, Combination ; Biological Products/therapeutic use
    Chemical Substances Antirheumatic Agents ; Etanercept (OP401G7OJC) ; Biological Products
    Language English
    Publishing date 2021-02-08
    Publishing country England
    Document type Letter ; Comment
    ZDB-ID 7090-7
    ISSN 1468-2060 ; 0003-4967
    ISSN (online) 1468-2060
    ISSN 0003-4967
    DOI 10.1136/annrheumdis-2021-219926
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Are We There Yet?: The Road to Routine Rapid CMR Imaging.

    Plein, Sven / Kozerke, Sebastian

    JACC. Cardiovascular imaging

    2021  Volume 14, Issue 9, Page(s) 1755–1757

    MeSH term(s) Gadolinium ; Humans ; Magnetic Resonance Imaging, Cine ; Predictive Value of Tests
    Chemical Substances Gadolinium (AU0V1LM3JT)
    Language English
    Publishing date 2021-08-18
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 2491503-8
    ISSN 1876-7591 ; 1936-878X
    ISSN (online) 1876-7591
    ISSN 1936-878X
    DOI 10.1016/j.jcmg.2021.03.023
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: CardioPulse: Year in cardiology 2015: the future of cardiovascular magnetic resonance imaging.

    Plein, Sven

    European heart journal

    2016  Volume 37, Issue 8, Page(s) 663–664

    MeSH term(s) Forecasting ; Heart Diseases/diagnosis ; Humans ; Magnetic Resonance Angiography/trends
    Language English
    Publishing date 2016-02-21
    Publishing country England
    Document type Journal Article
    ZDB-ID 603098-1
    ISSN 1522-9645 ; 0195-668X
    ISSN (online) 1522-9645
    ISSN 0195-668X
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: High-resolution quantification of stress perfusion defects by cardiac magnetic resonance.

    Scannell, Cian M / Crawley, Richard / Alskaf, Ebraham / Breeuwer, Marcel / Plein, Sven / Chiribiri, Amedeo

    European heart journal. Imaging methods and practice

    2024  Volume 2, Issue 1, Page(s) qyae001

    Abstract: Aims: Quantitative stress perfusion cardiac magnetic resonance (CMR) is becoming more widely available, but it is still unclear how to integrate this information into clinical decision-making. Typically, pixel-wise perfusion maps are generated, but ... ...

    Abstract Aims: Quantitative stress perfusion cardiac magnetic resonance (CMR) is becoming more widely available, but it is still unclear how to integrate this information into clinical decision-making. Typically, pixel-wise perfusion maps are generated, but diagnostic and prognostic studies have summarized perfusion as just one value per patient or in 16 myocardial segments. In this study, the reporting of quantitative perfusion maps is extended from the standard 16 segments to a high-resolution bullseye. Cut-off thresholds are established for the high-resolution bullseye, and the identified perfusion defects are compared with visual assessment.
    Methods and results: Thirty-four patients with known or suspected coronary artery disease were retrospectively analysed. Visual perfusion defects were contoured on the CMR images and pixel-wise quantitative perfusion maps were generated. Cut-off values were established on the high-resolution bullseye consisting of 1800 points and compared with the per-segment, per-coronary, and per-patient resolution thresholds. Quantitative stress perfusion was significantly lower in visually abnormal pixels, 1.11 (0.75-1.57) vs. 2.35 (1.82-2.9) mL/min/g (Mann-Whitney
    Conclusion: This study introduces a high-resolution bullseye consisting of 1800 points, rather than 16, per patient for reporting quantitative stress perfusion, which may improve sensitivity. Using this representation, the threshold required to identify areas of reduced perfusion is lower than for segmental analysis.
    Language English
    Publishing date 2024-01-09
    Publishing country England
    Document type Journal Article
    ISSN 2755-9637
    ISSN (online) 2755-9637
    DOI 10.1093/ehjimp/qyae001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Deep learning based automated left ventricle segmentation and flow quantification in 4D flow cardiac MRI.

    Sun, Xiaowu / Cheng, Li-Hsin / Plein, Sven / Garg, Pankaj / van der Geest, Rob J

    Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance

    2024  Volume 26, Issue 1, Page(s) 100003

    Abstract: Background: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the ... ...

    Abstract Background: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the segmentation derived from a registered cine MRI acquisition. This requires an additional acquisition and is prone to imperfect spatial and temporal inter-scan alignment. Therefore, in this work we developed and evaluated deep learning-based methods to segment the left ventricle (LV) from 4D flow MRI directly.
    Methods: We compared five deep learning-based approaches with different network structures, data pre-processing and feature fusion methods. For the data pre-processing, the 4D flow MRI data was reformatted into a stack of short-axis view slices. Two feature fusion approaches were proposed to integrate the features from magnitude and velocity images. The networks were trained and evaluated on an in-house dataset of 101 subjects with 67,567 2D images and 3030 3D volumes. The performance was evaluated using various metrics including Dice, average surface distance (ASD), end-diastolic volume (EDV), end-systolic volume (ESV), LV ejection fraction (LVEF), LV blood flow kinetic energy (KE) and LV flow components. The Monte Carlo dropout method was used to assess the confidence and to describe the uncertainty area in the segmentation results.
    Results: Among the five models, the model combining 2D U-Net with late fusion method operating on short-axis reformatted 4D flow volumes achieved the best results with Dice of 84.52% and ASD of 3.14 mm. The best averaged absolute and relative error between manual and automated segmentation for EDV, ESV, LVEF and KE was 19.93 ml (10.39%), 17.38 ml (22.22%), 7.37% (13.93%) and 0.07 mJ (5.61%), respectively. Flow component results derived from automated segmentation showed high correlation and small average error compared to results derived from manual segmentation.
    Conclusions: Deep learning-based methods can achieve accurate automated LV segmentation and subsequent quantification of volumetric and hemodynamic LV parameters from 4D flow MRI without requiring an additional cine MRI acquisition.
    Language English
    Publishing date 2024-01-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 1458034-2
    ISSN 1532-429X ; 1097-6647
    ISSN (online) 1532-429X
    ISSN 1097-6647
    DOI 10.1016/j.jocmr.2023.100003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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