Society for Cardiovascular Magnetic Resonance (SCMR) expert consensus for CMR imaging endpoints in clinical research: part I - analytical validation and clinical qualification

Püntmann, Valentina O.; Valbuena, S.; Hinojar, Rocío; Petersen, Steffen E.; Greenwood, John P.; Kramer, Christopher M.; Kwong, Raymond Y.; McCann, Gerry P; Berry, Colin; Nagel, Eike

doi:10.1186/s12968-018-0484-5

Cited by 109 publications

(103 citation statements)

References 101 publications

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“…This current study includes a thorough quantitative analysis of postinfarction scars. The values of absolute and relative mass of scars, gray zones, and segments with transmural fibrosis do not significantly differentiate the patients with and without VT (Table 1) which is comparable to the results obtained by Perez‐David 22 and different from those by Boyé et al, 21 Di Bella et al, 23 and Lønborg et al 24 This is likely due to methodology which was not consistent in all the studies with the recommendations of the Society of Cardiovascular Magnetic Resonance (SCMR) 25,26 . We believe that it is not about the quantity of heterogeneous tissue in general, but only these parts which connect to the healthy myocardium and thus facilitate for a wavefront to enter, propagate at a lower speed than the rest of the myocardium and exit belatedly.…”

Section: Discussionmentioning

confidence: 51%

Islets of heterogeneous myocardium within the scar in cardiac magnetic resonance predict ventricular tachycardia after myocardial infarction

Małaczyńska-Rajpold

Błaszyk

Kociemba

et al. 2020

Cardiovasc electrophysiol

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Introduction:We assessed findings in cardiac magnetic resonance (CMR) as predictors of ventricular tachycardia (VT) after myocardial infarction (MI), which could allow for more precise identification of patients at risk of sudden cardiac death.Methods: Forty-eight patients after prior MI were enrolled and divided into two groups: with (n = 24) and without (n = 24) VT. VT was confirmed by electrophysiological study and exit site was estimated based on 12-lead electrocardiogram.All patients underwent CMR with late gadolinium enhancement.Results: The examined groups did not differ significantly in clinical and demographical parameters (including LV ejection fraction). There was a significant difference in the infarct age between the VT and non-VT group (15.8 ± 8.4 vs 7.1 ± 6.7 years, respectively; P = .002), with the cut-off point at the level of 12 years. In the scar core, islets of heterogeneous myocardium were revealed. They were defined as areas of potentially viable myocardium within or adjacent to the core scar. The number of islets was the strongest independent predictor of VT (odds ratio [OR], 1.42; confidence interval [CI], 1.17-1.73), but total islet size and the largest islet area were also significantly higher in the VT group (OR, 1.04; CI, 1.02-1.07 and OR, 1.16; CI, 1.01-1.27, respectively). Myocardial segments with fibrosis forming 25%-75% of the ventricular wall were associated with a higher incidence of VT (7.5 ± 2.1 vs 5.7 ± 2.6; P = .014). Three-dimension CMR reconstruction confirmed good correlation of the location of the islets/channels with VT exit site during electroanatomical mapping in five cases. Conclusions:The identification and quantification of islets of heterogeneous myocardium within the scar might be useful for predicting VT in patients after MI. K E Y W O R D S cardiac magnetic resonance, late gadolinium enhancement, myocardial infarction, myocardial scar, SCD risk, ventricular tachycardia SUPPORTING INFORMATION Additional supporting information may be found online in the Supporting Information section. How to cite this article: Malaczynska-Rajpold K, Blaszyk K, Kociemba A, Pyda M, Posadzy-Malaczynska A, Grajek S. Islets of heterogeneous myocardium within the scar in cardiac magnetic resonance predict ventricular tachycardia after myocardial infarction.

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Section: Discussionmentioning

confidence: 51%

Islets of heterogeneous myocardium within the scar in cardiac magnetic resonance predict ventricular tachycardia after myocardial infarction

Małaczyńska-Rajpold

Błaszyk

Kociemba

et al. 2020

Cardiovasc electrophysiol

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“…Treatment up-titration of immunosuppressive therapy, optimal anti-remodeling therapy 4 , exercise restriction…”

Section: Diagnosis Of Autoimmune Myopericarditismentioning

confidence: 99%

“…Tapering of immunosuppressive therapy, continue optimal anti-remodeling therapy 4 , discontinuation of colchicine. Follow-up CMR in 6 months Fig.…”

Section: No Cmr-evidence Of Autoimmune Myopericarditismentioning

confidence: 99%

“…Even though, over the last decades, the mortality related to pericardial disease has been constantly decreasing [2], morbidity has remained a considerable and unconquered problem [3]. The main challenge remains a prompt diagnosis and targeted management guided by the underlying pathophysiology and mechanisms as well as the activity of the disease, all of which requires an accurate diagnostic method which can be safely applied serially to allow monitoring of treatment [4,5]. According to current clinical practice, the diagnosis of the pericardial diseases, such as pericarditis, is mainly based on the symptoms, the ECG, and the echocardiography.…”

Section: Introductionmentioning

confidence: 99%

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CMR in Pericardial Diseases - an Update

Vidalakis

Kolentinis

Gawor

et al. 2020

Curr Cardiovasc Imaging Rep

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Purpose of Review To review the latest developments and the current role of the cardiac magnetic resonance (CMR) in pericardial diseases and their complications. Recent Findings Cardiac Magnetic Resonance (CMR) has the ability to incorporate anatomy, physiology, and "virtual histology" strategies to achieve the most accurate diagnosis for even the most demanding, pericardial diseases. Summary Acute, chronic, recurrent, and constrictive pericarditis as well as pericarditis related complications, pericardial masses and congenital pericardial defects are commonly encountered in clinical practice with relatively significant morbidity and mortality. Owing to the challenging diagnosis, CMR imaging is often employed in confirming the diagnosis and elucidating the underling pathophysiology. In this review we outline the common CMR techniques and their expected diagnostic outcomes.

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“…For example, Bai et al recently demonstrated that CNN can provide a performance on par with human experts in analyzing cine cardiovascular magnetic resonance (CMR) data (5). Cine CMR data typically has high spatial temporal resolution, excellent SNR, and consistent bloodmyocardium CNR throughout the cardiac cycle, which is why cine CMR is the gold standard for assessment of ventricular function, volumes, mass, and ejection fraction (7).…”

Section: Introductionmentioning

confidence: 99%

Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN

Guo

Yoon

et al. 2019

Magnetic Resonance in Med

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words maximum)Purpose: To apply deep CNN to the segmentation task in myocardial arterial spin labeled (ASL) perfusion imaging and to develop methods that measure uncertainty and that adapt the CNN model to a specific false positive vs. false negative tradeoff. Methods:The Monte Carlo dropout (MCD) U-Net was trained on data from 22 subjects and tested on data from 6 heart transplant recipients. Manual segmentation and regional myocardial blood flow (MBF) were available for comparison. We consider two global uncertainty measures, named "Dice Uncertainty" and "MCD Uncertainty", which were calculated with and without the use of manual segmentation, respectively. Tversky loss function with a hyperparameter β was used to adapt the model to a specific false positive vs. false negative tradeoff. Results:The MCD U-Net achieved Dice coefficient of 0.91 ± 0.04 on the test set. MBF measured using automatic segmentations was highly correlated to that measured using the manual segmentation (R 2 = 0.96). Dice Uncertainty and MCD Uncertainty were in good agreement (R 2 = 0.64). As β increased, the false positive rate systematically decreased and false negative rate systematically increased. Conclusion:We demonstrate the feasibility of deep CNN for automatic segmentation of myocardial ASL, with good accuracy. We also introduce two simple methods for assessing model uncertainty. Finally, we demonstrate the ability to adapt the CNN model to a specific false positive vs. false negative tradeoff. These findings are directly relevant to automatic segmentation in quantitative cardiac MRI and are broadly applicable to automatic segmentation problems in diagnostic imaging.Keywords: MRI, arterial spin labeling, automatic segmentation, deep convolutional neural network, false positive and false negative tradeoff, uncertainty measure, quality assessment, Bayesian, Monte Carlo Dropout

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Society for Cardiovascular Magnetic Resonance (SCMR) expert consensus for CMR imaging endpoints in clinical research: part I - analytical validation and clinical qualification

Cited by 109 publications

References 101 publications

Islets of heterogeneous myocardium within the scar in cardiac magnetic resonance predict ventricular tachycardia after myocardial infarction

Islets of heterogeneous myocardium within the scar in cardiac magnetic resonance predict ventricular tachycardia after myocardial infarction

CMR in Pericardial Diseases - an Update

Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN

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