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• W4229574541 abstract "HomeCirculation: Cardiovascular ImagingVol. 12, No. 10In This Issue of the Journal Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBIn This Issue of the Journal Robert J. Gropler, MD Robert J. GroplerRobert J. Gropler Search for more papers by this author Originally published10 Oct 2019https://doi.org/10.1161/CIRCIMAGING.119.009980Circulation: Cardiovascular Imaging. 2019;12:e009980I want to welcome you to the October 2019 issue of Circulation: Cardiovascular Imaging. In addition to highlighting the role of advanced imaging in the management of various forms of cardiovascular disease, new enabling technologies such as artificial intelligence/machine learning, novel computer simulations, and 3D printing examples are discussed.Heart transplantation is an established lifesaving treatment that improves survival, functional status, and quality of life for patients with advanced heart failure. However, long-term survival remains limited by transplant vasculopathy and allograft failure. Thus there is a compelling need for accurate, noninvasive, and safe surveillance approaches to reliably evaluate the health of the allograft. Hughes and colleagues, using late gadolinium enhancement cardiac magnetic resonance imaging, report that myocardial fibrosis is seen in 18% of heart transplant recipients and that its presence and the extent are independently associated with the long-term risk. In their commentary, Ibrahim and Fang nicely summarize the strengths and weaknesses of the study and raise the interesting point that multi-parametric cardiac magnetic resonance imaging that includes T1 and T2 mapping and myocardial blood flow measurements may lead to further improvement in the detection and prognostication of heart transplant rejection. Please note Continuing Medical Education Credit is offered with this article.It is becoming increasing apparent that that traditional algorithms for determining the pretest likelihood of significant coronary artery disease tend to markedly overestimate its prevalence, yet are typically used to identify patients for further testing. As a consequence, there has been a decline in detection rates of myocardial ischemia on myocardial perfusion single-photon emission computed tomography, one of the most commonly used noninvasive diagnostic tests for coronary artery disease detection. In support of this relationship, Batal et al provide evidence that in stable patients being evaluated for suspected coronary artery disease, the observed prevalence of a myocardial perfusion imaging abnormality is much lower than the expected prevalence of obstructive coronary artery disease derived from standard pretest likelihood models. In his editorial, Hendel concisely summarizes the evolution of pretest likelihood models for coronary artery disease, nicely contextualizes the study results with this evolution and makes a compelling call for improved algorithms to identify patients for further noninvasive testing.It is hoped the continued development artificial intelligence/machine learning tools will improve both the precision of serial cardiovascular imaging and shorten the time to complete imaging measurements. Using a fully automated convolutional neural network, Bhuva and colleagues report serial cardiac magnetic resonance imaging measurements of left ventricular chamber volumes, mass and ejection fraction that were equivalent to those obtained by expert readers but 186× faster. In his commentary, Colletti nicely summarizes the study and the potential of artificial intelligence /machine learning tools to enhance cardiovascular image interpretation.While outcomes of transcatheter aortic valve replacement in bicuspid aortic valve morphology continue to improve there is a need to better identify patients who may be at risk for unfavorable clinical outcomes, such as paravalvular regurgitation and conduction abnormalities. Dowling and colleagues describe their experience employing finite element analysis and computational fluid dynamics to generate computer simulations that provides patient-specific valve sizing and positioning. Their data suggest this more personalized approach may have potential in reducing paravalvular regurgitation and markers of conduction abnormalities in these patients.While the relatively poor prognosis in patients with low-flow-low gradient severe aortic valve stenosis with preserved left ventricular ejection fraction has been well-documented, little is known about the prognostic impact when a high gradient is documented. Maréchaux et al report asymptomatic or minimally symptomatic patients with this condition have a considerable increased risk of mortality suggesting prompt aortic valve replacement should be considered. In their editorial, Golbus and Bach provide a comprehensive review of the study and emphasize the need for future advances in cardiovascular imaging to better characterize and risk stratify these patients.It is becoming increasing appreciated that mitral regurgitation secondary to left atrial dilation in patients with atrial fibrillation can improve after sinus rhythm is maintained after catheter ablation. However, the mechanisms responsible for this improvement are unclear. To help in this regard, Nishino and colleagues performed serial 3D echocardiography before and after the maintenance normal sinus rhythm in these patients and report reverse remodeling of the mitral valve apparatus may be important an contributor to the reduced mitral regurgitation.Cardiovascular 3D printing of replicas of patient-specific cardiac anatomy using image data sets obtained from multi-modality imaging is gaining increasing usage from both an educational and treatment planning perspective in both congenital and adult cardiac diseases. In Advances in Cardiovascular Imaging, Harb and colleagues provide a wonderful summary of the key aspects of this technology such as the workflow from imaging acquisition to model generation as well discuss its application in percutaneous structural interventions.In the Cardiovascular Images section, Slavich and colleagues present an interesting case of how multimodality imaging was used for sudden cardiac death risk stratification in a patient with mitral valve prolapse. Li et al describe how echocardiography and cardiac computed tomography were needed to diagnose a rare case of mycobacterium spindle cell pseudo-tumor. Yao and colleagues present a case on how single-photon computed tomography and positron emission tomography/magnetic resonance imaging aided in the diagnosis of takotsubo cardiomyopathy. D’Ostrevy et al discuss how contrast computed tomography angiography contributed to the diagnosis of an endo-aortic chord as the cause of unexplained stroke. Ciccio and colleagues present a case that shows the added value of multimodality imaging in detecting myocardial metastasis from a carcinoid ileal tumor. Dugo et al report on how multimodality imaging was critical in the assessments of a complex case of aortic prosthetic valve endocarditis.I thank you for reading Circulation: Cardiovascular Imaging and hope you enjoyed reading the various pieces. Please take advantage of our Continuing Medical Education opportunity. We look forward to your future visits to the journal.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association. Previous Back to top Next FiguresReferencesRelatedDetails October 2019Vol 12, Issue 10 Advertisement Article InformationMetrics © 2019 American Heart Association, Inc.https://doi.org/10.1161/CIRCIMAGING.119.009980PMID: 31597468 Originally publishedOctober 10, 2019 PDF download Advertisement" @default.
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