Cardiac Imaging: Page 52

CCTA, CT perfusion bolster coronary stent evaluation
By Abraham Kim
Monday, November 26 | 11:50 a.m.-12:00 p.m. | SSC01-09 | Room S504CDCombining coronary CT angiography (CCTA) with stress CT perfusion increases the accuracy of diagnosing in-stent restenosis in patients with heart disease, according to this study to be presented on Monday.
October 31, 2018
CT perfusion plus CCTA improves diagnosis of CAD
By Abraham Kim
Monday, November 26 | 11:20 a.m.-11:30 a.m. | SSC01-06 | Room S504CDA group from Italy found that stress coronary CT perfusion in addition to coronary CT angiography (CCTA) can enhance the diagnosis of coronary artery disease (CAD) in patients at intermediate to high risk for the disease.
October 31, 2018
Deep-learning algorithm automates FFR-CT estimation
By Abraham Kim
Sunday, November 25 | 11:35 a.m.-11:45 a.m. | SSA03-06 | Room S404ABResearchers from Japan have developed a deep-learning model capable of automatically estimating minimum fractional flow reserve CT (FFR-CT) with an accuracy approaching that of invasive FFR.
October 31, 2018
CT perfusion tops FFR-CT in predicting cardiac events
By Abraham Kim
Sunday, November 25 | 11:05 a.m.-11:15 a.m. | SSA03-03 | Room S404ABCT myocardial perfusion may be a better predictor for major adverse cardiac events in patients with coronary artery disease than fractional flow reserve CT (FFR-CT), according to this study being presented on Sunday.
October 31, 2018
Is FFR-CT or CT perfusion better for evaluating CAD?
By Abraham Kim
Sunday, November 25 | 10:55 a.m.-11:05 a.m. | SSA03-02 | Room S404ABBoth fractional flow reserve CT (FFR-CT) and CT perfusion are viable options for detecting stenosis in patients with coronary artery disease (CAD), though CT perfusion may be more accurate, according to researchers from Italy.
October 31, 2018
Deep-learning reconstruction improves coronary MR angiography
By Wayne Forrest
Wednesday, November 28 | 11:40 a.m.-11:50 a.m. | SSK03-08 | Room S102CDIn this session, Japanese researchers will share results from their newly developed deep-learning reconstruction technique designed to improve image quality for noncontrast coronary MR angiography.
October 30, 2018
Cardiac FDG-PET/MRI's role in Anderson-Fabry disease
By Wayne Forrest
Tuesday, November 27 | 3:50 p.m.-4:00 p.m. | SSJ03-06 | Room E353ASimultaneous cardiac FDG-PET/MRI could be a welcome option for the early detection of cardiac involvement in Anderson-Fabry disease and identifying different stages of disease progression, according to researchers from Italy.
October 30, 2018
Deep learning with MRI mimics cardiac technologists
By Wayne Forrest
Monday, November 26 | 3:00 p.m.-3:10 p.m. | SSE04-01 | Room N226Researchers are reporting success with a deep-learning convolutional neural network designed to localize key cardiac landmarks to help with complex examinations such as cardiac MRI.
October 30, 2018
Machine learning bests CAD-RADS for risk assessment
By Erik L. Ridley
Tuesday, November 27 | 10:40 a.m.-10:50 a.m. | SSG02-02 | Room S104BIn this talk, researchers will report that machine learning performs better than the Coronary Artery Disease Reporting and Data System (CAD-RADS) for predicting death and coronary events on coronary CT angiography.
October 28, 2018
Augmented reality primed for MR-guided cardiac ablation
By AuntMinnie.com staff writers
Augmented reality could someday allow clinicians to visualize 3D cardiac MRI scans while performing radiofrequency ablation to treat heart rhythm disorders, according to an article recently published online in PLOS One.
October 24, 2018
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CT reveals heart enlargement differs in men, women
By Abraham Kim
Is CT a viable alternative to cardiac MRI for assessing heart chamber enlargement? Yes, say researchers from Canada, who discovered that the condition was much more common in men than women. Their findings were published in the November issue of the American Journal of Roentgenology.
October 24, 2018
2018 10 24 22 23 8807 Heart Metallic Red Gold 400
Machine-learning CCTA helps characterize heart plaques
By Abraham Kim
Researchers from Japan have developed a method that uses machine learning to characterize plaques on coronary CT angiography (CCTA) scans with an accuracy exceeding that of standard techniques, according to an article published online October 20 in the Journal of Cardiovascular Computed Tomography.
October 23, 2018
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