It can be hard for physicians to identify the damaged artery responsible for a non-ST segment elevation myocardial infarction (NSTEMI). Typically coronary angiography is the first choice, but is it the best?
Deep learning designed to read single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) can improve the diagnosis of coronary artery disease—a killer of more than 370,000 people in the U.S. annually.
The European Association of Percutaneous Cardiovascular Interventions (EAPCI) has published a clinical guidance statement regarding the use of intra-coronary imaging (IC), sharing the document during its EuroPCR Annual Meeting, May 21-24 in Paris.
A machine learning algorithm trained to read imaging scans was more accurate at predicting heart attacks or death than expertly trained physicians, according to a study presented at the International Conference on Nuclear Cardiology and Cardiac CT (ICNC) in Lisbon, Portugal, on May 12.
A CT angiography (CTA) approach with "drastically" lowered tube currents combined with iterative construction reduced radiation exposure and maintained image quality in patients with suspected acute stroke, reported authors of a recent study published in the American Journal of Roentgenology.
“For CCTA to enter the mainstream of diagnostic clinical care, it is necessary to decrease observer variability and automate key parts of the interpretive process to manage the subjectivity, time-consuming nature, and variability of reader interpretation,” wrote authors of a new study published in the European Journal of Radiology.
“With imaging, we’ll be able to identify vulnerable plaque, deliver treatment directly to it, and see whether the treatment is effective," said award recipient Yongjian Liu, PhD, with Washington University in St. Louis.
Nearly a quarter of CT pulmonary angiography (CTPA) orders did not align with scoring system guidelines for evaluating potential pulmonary embolism (PE) in the emergency department, according to a single-center study published in the Journal of the American College of Radiology.