Here's what to expect this week on Diagnostic Imaging.
In this week’s preview, here are some highlights of what you can expect to see coming soon:
Smoking can make changes to the airways early and in ways that are likely to go unnoticed. Quantitative CT (QCT) has become the gold standard for imaging these alterations in patients with chronic obstructive pulmonary disease (COPD) because it is non-invasive, but there has been little research into whether it can pinpoint changes in smokers who appear to have a normal CT. Look for an article later this week that discusses how well QCT works with this subclinical population.
For more lung CT coverage, click here.
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Even as vaccine rates increase, there is still a need for rapid, accurate COVID-19 diagnosis. Chest X-ray, bolstered by artificial intelligence (AI), could be a near-perfect option – especially when chest CT is not readily available or swab and saliva test results are delayed. Keep your eyes open for an article later this week.
For more coverage of chest X-rays and COVID-19, click here.
Last week, Diagnostic Imaging published an article on the use of a triaging model to better prioritize mammography screenings during times of crisis, such as the COVID-19 pandemic. This week, be on the lookout for interview with study lead Diana L. Miglioretti, Ph.D., from the University of California at Davis about why such a system is important and the impacts it could have downstream.
For additional mammography and COVID-19 coverage, click here.
GE HealthCare Debuts AI-Powered Cardiac CT Device at ACC Conference
April 1st 2025Featuring enhanced low-dose image quality with motion-free images, the Revolution Vibe CT system reportedly facilitates improved diagnostic clarity for patients with conditions ranging from in-stent restenosis to atrial fibrillation.
Predicting Diabetes on CT Scans: What New Research Reveals with Pancreatic Imaging Biomarkers
March 25th 2025Attenuation-based biomarkers on computed tomography (CT) scans demonstrated a 93 percent interclass correlation coefficient (ICC) agreement across three pancreatic segmentation algorithms for predicting diabetes, according to a study involving over 9,700 patients.