News From ISCT 2018

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Should CT Be First For Evaluating Chest Trauma?

What's the best first-line diagnostic technique for assessing penetrating chest trauma? Dr. Savvas Nicolaou made the case for CT as the imaging modality of choice in a Friday presentation at the 2018 International Society for Computed Tomography (ISCT) symposium.

CT is ideal for the initial assessment of hemodynamically stable patients (blood pressure > 90 mm Hg) who present with acute penetrating trauma in the emergency department, Nicolaou, director of emergency and trauma radiology at Vancouver General Hospital in Canada, told meeting attendees. The technique can reveal the mechanism of injury and key anatomical landmarks, allowing for rapid triage and diagnosis.



5 Ways DECT Can Improve The Value Of Imaging

What are the top clinical applications of dual-energy CT (DECT), and in what ways does the technique add value to diagnostic imaging? Radiologists from Stanford and Duke University tackled these and similar questions in a session at the 2018 ISCT symposium.

Advancements to CT technology have led to the emergence of DECT as a means of enhancing various aspects of image visualization. From improving lesion conspicuity to overcoming uncertainty in characterizing small lesions, DECT offers numerous diagnostic advantages compared with conventional CT, without altering costs, scanning times, or radiation dose, Dr. Bhavik Patel of Stanford told meeting attendees.



Machine Learning FFR-CT Expedites Heart Evaluation

Machine-learning fractional flow reserve CT (FFR-CT) may help clinicians overcome key barriers to conventional FFR-CT, including the lengthy amount of time required to obtain measurements, according to a presentation at the 2018 International Society for Computed Tomography (ISCT) symposium.

There are a number of solutions being developed to address the clinical and nonclinical challenges of using FFR-CT to determine the degree of stenosis in an artery, presenter Dr. Koen Nieman, PhD, from Stanford University told meeting attendees. One possible method is to use the computing power of artificial intelligence to help speed up the collection of FFR measurements.