Computed Tomography (CT)
Cardiac computed tomography CT systems use a series of X-ray images to create an image volume dataset that can be sliced or manipulated on any plane using advanced visualization software. This channel includes content on CT scanners, CT contrast agents, CT angiography (CTA and CCTA), CT perfusion, spectral CT (also called dual souce or dual energy CT), and interative image reconstruction software that can reduce dose and make lower-quality CT images diagnostic.
Figure 4 for the study. Images of a 65-year-old man (patient 6). (a) Cardiac MRI perfusion shows perfusion deficit of anterior/anterolateral wall attributed to left anterior descending artery/left circumflex artery (*). (b) CT coronary angiography. (c) Coronary angiography, left anterior oblique projection with caudal angulation. (d) Three-dimensional image fusion helped refine diagnosis: perfusion deficits (*) were most likely caused by narrow first diagonal branch and its first, stented side branch (arrowhead). Retrospectively, denoted lesion could also be found at CT coronary angiography and coronary angiography (arrowheads in b and c, respectively). CT FFR = CT-derived fractional flow reserve, LGE = late gadolinium enhancement. Image courtesy of RSNA, Radiology.
Figure 2: Pulmonary CT angiography of a 68 year old male. The CT scan was obtained 10 days after the onset of COVID-19 symptoms and on the day the patient was transferred to the intensive care unit. Axial CT images (lung windows) (a,b) show peripheral ground-glass opacities (arrow) associated with areas of consolidation in dependent portions of the lung (arrowheads). Interlobular reticulations, bronchiectasis (black arrow) and lung architectural distortion are present. Involvement of the lung volume was estimated to be between 25% and 50%. Coronal CT reformations (mediastinum windows) (c,d) show bilateral lobar and segmental pulmonary embolism (black arrows). Courtesy of RSNA
An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal. Photo by Dave Fornell
Two examples of CT myocardial perfusion (CTP) imaging assessment software. Canon is on the left and GE Healthcare is on the right. Both of these technologies have been around for a few years, but there have been an increasing amount of clinical data from studies showing the accuracy of the technology compared to nuclear imaging, the current stand of care for myocardial perfusion imaging, and cardiac MRI.
GE Healthcare partnered with the AI developer Dia to provide an artificial intelligence algorithm to auto contour and calculate cardiac ejection fraction (EF). The app is now available on the GE Vscan pocket, point-of-care ultrasound (POCUS) system, as seen here displayed at RSNA 2019. Watch a VIDEO demo from RSNA.
This is an example of artificial intelligence automation for cardiac MRI using an AI app from TeraRecon's Envoy AI marketplace. The GE MRI image has been countoured, anatomy labeled and all quantification automated by the AI, greatly reducing post-processing time. See this and other new AI imaging technologies from the 2019 RSNA meeting.