Ultrasound Imaging

Getty Images

Getty Images

GE Healthcare Vscan with artificial intelligence (AI) automated left ventricular ejection fraction assessment with the Dia LVivo EF app.

A and B, Lung ultrasound images obtained with convex (A) and linear (B) probes. Multiple confluent B-lines (arrows), patchy pulmonary consolidation (asterisk, B), and thickened pleural line (between arrowheads, A) are visualized. C, Chest CT image shows reticular and interlobular septal thickening and patchy, focal opacities associated with architectural distortion. This patient was classified in critical group and was assigned to severe group for statistical analysis.

Volume flow as a function of color flow gain (at a single testing site). For each row the color flow c-plane and the computed volume flow are shown as a function of color flow gain. The c-plane is shown for four representative gain levels, whereas the computed volume flow is shown for 12–17 steps across the available gain settings. Flow was computed with (solid circles on the graphs) and without (hollow circles on the graphs) partial volume correction. Partial volume correction accounts for pixels that are only partially inside the lumen. Therefore, high gain (ie, blooming) does not result in overestimation of flow. Systems 1 and 2 converge to true flow after the lumen is filled with color pixel. System 3 is nearly constant regarding gain and underestimates the flow by approximately 17%. Shown are mean flow estimated from 20 volumes, and the error bars show standard deviation. Image courtesy of the journal Radiology