Ke Lei1, Shreyas Vasanawala2, and John Pauly1
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States
We proposed a CNN model that automatically assesses image quality within seconds after a scan to reduce the number of patient recalls and inadequate images. Our model is deployed to the clinics where it alerts technicians to take action in real time for highly corrupted images.
Figure 2. Three samples of the nine image rulers in use. From top to bottom: for F/S elbow, hip, and F/S brain scans.
Figure 3. Plots shown to technicians on scanner. The red threshold line is chosen by radiologists, and the two class scores around it are defined as moderate for the pie chart.