Accessing image resolution and frame rate effects in radiology from a human and a machine point of view

In teleradiology a vast amount of medical images is sent from one location to another location. If the network infrastructure between the locations is poor, users experience long download times or, if a client application is used, application lags. To solve this issue lossless compression algorithms can be used as a first option. Unfortunately these algorithms can only compress the data to a certain degree which is most of the time not enough for the heavy requirements in teleradiology. As a second option the image data can be compressed lossily by reducing the image quality. This however can have an impact on the work of the user and also on image processing tools, when the images are post-processed. In this contribution we give a first impression of frame rate and resolution effects on the work of both, humans and machines, using the example of tumor diagnosis.