Disease specific intelligent pre-fetch and hanging protocol for diagnostic neuroradiology workstations

Clinical data sets for neuroradiological cases can be quite large. A typical brain tumor patient at UCLA will undergo 8-10 separate studies over a 2 year period, each study will produce 60-100 magnetic resonance (MR) images. Gathering and sorting through a patient s imaging events during the course of treatment can be both overwhelming and time consuming. The purpose of this research is to develop an intelligent pre-fetch and hanging protocol that automatically gathers the relevant prior examinations from a picture archiving, and communication systems (PACS) archive and sends the pertinent historical images to the diagnostic display station where the new examination is subsequently read out. The intelligent hanging protocol describes the type of layout and sequence for image display. We have developed a classification scheme to organize the pertinent patient information to selectively pre-fetch and intelligently present the images to review brain tumor cases for a diagnostic neuroradiology workstation.