Lonny Trestrail MS, Abstract Co-Author: Nothing to Disclose

Daniel Sanchez MS, Abstract Co-Author: Nothing to Disclose

Daniel John Sandoval, Abstract Co-Author: Nothing to Disclose

Philip H. Heintz PhD, Presenter: Nothing to Disclose

Shuang Luan PhD, Abstract Co-Author: Nothing to Disclose

Danny Z. Chen PhD, Abstract Co-Author: Nothing to Disclose

UNMH has successfully developed a network CR QA analysis program.

The wide use of digital radiography make it possible to automatically monitor the performance and implement quality assurance (QA). The first step toward automatic QA is data collection and performance monitoring. Most digital radiology vendors have some type of reject/repeat databases, but such databases are installed on different platforms scattered around the hospitals. To perform efficient data collection, one needs a QA program that allows cross-network and cross-platform data collection. To our best knowledge, there is no commercial solution to address this problem. An automated web-based QA program was developed using Perl language and runs on an Apache web server. The program currently targets Fuji CR workstations but can be extended to other digital imaging systems. It automatically accesses the QA database on each Fuji CR workstation in the background over the network and retrieves various QA statistics and data (e.g., rejected images). It also comes with a web-based user interface and can generate reports of various formats. It is user interactive and can display jpeg images of the rejected exposures.

The QA program has been up and running at the University of New Mexico Hospital (UNMH) since April 2008. UNMH has 25 Fuji CR QA stations connected to 17 CR Readers located in 7 different buildings. Over 100 x-ray technologists perform over 5000 CR radiographic procedures/month. The program proves to be very reliable and secure, and has been used on a regular basis by the supervising radiology managers to evaluate the reject rates and spot trouble areas.

Monthly QA reports show that: (1) On average 6.7% of the images are rejected (excluding mammography). (2) Chest, lower extremity, and pelvis radiographs have the highest reject rates at 27%, 20%, and 18%, respectively. (3) Mammography has the lowest rejection rates at 0.75%, down from 2.4% when digital mammography was first implemented. (4) Technologists have personal reject rates from 0% to 33%. (5) The average rejection rate for pediatric patients is lower at 6.2%. (6) The predominant reason for image rejection was improper positioning.

Trestrail, L, Sanchez, D, Sandoval, D, Heintz, P, Luan, S, Chen, D, A Web-based Automated QA Analysis Program for Digital Image Tracking.  Radiological Society of North America 2009 Scientific Assembly and Annual Meeting, November 29 - December 4, 2009 ,Chicago IL. http://archive.rsna.org/2009/8003509.html