Application of Statistical Process Control to Long Term CT Constancy Testing
J Fredriksson1,2*, H Olafsdottir1,2, A Kristbjornsson1,2, A Healy1,3, J Levy1,3, D Goodenough1,4, S Kristinsson1,2, (1) Image Owl, Salem, NY, (2) Raförninn, Reykjavik, Iceland, (3) Phantom Laboratory, Salem, NY, (4) George Washington University, Washington, DCSU-C-217BCD-4 Sunday 1:30:00 PM - 2:15:00 PM Room: 217BCD
To validate how a reliable Statistical Process Control can be implemented, using routine scanning of a test phantom and automatic QA program, to establish effective long term performance monitoring of CT imaging systems.
A data set of 21474 QA images acquired on 1565 distinct days was collected on a CT system from 2002 until 2011, during a large population based quantitative imaging study with a longitudinal component, using the Catphan® 500 phantom. The image data was processed with the ImageOwl Catphan®QA software. Tolerances for CT imaging quality parameters were selected from IEC-61223-3-5 and other sources. Process capability, Cp, describes the ratio of tolerance range to observed standard deviation. Cpk additionally weighs in closeness to one side of specification limits. A process capability equal to 1 means 99.7% chance of correctly observing a single measurement within specification, i.e. 3 sigma.
While slice thickness measurement can be an extremely reliable process (Cp=4.5, 5.1), image uniformity at standard +/-4HU limits, though generally reliable (Cp=2.1), showed relatively high probability of reporting false positive system out of specification (Cpk=0.8), due to measurements being near lower limit. Tube aging is manifested by changes in effective energy, causing changes in readings of sensitometric targets. This is confirmed by an effective energy value reset at tube renewal.
By monitoring modern CT scanners with today's test systems using commonly accepted control limits, reliable go/no-go signals can be generated for most CT QA variables. However, physicists implementing a monitoring system have to pay special attention to low process capability caveats when establishing monitoring action rules. This is especially important when implementing monitoring of large scale clinical or research operations requiring high reliability and cost effective QA solutions.