Fifty-eighth annual meeting of the american association of physicists in medicine
TU-H-207A-05: Automated Early Identification of An Excessive Air-In-Oil X-Ray Tube Artifact That Mimics Acute Cerebral Infarct
There is an infrequent but serious CT artifact that occurs when there is too much air in the cooling oil of an x-ray tube. This artifact manifests as patchy hypodensities and mimics acute cerebral infarct. Routine quality control testing is unlikely to detect this artifact before it is observed in patient images. The purpose of this project was to develop an automated, quantitative method that increased the likelihood of identifying and preventing such artifacts.
Using QC phantom images with a known air-in-oil artifact, a 1D radial representation of the 2D noise power spectrum(NPS) was calculated and compared against that for artifact-free images. The QC program software used at our institution to analyze daily phantom images was modified to include measuring the average frequency of NPS within the water section of daily phantom scans. The threshold values developed for each CT system were incorporated into our daily QC program and email notification system.
The NPS for the known air-in-oil artifact images included a large low frequency peak compared with artifact-free images; the average NPS frequency for these images were 0.197 and 0.319 (1/mm), respectively. The average NPS frequency values (mean+/− standard deviation) for the GE CT750, GE VCT, GE Lightspeed Xtra, and Siemens SOMATOM Definition Flash scanners were 0.322+/− 0.0058, 0.324+/−0.0024, 0.320+/−0.0020, and 0.303+/−0.0039 (1/mm), respectively. Threshold values were chosen to be the average plus or minus twice the standard deviation. Automated QC successfully identified an air-in-oil artifact in the Lightspeed Xtra before any detrimental clinical effect occurred; the average NPS frequency value that triggered service was 0.307, which is six standard deviations smaller than average.
Clinically serious problems associated with the air-in-oil artifact can be detected earlier and mitigated/avoided by incorporating the average frequency of NPS measurements of daily phantom images into an automated QC program.