CT Radiation Doses Vary Among Countries

The amount of radiation used to perform CT scans varies considerably around the world and automated radiation-tracking software would help update existing references, according to a study published in the American Journal of Roentgenology.

Researchers from Switzerland and the United States sought to compare diagnostic reference levels from a local European CT dose registry with preexisting European and North American diagnostic reference levels.

Using radiation-tracking software, the researchers looked at data from 43,761 CT scans (means, medians, and interquartile ranges of volumetric CT dose index [CTDIvol] dose-length product [DLP], size-specific dose estimate, and effective dose values) obtained over the course of two years from eight CT scanners at six institutions.

The scans included examinations of the following:

• Head

• Paranasal sinuses

• Thorax

• Pulmonary angiogram

• Abdomen-pelvis

• Renal-colic

• Thorax-abdomen-pelvis

• Thoracoabdominal angiogram

The metrics from the registry were compared with diagnostic reference levels from Canada and California (published in 2015), the ACR dose index registry (2015), and national diagnostic reference levels from local CT dose registries in Switzerland (2010), the United Kingdom (2011), and Portugal (2015).

The results showed that the local registry had a lower 75th percentile CTDIvol for all protocols than did the individual internationally sourced data. Compared with their study, the ACR dose index registry had higher 75th percentile CTDIvol values by:

• 55% for head

• 240% for thorax

• 28% for abdomen-pelvis

• 42% for thorax-abdomen-pelvis

• 128% for pulmonary angiogram

• 138% for renal-colic

• 58% for paranasal sinus studies

The researchers concluded they had lower diagnostic reference level values than did existing European and North American diagnostic reference levels.

“Automated radiation-tracking software could be used to establish and update existing diagnostic reference levels because they are capable of analyzing large datasets meaningfully,” they wrote.