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An Effective Dose Monitoring Program for Computed Radiography

J Johnson*, E Samei, O Christianson, D Bower, Duke University Medical Center, Durham, NC

SU-C-217A-2 Sunday 1:30:00 PM - 2:15:00 PM Room: 217A

Purpose: To develop a method for determining the effective dose to patients undergoing computed radiography examinations and incorporating this method into an automated dose monitoring program.

Methods: Images from computed radiography exams were pulled from PACS to get dicom header information. Exposure indicator values were converted to plate exposure using vendor specified relationships between exposure indicator and plate exposure. Plate exposure was converted to patient entrance skin exposure through experimentally determined transmission factors. Transmission factors were determined by placing an ion chamber 40 inches from the x-ray source with a grid directly in front of the ion chamber and lucite directly in front of the grid. This was repeated for different lucite thickness (15.7cm, 20.3cm, 24.7cm, 30.3cm, and 34.5cm), kVp (60, 80, 100, 120), field size (5x5, 10x10, and 14x14 in.), and grid ratio (none, 8:1, and 12:1). These data were fit to a surface using the following equation for each grid ratio and field size:
TF=(C1*–kVp—^2+C2*kVp+C0)*e^(-C4*thickness)
This equation was used to calculate transmission factors for patient exams using assumed kVp and thickness. Patient entrance skin exposure was converted to effective dose through Monte Carlo simulations through PCXMC using kVp and patient thickness typical of each exam for average adults.

Results: Examination of patient dose for different scans shows wide variability in the amount of radiation dose delivered to patients. For Chest exams, the dose range was from 0.002mSv to 0.3 mSv with a median dose of 0.044mSv. Philips had a median dose of 0.051mSv while Carestream had a median dose of 0.033mSv.

Conclusions: The results emphasize the importance of monitoring dose for computed radiography. This data can be used to compare dose across vendors, machines, and exam types. We can determine diagnostic reference levels, flag outliers, and potentially optimize image quality.

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