Opportunistic Screening With CT: Comparison of Phantomless BMD Calibration Methods

Opportunistic screening is a new promising technique to identify individuals at high risk for osteoporotic fracture using computed tomography (CT) scans originally acquired for an clinical purpose unrelated to osteoporosis. In these CT scans, a calibration phantom traditionally required to convert measured CT values to bone mineral density (BMD) is missing. As an alternative, phantomless calibration has been developed. This study aimed to review the principles of four existing phantomless calibration methods and to compare their performance against the gold standard of simultaneous calibration (ΔBMD). All methods were applied to a dataset of 350 females scanned with a highly standardized CT protocol (DS1) and to a second dataset of 114 patients (38 female) from clinical routine covering a large range of CT acquisition and reconstruction parameters (DS2). Three of the phantomless calibration methods must be precalibrated with a reference dataset containing a calibration phantom. Sixty scans from DS1 and 57 from DS2 were randomly selected for this precalibration. For each phantomless calibration method first the best combination of internal reference materials (IMs) was selected. These were either air and blood or subcutaneous adipose tissue, blood, and cortical bone. In addition, for phantomless calibration a fifth method based on average calibration parameters derived from the reference dataset was applied. For DS1, ΔBMD results (mean (Formula presented.) standard deviation) for the phantomless calibration methods requiring a precalibration ranged from 0.1 (Formula presented.) 2.7 mg/cm³ to 2.4 (Formula presented.) 3.5 mg/cm³ with similar means but significantly higher standard deviations for DS2. Performance of the phantomless calibration method, which does not require a precalibration was worse (ΔBMD DS1: 12.6 (Formula presented.) 13.2 mg/cm³, DS2: 0.5 (Formula presented.) 8.8 mg/cm³). In conclusion, phantomless BMD calibration performs well if precalibrated with a reference dataset.