Room: Track 1
Purpose: To image multiple high-Z contrast agents using a table-top photon-counting CT (PCCT) system and to determine the system parameters that give optimal contrast separation as well as accurately quantify contrast concentration.
Methods: A small cylindrical phantom with vials of 1%-5% contrast solutions of gadolinium, dysprosium, lutetium, and gold was imaged on a table-top PCCT system employing a high-flux photon-counting cadmium zinc telluride (CZT) detector with six energy bins. The phantom was scanned using a cone-beam setup at 120 kVp with various filters and tube currents ranging from 1.00-4.75 mA. Three system parameters were varied experimentally: 1) filter type and thickness, 2) projection acquisition time, and 3) energy bin width. Contrast agents were separated using K-edge subtraction after PCCT image reconstruction and normalized based on the highest known 5% concentration. The results were analyzed and compared based on K-edge image contrast to noise ratio (CNR) or noise.
Results: For gadolinium (Z=64), K-edge CNR decreased from 22 to 16 with increasing filtration from 2 mm aluminium to 1 mm copper. However, the K-edge CNR for gold (Z=79) increased from 29 to 34. Decreasing acquisition time from 1.0s to 0.1s lowered K-edge CNR for all contrast agents, with gadolinium decreasing from 13 to 6 and gold from 19 to 8. K-edge CNR was maximized for gadolinium at a bin width of 11.0 keV, and for gold at 16.4 keV. Contrast concentration was accurately determined within ~0.13% or less for all contrast agents.
Conclusion: The presented results demonstrate the ability of PCCT to both separate multiple high-Z contrast agents using K-edge subtraction and quantify contrast concentration in K-edge images. Based on this work, the potential for use of PCCT in preclinical studies and in the clinic shows promise. A contrast-specific set of optimal PCCT imaging parameters has been compiled.
Funding Support, Disclosures, and Conflict of Interest: This work was partly funded by NSERC Engage and Engage Plus grants, NSERC CGSM, NSERC Discovery grant, Canada Foundation for Innovation, British Columbia Knowledge Development Fund and the Canada Research Chair program. Kris Iniewski is an employee of Redlen Technologies.
Not Applicable / None Entered.