Purpose: To optimize photon-counting dual-energy angiography.
Methods: An imaging phantom was developed to investigate the iodine signal-to-noise ratio (SNR) in dual-energy images produced using a cadmium telluride photon-counting x-ray detector. The detector is 750-μm thick with 100-μm pixels and analog charge summing for correction of charge sharing and is capable of counting photons in two energy bins. The phantom was a 30-cm water tank within which was embedded an iodine step wedge (16-48 mg/cm² of iodine). An aluminum slab was placed in the beam path to simulate attenuation due to bone. Images of the phantom were acquired at 120 kV with 1.2-mm of aluminum filtration and a tube current of 100mA, which yielded an entrance air kerma of 1.1 mGy. The energy threshold separating high and low energy images was varied from 40 keV to 110 keV by 10 keV. For each threshold, soft-tissue-suppressed dual-energy images were generated. The optimal threshold value separating the low and high energy images was determined from the iodine SNR in the dual-energy image.
Results: It was found that a threshold value of 70 keV optimized iodine SNR in soft-tissue-suppressed dual-energy images. This corresponded to 70% of detected photons in the low energy image. Dual-energy subtraction also reduced the contrast of the aluminum slab, which simulated bone.
Conclusion: For photon-counting dual-energy angiography implemented using a tube voltage of 120 kV, the energy threshold that optimizes iodine SNR for large patients (i.e. 30-cm water equivalent thickness) is approximately 70 keV, which corresponded to 70% of the total photons detected in the low-energy bin. The heat units required to produce such images are expected to be much less than those of kV-switching-based methods for matched patient entrance exposure. Future work will compare the SNR and tube loading of photon-counting, kV-switching and temporal subtraction approaches for x-ray angiography.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants Program, the Canadian Foundation for Innovation and the Ontario Research Fund. Sarah Aubert was supported by Ontario's Ministry of Colleges and Universities through the Queen Elizabeth II Graduate Scholarship in Science and Technology.