Purpose: To investigate the ability of split-filter dual-energy computed tomography (DECT) to enhance oropharyngeal tumor contrast and contrast-to-noise ratio (CNR) for radiation therapy treatment planning.
Methods: Split-filter DECT contrast enhanced images were acquired for twenty patients with oropharyngeal cancer. Virtual monoenergetic images (VMIs) were generated at energies of 40 keV and 50 keV and compared to virtual 120 kVp-equivalent images. Additional images were reconstructed with an iterative reconstruction technique (SAFIRE strength 3). Average contrast and contrast-to-noise ratios were calculated using ROIs placed in the gross-tumor volume (GTV) and the geniohyoid muscle.
Results: The VMIs increased the average GTV contrast from 4.44±10.0 HU for the 120 kVp-equivalent image to 42.0±32.9 HU (p=1x10⁻⁶) and 25.6±22.3 (p=1x10⁻⁶) for the 40 keV and 50 keV images, respectively. The 40 keV and 50 keV VMIs also demonstrated an increase in noise by 93% (p=4x10⁻¹²) and 46% (p=4x10⁻¹¹), respectively, relative to the 120 kVp-equivalent image. The average CNR values increased from 0.54±1.16 for the 120 kVp-equivalent image to 2.51±1.88 (p=1x10⁻⁶) for the 40 keV image and 1.99±1.64 (p=3x10⁻⁷) for the 50 keV image. The iterative reconstruction algorithm decreased noise in the VMIs by 19% (p=2x10⁻⁷) and 23% (p=3x10⁻¹⁰), and improved CNR by 26% (p=5x10⁻⁴) and 31% (p=8x10⁻⁵), in the 40 and 50 keV images, respectively.
Conclusion: Primary oropharyngeal tumor contrast and CNR were significantly improved using VMIs reconstructed from the split‐filter DECT technique, and the use of iterative reconstruction further improved the CNR by decreasing the noise in the image. This gain in tumor contrast has the potential to enable more accurate tumor vs normal tissue delineation for radiation therapy treatment planning.