Abstract
Abstract
Objective: Dual-energy (DE) imaging techniques using cone-beam computed tomography (CBCT) have applications for radiotherapy including material separation and quantification, improved contrast-to-noise ratios (CNR), and reduced metal artifacts. Traditional limitations of CBCT can be addressed by using HyperSight imaging on O-ring gantry radiotherapy units, however, DE imaging technologies have yet to be established for HyperSight. The objective of this study was to implement DE technology for HyperSight by developing a novel material specific imaging algorithm and adopting a virtual monoenergetic imaging (VMI) method.
Approach: Images of a calibration phantom with various material inserts, including concentrations of iodine and calcium, were obtained at 80 and 140 kVp. A novel material separation algorithm was developed using DE Hounsfield unit values with statistical classification via the Mahalanobis distance and confidence filtering to differentiate six material groups (lung, soft tissue, fat tissue, iodine, calcium, bone). The algorithm includes concentration/density mapping via interpolation between calibrated values and was evaluated using an anthropomorphic phantom. VMIs were developed using an established method based on weighted addition of high- and low-energy images to quantify CNR enhancement and metal artifact reduction.
Main Results: Iodine and calcium were separable for concentrations above 47 mg I/mL and 303 mg Ca/mL, whereas iodine and bone were separable above 25 mg I/mL and 1.4 g/cm3, respectively. Soft tissue, fat tissue, and lung demonstrated reasonable classification with accuracies of 94.6%, 91.7%, and 97.8%, respectively. At 75 keV, CNR improved by 52% for inner bone, 41% for liver, and 88% for exhale lung compared to the single-energy reference. At 70 keV, metal artifact reduction improved by 11% compared to the single-energy reference.
Significance: This study demonstrates the feasibility of DE CBCT technology on the HyperSight imaging system through a novel statistical material specific imaging algorithm and VMI-based CNR improvements and metal artifact reduction.