Dual energy (DE) imaging consists of obtaining kilovoltage (kV) x-ray images at two different diagnostic energies and performing a weighted subtraction of these images. A third image is then produced that highlights soft tissue. DE imaging has been used by radiologists to aid in the detection of lung malignancies. However, it has not been used clinically in radiotherapy. The goal of this study is to assess the feasibility of performing DE imaging using a commercial on-board imaging system. Both a simple and an anthropomorphic phantom were constructed for this analysis. Planar kV images of the phantoms were obtained using varied imaging energies and mAs. Software was written to perform DE subtraction using empirically determined weighting factors. Tumor detectability was assessed quantitatively using the signal-difference-tonoise ratio (SDNR). Overall DE subtraction suppressed high density objects in both phantoms. The optimal imaging technique, providing the largest SDNR with a dose less than our reference technique was 140 kVp, 1.0 mAs and 60 kVp, 3.2 mAs. Based on this analysis, DE subtraction imaging is feasible using a commercial on-board imaging system and may improve the visualization of tumors in lung cancer patients undergoing image-guided radiotherapy.

This study illustrates the feasibility of performing DE imaging at oblique gantry angles using a clinical on-board imaging system. Incorporating DE imaging into clinical practice may allow for verification of tumor position at oblique gantry angles, and may facilitate the development of markerless motion tracking techniques. Supported by a grant from Varian Medical Systems.

To determine the effect of megavoltage (MV) scatter on the accuracy of markerless tumor tracking (MTT) for lung tumors using dual energy (DE) imaging and to consider a post-processing technique to mitigate the effects of MV scatter on DE-MTT. Methods: A Varian TrueBeam linac was used to acquire a series of interleaved 60/120 kVp images of a motion phantom with simulated tumors (10 and 15 mm diameter). Two sets of consecutive high/low energy projections were acquired, with and without MV beam delivery. The MV field sizes (FS) ranged from 2 × 2 cm 2 -6 × 6 cm 2 in steps of 1 × 1 cm 2 . Weighted logarithmic subtraction was performed on sequential images to produce soft-tissue images for kV only (DE kV ) and kV with MV beam on (DE kV+MV ). Wavelet and fast Fourier transformation filtering (wavelet-FFT) was used to remove stripe noise introduced by MV scatter in the DE images (DE Corr kV+MV ). A template-based matching algorithm was then used to track the target on DE kV, DE kV+MV ,and DE Corr kV+MV images.Tracking accuracy was evaluated using the tracking success rate (TSR) and mean absolute error (MAE). Results: For the 10 and 15 mm targets, the TSR for DE kV images was 98.7% and 100%, and MAE was 0.53 and 0.42 mm, respectively. For the 10 mm target, the TSR, including the effects of MV scatter, ranged from 86.5% (2 × 2 cm 2 ) to 69.4% (6 × 6 cm 2 ), while the MAE ranged from 2.05 mm to 4.04 mm. The application of wavelet-FFT algorithm to remove stripe noise (DE Corr kV+MV ) resulted in TSR values of 96.9% (2 × 2 cm 2 ) to 93.4% (6 × 6 cm 2 ) and subsequent MAE values were 0.89 mm to 1.37 mm. Similar trends were observed for the 15 mm target. Conclusion: MV scatter significantly impacts the tracking accuracy of lung tumors using DE images. Wavelet-FFT filtering can improve the accuracy of DE-MTT during treatment.

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A Fourier Transform (FT) based pattern-matching algorithm was adapted for use in medical image registration. This algorithm obtained the FT of two images, determined the normalized cross-power spectrum of the transformed images, and then applied an inverse FT. The result was a delta function with a maximum value at the location corresponding to the distance between the two images; a similar method was used to recover rotations. This algorithm was first tested using a simple two-dimensional image, with induced shifts of ±20 pixels and ±10 degrees. All translations were recovered with no error and all rotations were recovered within 0.18 degrees. Subsequently, this algorithm was tested on eight clinical kV images drawn from four different body sites. Twenty-five random shifts and rotations were applied to each image. The average mean error of the registration solution was −0.002 ± 0.077 mm in the x direction, 0.002 ± 0.075 mm in the y direction, and −0.012 ± 0.099 degrees. These initial results suggest that a FT algorithm has a high degree of accuracy when registering clinical kV images.