Optimization-based image reconstruction regularized with inter-spectral structural similarity for limited-angle dual-energy cone-beam CT.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiation Oncology, Emory University, 100 Woodruff Circle, Atlanta, Georgia, 30322-1007, UNITED STATES.
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, New York, 10065, UNITED STATES.
- Department of Radiology and Sciences Imaging Department of Radiology Oncology, Emory University, 100 Woodruff Circle, Atlanta, Georgia, 30322-1007, UNITED STATES.
- Department of Radiology Oncology, Emory University, 1365 Clifton RD NE, Atlanta, Georgia, 30322-1007, UNITED STATES.
- Radiation Oncology, Emory University School of Medicine, 1365 Clifton Rd NE Building C, Atlanta, Georgia, 30303-3073, UNITED STATES.
- Department of Radiology and Imaging Sciences, Emory University, Emory Clinic Building C Suite 5018, 1701 Uppergate Drive, Atlanta, Georgia, 30322, UNITED STATES.
- Department of Radiology Oncology, Emory University, Clifton Rd, Atlanta, Georgia, 30322-1007, UNITED STATES.
Abstract

Limited-angle dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, its clinical implementations are hindered by the challenging image reconstruction from limited-angle projections. While optimization-based and deep learning-based methods have been proposed for image reconstruction, their utilization is limited by the requirement for X-ray spectra measurement or paired datasets for model training. This work aims to facilitate the clinical applications of fast and low-dose DE-CBCT by developing a practical solution for image reconstruction in limited-angle DE-CBCT.
Methods:
An inter-spectral structural similarity-based regularization was integrated into the iterative image reconstruction in limited-angle DE-CBCT. By enforcing the similarity between the DE images, limited-angle artifacts were efficiently reduced in the reconstructed DECBCT images. The proposed method was evaluated using two physical phantoms and three digital phantoms, demonstrating its efficacy in quantitative DECBCT imaging.
Results:
In all the studies, the proposed method achieves accurate image reconstruction without visible residual artifacts from limited-angle DE-CBCT projection data. In the digital phantom studies, the proposed method reduces the mean-absolute-error (MAE) from 309/290 HU to 14/20 HU, increases the peak signal-to-noise ratio (PSNR) from 40/39 dB to 70/67 dB, and improves the structural similarity index measurement (SSIM) from 0.74/0.72 to 1.00/1.00.
Conclusions:
The proposed method achieves accurate optimization-based image reconstruction in limited-angle DE-CBCT, showing great practical value in clinical implementations of limited-angle DE-CBCT.
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