Jing Wang1, Lei Zhu, Lei Xing. 1. Department of Radiation Oncology, Stanford University School of Medicine, CA 94305, USA. jingwang9@stanford.edu
Abstract
PURPOSE: To improve the quality of low-dose X-ray fluoroscopic images using statistics-based restoration algorithm so that the patient fluoroscopy can be performed with reduced radiation dose. METHOD AND MATERIALS: Noise in the low-dose fluoroscopy was suppressed by temporal and spatial filtering. The temporal correlation among neighboring frames was considered by the Karhunen-Loève (KL) transform (i.e., principal component analysis). After the KL transform, the selected neighboring frames of fluoroscopy were decomposed to uncorrelated and ordered principal components. For each KL component, a penalized weighted least-squares (PWLS) objective function was constructed to restore the ideal image. The penalty was chosen as anisotropic quadratic, and the penalty parameter in each KL component was inversely proportional to its corresponding eigenvalue. Smaller KL eigenvalue is associated with the KL component of lower signal-to-noise ratio (SNR), and a larger penalty parameter should be used for such KL component. The low-dose fluoroscopic images were acquired using a Varian Acuity simulator. A quality assurance phantom and an anthropomorphic chest phantom were used to evaluate the presented algorithm. RESULTS: In the images restored by the proposed KL domain PWLS algorithm, noise is greatly suppressed, whereas fine structures are well preserved. Average improvement rate of SNR is 75% among selected regions of interest. Comparison studies with traditional techniques, such as the mean and median filters, show that the proposed algorithm is advantageous in terms of structure preservation. CONCLUSIONS: The proposed noise reduction algorithm can significantly improve the quality of low-dose X-ray fluoroscopic image and allows for dose reduction in X-ray fluoroscopy.
PURPOSE: To improve the quality of low-dose X-ray fluoroscopic images using statistics-based restoration algorithm so that the patient fluoroscopy can be performed with reduced radiation dose. METHOD AND MATERIALS: Noise in the low-dose fluoroscopy was suppressed by temporal and spatial filtering. The temporal correlation among neighboring frames was considered by the Karhunen-Loève (KL) transform (i.e., principal component analysis). After the KL transform, the selected neighboring frames of fluoroscopy were decomposed to uncorrelated and ordered principal components. For each KL component, a penalized weighted least-squares (PWLS) objective function was constructed to restore the ideal image. The penalty was chosen as anisotropic quadratic, and the penalty parameter in each KL component was inversely proportional to its corresponding eigenvalue. Smaller KL eigenvalue is associated with the KL component of lower signal-to-noise ratio (SNR), and a larger penalty parameter should be used for such KL component. The low-dose fluoroscopic images were acquired using a Varian Acuity simulator. A quality assurance phantom and an anthropomorphic chest phantom were used to evaluate the presented algorithm. RESULTS: In the images restored by the proposed KL domain PWLS algorithm, noise is greatly suppressed, whereas fine structures are well preserved. Average improvement rate of SNR is 75% among selected regions of interest. Comparison studies with traditional techniques, such as the mean and median filters, show that the proposed algorithm is advantageous in terms of structure preservation. CONCLUSIONS: The proposed noise reduction algorithm can significantly improve the quality of low-dose X-ray fluoroscopic image and allows for dose reduction in X-ray fluoroscopy.