Noise Reduction in Low-Dose X-Ray Fluoroscopy for Image-Guided Radiation Therapy

Jing Wang, Lei Zhu, Lei Xing

Research output: Contribution to journalArticle

15 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)637-643
Number of pages7
JournalInternational Journal of Radiation Oncology Biology Physics
Volume74
Issue number2
DOIs
StatePublished - Jun 1 2009

Fingerprint

Image-Guided Radiotherapy
fluoroscopy
Love
Fluoroscopy
noise reduction
Noise
radiation therapy
X-Rays
dosage
penalties
x rays
Signal-To-Noise Ratio
signal to noise ratios
eigenvalues
Least-Squares Analysis
acuity
spatial filtering
chest
assurance
principal components analysis

Keywords

  • Anisotropic penalty
  • Karhunen-Loève (KL) transform
  • Low-dose fluoroscopy
  • Noise reduction
  • Penalized weighted least-squares

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Radiation
  • Cancer Research

Cite this

Noise Reduction in Low-Dose X-Ray Fluoroscopy for Image-Guided Radiation Therapy. / Wang, Jing; Zhu, Lei; Xing, Lei.

In: International Journal of Radiation Oncology Biology Physics, Vol. 74, No. 2, 01.06.2009, p. 637-643.

Research output: Contribution to journalArticle

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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-Lo{\`e}ve (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.",
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