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Proceedings Article

Adaptive noise reduction toward low-dose computed tomography

[+] Author Affiliations
Hongbing Lu

SUNY/Stony Brook (USA) and Fourth Military Medical Univ. (China)

Xiang Li, Lihong Li, Yuxiang Xing, Zhengrong Liang

SUNY/Stony Brook (USA)

Dongqing Chen

Viatronix Inc. (USA)

Jiang Hsieh

GE Medical Systems (USA)

Proc. SPIE 5030, Medical Imaging 2003: Physics of Medical Imaging, 759 (June 9, 2003); doi:10.1117/12.480374
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From Conference Volume 5030

  • Medical Imaging 2003: Physics of Medical Imaging
  • Martin J. Yaffe; Larry E. Antonuk
  • San Diego, CA | February 15, 2003

abstract

An efficient noise treatment scheme has been developed to achieve low-dose CT diagnosis based on currently available CT hardware and image reconstruction technologies. The scheme proposed includes two main parts: filtering in sinogram domain and smoothing in image domain. The acquired projection sinograms were first treated by our previously proposed Karhunen-Loeve (K-L) domain penalized weighted least-square (PWLS) filtering, which fully utilizes the prior statistical noise property and three-dimensional (3D) spatial information for an accurate restoration of the low-dose projections. To treat the streak artifacts due to photon starvation, we also incorporated an adaptive filtering into our PWLS framework, which selectively smoothes those channels contributing most to the streak artifacts. After the sinogram filtering, the image was reconstructed by the conventional filtered backprojection (FBP) method. The image is assumed as piecewise regions each has a unique texture. Therefore, an edge-preserving smoothing (EPS) with locally-adaptive parameters to the noise variation was applied for further noise reduction in image domain. Experimental phantom projections acquired by a GE spiral computed tomography (CT) scanner under 10 mAs tube current were used to evaluate the proposed smoothing scheme. The reconstructed imaged demonstrated that the smoothing scheme with appropriate control parameters provides a significant improvement on noise suppression without sacrificing the spatial resolution.

© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Hongbing Lu ; Xiang Li ; Lihong Li ; Dongqing Chen ; Yuxiang Xing, et al.
"Adaptive noise reduction toward low-dose computed tomography", Proc. SPIE 5030, Medical Imaging 2003: Physics of Medical Imaging, 759 (June 9, 2003); doi:10.1117/12.480374; http://dx.doi.org/10.1117/12.480374


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