Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II

An adaptive approach

Hao Zhang, Jianhua Ma, Jing Wang, Yan Liu, Hao Han, Hongbing Lu, William Moore, Zhengrong Liang

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

To reduce radiation dose in X-ray computed tomography (CT) imaging, one common strategy is to lower the tube current and exposure time settings during projection data acquisition. However, this strategy would inevitably increase the projection data noise, and the resulting image by the conventional filtered back-projection (FBP) method may suffer from excessive noise and streak artifacts. The well-known edge-preserving nonlocal means (NLM) filtering can reduce the noise-induced artifacts in the FBP reconstructed image, but it sometimes cannot completely eliminate the artifacts, especially under the very low-dose circumstance when the image is severely degraded. Instead of taking NLM filtering, we proposed a NLM-regularized statistical image reconstruction scheme, which can effectively suppress the noise-induced artifacts and significantly improve the reconstructed image quality. From our previous investigation on NLM-based strategy, we noted that using a spatially invariant filtering parameter in the regularization was rarely optimal for the entire field of view (FOV). Therefore, in this study we developed a novel strategy for designing spatially variant filtering parameters which are adaptive to the local characteristics of the image to be reconstructed. This adaptive NLM-regularized statistical image reconstruction method was evaluated with low-contrast phantoms and clinical patient data to show (1) the necessity in introducing the spatial adaptivity and (2) the efficacy of the adaptivity in achieving superiority in reconstructing CT images from low-dose acquisitions.

Original languageEnglish (US)
Pages (from-to)26-35
Number of pages10
JournalComputerized Medical Imaging and Graphics
Volume43
DOIs
StatePublished - Jul 1 2015

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Computer-Assisted Image Processing
Image reconstruction
Artifacts
Tomography
Noise
X Ray Computed Tomography
Image quality
Dosimetry
Data acquisition
Radiation
Imaging techniques
X rays

Keywords

  • Adaptive nonlocal means
  • Low-dose
  • Statistical image reconstruction
  • X-ray CT

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

Cite this

Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II : An adaptive approach. / Zhang, Hao; Ma, Jianhua; Wang, Jing; Liu, Yan; Han, Hao; Lu, Hongbing; Moore, William; Liang, Zhengrong.

In: Computerized Medical Imaging and Graphics, Vol. 43, 01.07.2015, p. 26-35.

Research output: Contribution to journalArticle

Zhang, Hao ; Ma, Jianhua ; Wang, Jing ; Liu, Yan ; Han, Hao ; Lu, Hongbing ; Moore, William ; Liang, Zhengrong. / Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II : An adaptive approach. In: Computerized Medical Imaging and Graphics. 2015 ; Vol. 43. pp. 26-35.
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