基于先验组分的多谱CT序列DCM融合算法研究

doi:10.3964/j.issn.1000-0593(2018)03-0968-05

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Abstract Multi-spectral CT imaging is to characterize the different components in the CT images of different spectral ranges. For more convenient displaying all components physical information in an image, it is necessary to study the fusion method of multi-spectral CT sequence. But the commonly used fusion methods, such as weighted average method and wavelet transform fusion method, are mainly for the optimization of image information. The physical properties of the components can not be expressed, so that the gray scale of the fused image without physical representation affects the quantitative detection of CT. A multi-spectral CT sequence DCM fusion algorithm based on priori components with physical characterization was presented. First, we got multi-spectrum projection sequence by Imaging method separated by energy spectrum filtering and the CT sequence with different energy spectrum can be obtained by TV-OSEM reconstruction algorithm. Second, the traditional DCM model and the improved DCM model were used to fuse the multi-spectral CT sequences. The traditional DCM model was strictly single energy, considering the non-strict monocular characteristic of the filtered energy spectrum. The fusion result can not accurately characterized all the components in the object sequence. To solve this problem, an improved DCM model was proposed. In the improved DCM model, a new voxel definition was selected and a metallic priori component was introduced as a reference substance in the multi-spectral CT sequence. The Prior component was used to calibrate other substances in the fusion results. Thus accurate distribution of each component in the CT sequence was achieved by calibration of the fusion results. Simulation result, the method can realize multi-spectral CT sequence fusion from the perspective of the physical representation. while satisfying the different components distinction of the CT sequence, the gray scale of its fused image had physical reference. This method is beneficial to the subsequent CT quantitative detection.

Key words：Multi-spectrum CT; CT sequence fusion; Data-constrained model; Prior component

Received: 2017-04-24 Accepted: 2017-09-12

ZTFLH:

TP391.4

Corresponding Authors: LI Yi-hong E-mail: zhao15903402586@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)03-0968-05 OR https://www.gpxygpfx.com/EN/Y2018/V38/I03/968