Local tomography based on grey model

This paper studied region-of-interest (ROI) problem of computed tomography. It is necessary to use almost local projection data to reduce radiation exposure or time in medical imaging when only a small part of the patient's body needs to be viewed. Improving the quality of reconstructed ROI and reducing radiation exposure are our aims. However, the traditional local tomography algorithm has difficulty in reconstructing the ROI due to significant truncation artifacts. In this paper, a new grey model based method is reported for ROI image reconstruction from truncated projection data. By using grey model, the proposed method can extrapolate the truncated projection data, and reconstruct the ROI from a set of its projections. As a result, about 75% of full projection data are saved, as compared with other traditional approachs, in reconstructing a local region of 32 pixels in radius in an image of 256x256 pixels. Experimental results show that the proposed method exhibits more saving in exposure as compared with other local tomography algorithms and results in better quality of the reconstructed image.

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