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Metal Artifact Reduction in CT Using Deformable Tissue-Class Modeling


Y Zhuge

Y Zhuge*, H Ning, J Cheng, B Arora, H Xie, R Miller, National Cancer Institute, Bethesda, MD

SU-E-I-51 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: The purpose of this work is to present a novel approach to reduce metal artifacts in CT using deformable tissue-class modeling. The tissue-class model is generated by combining information from both original corrupted image slice (original slice) with metal artifacts, and its neighboring image slice (reference slice) in the same scan, but without metal artifacts. Missing or corrupted information in the original slice is estimated from the reference slice.

Methods: The proposed method consists of four major steps. (1) Reference slice is deformed to the original slice using diffeomorphic demons registration algorithm. (2) Strong bright streak including metal objects, and dark streak artifacts are segmented respectively, by applying the basic connected threshold method on the difference image between the original and deformed reference image. (3) Pixel intensities of strong bright and dark streaks in original slice are replaced by those of corresponding pixels in deformed reference slice. The k-means clustering algorithm is then utilized to segment the original slice into four tissue classes: air, soft tissue, normal tissue, and bone. This tissue-class model is forward projected to produce a model sinogram. (4) Corrupted projection data in the sinogram of the original slice is substituted by corresponding segments in the model sinogram. The completed sinogram is then reconstructed with the filtered back-projection to produce the corrected image.

Results: The proposed method has been tested on clincal patient data with dental fillings, prostate fiducial markers. Both qualitative and quantitative analysis indicate that image quality has been improved considerably after correction, and the proposed method outperforms the standard linear-interpolation based method, and the method using tissue-class modelling on the original slice only.

Conclusion: A novel method for metal artifact reduction in CT has been developed. The method is capable of reducing bright and dark streaks caused by metal objects in CT.



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