A segmentation-based method for metal artifact reduction.
OBJECTIVE
We propose a novel segmentation-based interpolation method to reduce the metal artifacts caused by surgical aneurysm clips.


METHODS
Our method consists of five steps: coarse image reconstruction, metallic object segmentation, forward-projection, projection interpolation, and final image reconstruction. The major innovations are 2-fold. First, a state-of-the-art mean-shift technique in the computer vision field is used to improve the accuracy of the metallic object segmentation. Second, a feedback strategy is developed in the interpolation step to adjust the interpolated value based on the prior knowledge that the interpolated values should not be larger than the original ones. Physical phantom and real patient datasets are studied to evaluate the efficacy of our method.


RESULTS
Compared to the state-of-the-art segmentation-based method designed previously, our method reduces the metal artifacts by 20-40% in terms of the standard deviation and provides more information for the assessment of soft tissues and osseous structures surrounding the surgical clips.


CONCLUSIONS
Mean shift technique and feedback strategy can help to improve the image quality in terms of reducing metal artifacts.
Yu
Hengyong
H
Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Science, Virginia Technical, Blacksburg, VA 24061, USA. hengyongyu@vt.edu
Zeng
Kai
K
Bharkhada
Deepak K
DK
Wang
Ge
G
Madsen
Mark T
MT
Saba
Osama
O
Policeni
Bruno
B
Howard
Matthew A
MA
Smoker
Wendy R K
WR
D000465:Algorithms; D016477:Artifacts; D006801:Humans; D008670:Metals; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D014057:Tomography, X-Ray Computed
D008670:Metals