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Iterative Reconstruction Via Prior Image Constrained Total Generalized Variation for Spectral CT

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S Niu

S Niu1*, Y Zhang2 , J Ma3 , J Wang4 , (1) ,,,(2) UT Southwestern Medical Ctr at Dallas, Dallas, TX, (3) Southern Medical University, Guangzhou, Guangdong, (4) UT Southwestern Medical Center, Dallas, TX


WE-FG-207B-5 (Wednesday, August 3, 2016) 1:45 PM - 3:45 PM Room: 207B

Purpose:To investigate iterative reconstruction via prior image constrained total generalized variation (PICTGV) for spectral computed tomography (CT) using fewer projections while achieving greater image quality.

Methods:The proposed PICTGV method is formulated as an optimization problem, which balances the data fidelity and prior image constrained total generalized variation of reconstructed images in one framework. The PICTGV method is based on structure correlations among images in the energy domain and high-quality images to guide the reconstruction of energy-specific images. In PICTGV method, the high-quality image is reconstructed from all detector-collected X-ray signals and is referred as the broad-spectrum image. Distinct from the existing reconstruction methods applied on the images with first order derivative, the higher order derivative of the images is incorporated into the PICTGV method. An alternating optimization algorithm is used to minimize the PICTGV objective function. We evaluate the performance of PICTGV on noise and artifacts suppressing using phantom studies and compare the method with the conventional filtered back-projection method as well as TGV based method without prior image.

Results:On the digital phantom, the proposed method outperforms the existing TGV method in terms of the noise reduction, artifacts suppression, and edge detail preservation. Compared to that obtained by the TGV based method without prior image, the relative root mean square error in the images reconstructed by the proposed method is reduced by over 20%.

Conclusion:The authors propose an iterative reconstruction via prior image constrained total generalize variation for spectral CT. Also, we have developed an alternating optimization algorithm and numerically demonstrated the merits of our approach. Results show that the proposed PICTGV method outperforms the TGV method for spectral CT.

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