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Multiple Penalties with Different Orders for Structure Adaptive CBCT Reconstruction

S Tan

Q Shi1, P Cheng1 , J Wang2, S Tan1 , S Tan1*, (1) Huazhong University of Science and Technology, Wuhan, (2) UT Southwestern Medical Center, Dallas, TX


MO-FG-CAMPUS-IeP2-4 (Monday, August 1, 2016) 5:00 PM - 5:30 PM Room: ePoster Theater

To combine total variation (TV) and Hessian penalty in a structure adaptive way for cone-beam CT (CBCT) reconstruction.

TV is a widely used first order penalty with good ability in suppressing noise and preserving edges, but leads to the staircase effect in regions with smooth intensity transition. The second order Hessian penalty can effectively suppress the staircase effect with extra cost of blurring object edges. To take the best of both penalties, we proposed a novel method to combine both for CBCT reconstruction in a structure adaptive way. The proposed method adaptively determined the weight of each penalty according to the geometry of local regions. An specially-designed exponent term with image gradient involved was used to characterize the local geometry such that the weights for Hessian and TV were 1 and 0, respectively, at uniform local regions, and 0 and 1 at edge regions. For other local regions, the weights varied from 0 to 1. The objective functional was minimized using the majorzation-minimization approach. We evaluated the proposed method on a modified 3D shepp-logan and a CatPhan 600 phantom. The full-width-at-half-maximum (FWHM) and contrast-to-noise (CNR) were calculated.

For 3D shepp-logan, the reconstructed images using TV had an obvious staircase effect, while those using the proposed method and Hessian preserved the smooth transition regions well. FWHMs of the proposed method, TV and Hessian penalty were 1.75, 1.61 and 3.16, respectively, indicating that both TV and the proposed method is able to preserve edges. For CatPhan 600, CNR values of the proposed method were similar to those of TV and Hessian.

The proposed method retains favorable properties of TV like preserving edges and also has the ability in better preserving gradual transition structure as Hessian does. All methods performs similarly in suppressing noise.

Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by National Natural Science Foundation of China (NNSFC), under Grant Nos.60971112 and 61375018, grants from the Cancer Prevention and Research Institute of Texas (RP130109 and RP110562-P2), National Institute of Biomedical Imaging and Bioengineering (R01 EB020366) and a grant from the American Cancer Society (RSG-13-326-01-CCE).

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