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Cone-Beam CT Reconstruction with Deformed Prior Image

J Wang

H Zhang1 , L Ouyang2 , J Huang1 , J Ma1 , W Chen1 , J Wang2*, (1) Southern Medical University, Guangzhou, GuangDong, (2) UT Southwestern Medical Center, Dallas, Texas,


WE-G-18A-5 Wednesday 4:30PM - 6:00PM Room: 18A

Purpose:Prior image can be incorporated into image reconstruction process to improve the quality of on-treatment cone-beam CT (CBCT) from sparse-view or low-dose projections. However, the deformation between the prior image and on-treatment CBCT are not considered in current prior image based reconstructions (e.g., prior image constrained compressed sensing (PICCS)). The purpose of this work is to develop a deformed-prior-image-based-reconstruction strategy (DPIR) to address the mismatch problem between the prior image and target image.

Methods:The deformed prior image is obtained by a projection based registration approach. Specifically, the deformation vector fields (DVF) used to deform the prior image is estimated through matching the forward projection of the prior image and the measured on-treatment projection. The deformed prior image is then used as the prior image in the standard PICCS algorithm. Simulation studies on the XCAT phantom was conducted to evaluate the performance of the projection based registration procedure and the proposed DPIR strategy.

Results:The deformed prior image matches the geometry of on-treatment CBCT closer as compared to the original prior image. Using the deformed prior image, the quality of the image reconstructed by DPIR from few-view projection data is greatly improved as compared to the standard PICCS algorithm. The relative image reconstruction error is reduced to 11.13% in the proposed DPIR from 17.57% in the original PICCS.

Conclusion:The proposed DPIR approach can solve the mismatch problem between the prior image and target image, which overcomes the limitation of the original PICCS algorithm for CBCT reconstruction from sparse-view or low-dose projections.

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