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Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for 4D Cone-Beam CT

J Wang

J Wang*, X Gu, UT Southwestern Medical Center, Dallas, TX

TH-C-103-2 Thursday 10:30AM - 12:30PM Room: 103

Purpose: Image reconstruction and motion model estimation in four dimensional cone-beam CT (4D-CBCT) are handled as two sequential steps conventionally. Due to limited number of projections at each phase, the image quality of 4D-CBCT is degraded by the view-aliasing artifacts and the accuracy of subsequent motion modeling is compromised by inferior 4D-CBCT. The objective of this work is to enhance the image quality of 4D-CBCT and accuracy of motion model estimation through developing a novel strategy that is able to perform Simultaneous Motion Estimation and Image Reconstruction (SMEIR).

Methods: The proposed SMEIR algorithm consists of two alternating steps: 1) model-based iterative image reconstruction to obtain motion-compensated CBCT (mCBCT) and 2) motion model estimation through the deformable registration of mCBCT and projections of individual phase of 4D-CBCT. The iterative reconstruction is based on the simultaneous algebraic reconstruction (SART) technique coupled with total variation regularization. During the forward- and back-projection of SART, all projection data are used for the reconstruction of mCBCT by utilizing the estimated deformable vector fields (DVF). The DVF is estimated by matching the forward projection of the deformed mCBCT and projections of other phases of 4D-CBCT. A 4D NCAT phantom is used to quantitatively evaluate the performance of SMEIR algorithm with comparison to FDK and total variation (TV) minimization algorithm.

Results: When all projections are used for image reconstruction by FDK, motion blurring artifacts are presented in 3D-CBCT, leading to 33.1% relative reconstruction error. View aliasing artifacts are presented in 4D-CBCT reconstructed by FDK, where the relative error is 46.2%. When TV minimization is used, the relative error is 19.8%. Image quality of 4D-CBCT is substantially improved by using the proposed SMEIR algorithm and relative error is reduced to 7.6%.

Conclusion: The proposed SMEIR algorithm is able to estimate motion model and reconstruct motion-compensated 4D-CBCT with high accuracy.

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