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Deformation Vector Fields (DVF)-Driven Image Reconstruction for 4D-CBCT

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J Dang

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

TU-G-141-6 Tuesday 4:30PM - 6:00PM Room: 141

Purpose: High-quality four dimensional cone-beam CT (4D-CBCT) can be obtained by deforming the planning CT in radiotherapy, where the deformation vector fields (DVF) is estimated by matching the forward projection of the deformed planning CT and measured 4D-CBCT projections. Due to the presence of scatter signal in CBCT projection images, the accuracy of DVF estimation is degraded when the sum of squared intensity difference (SSID) is used as the matching criterion. The goal of this work is to improve the estimation accuracy of DVF by using normalized correlation coefficient (NCC) as the matching criterion to obtain high quality on-treatment 4D-CBCT of lung cancer patients in radiation therapy.

Methods: The DVF used to deform the planning CT is estimated by maximizing the NCC between the projections of on-treatment 4D-CBCT and the forward projection of the deformed planning CT. A non-linear conjugate gradient (NLCG) algorithm was used for the optimization. To obtain better initial DVF for NLCG optimization, demons registration was first performed between planning CT and 4D-CBCT reconstructed by total variation (TV) minimization. A 4D NCAT phantom was used to quantitatively evaluate the performance of the algorithm, where the reconstruction error is calculated as the sum of squared difference between the reconstructed 4D-CBCT and the phantom image.

Results: NCC based DVF estimation improves the image reconstruction accuracy as compared to the results obtained using SSID criterion. The image reconstruction error of 4D-CBCT obtained by NCC criterion is 61.5% smaller than that is obtained by SSID criterion.

Conclusion: The accuracy DVF estimation and image reconstruction is substantially improved by using the NCC criterion, as compared to the results obtained by SSID criterion. High quality 4D-CBCT can be a valuable tool in image-guided radiation therapy for lung cancer patients.

Funding Support, Disclosures, and Conflict of Interest: Cancer Prevention and Research Institute of Texas (RP110562)

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