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Deformation vector fields (DVF)-driven image reconstruction for 4D-CBCT

Deformation vector fields (DVF)-driven image reconstruction for 4D-CBCT BACKGROUND: High quality 4D-CBCT can be obtained by deforming a planning CT (pCT), where the deformation vector fields (DVF) are estimated by matching the forward projections of pCT and 4D-CBCT projections. The matching metric used in the previous study is the sum of squared intensity differences (SSID). The scatter signal level in CBCT projections is much higher than pCT, the SSID metric may not lead to optimal DVF. OBJECTIVE: To improve the DVF estimation accuracy, we develop a new matching metric that is less sensitive to the intensity level difference caused by the scatter signal. METHODS: The negative logarithm of correlation coefficient (NLCC) is used as the matching metric. A non-linear conjugate gradient optimization algorithm is used to estimate the DVF. A 4D NCAT phantom and an anthropomorphic thoracic phantom were used to evaluate the NLCC-based algorithm. RESULTS: In the NCAT phantom study, the relative reconstruction error is reduced from 18.0% in SSID to 14.13% in NLCC. In the thoracic phantom study, the root mean square error of the tumor motion is reduced from 1.16 mm in SSID to 0.43 mm in NLCC. CONCLUSION: NLCC metric can improve the image reconstruction and motion estimation accuracy of DVF-driven image reconstruction for 4D-CBCT. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of X-Ray Science and Technology IOS Press

Deformation vector fields (DVF)-driven image reconstruction for 4D-CBCT

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References (43)

Publisher
IOS Press
Copyright
Copyright © 2015 by IOS Press, Inc
ISSN
0895-3996
eISSN
1095-9114
DOI
10.3233/XST-140466
pmid
25567403
Publisher site
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Abstract

BACKGROUND: High quality 4D-CBCT can be obtained by deforming a planning CT (pCT), where the deformation vector fields (DVF) are estimated by matching the forward projections of pCT and 4D-CBCT projections. The matching metric used in the previous study is the sum of squared intensity differences (SSID). The scatter signal level in CBCT projections is much higher than pCT, the SSID metric may not lead to optimal DVF. OBJECTIVE: To improve the DVF estimation accuracy, we develop a new matching metric that is less sensitive to the intensity level difference caused by the scatter signal. METHODS: The negative logarithm of correlation coefficient (NLCC) is used as the matching metric. A non-linear conjugate gradient optimization algorithm is used to estimate the DVF. A 4D NCAT phantom and an anthropomorphic thoracic phantom were used to evaluate the NLCC-based algorithm. RESULTS: In the NCAT phantom study, the relative reconstruction error is reduced from 18.0% in SSID to 14.13% in NLCC. In the thoracic phantom study, the root mean square error of the tumor motion is reduced from 1.16 mm in SSID to 0.43 mm in NLCC. CONCLUSION: NLCC metric can improve the image reconstruction and motion estimation accuracy of DVF-driven image reconstruction for 4D-CBCT.

Journal

Journal of X-Ray Science and Technology – IOS Press

Published: Jan 1, 2015

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