4D liver tumor localization using cone-beam projections and a biomechanical model

You Zhang, Michael R Folkert, Bin Li, Xiaokun Huang, Jeffrey J Meyer, Tsuicheng Chiu, Pam Lee, Joubin Nasehi Tehrani, Jing Cai, David Parsons, Xun Jia, Jing Wang

Research output: Contribution to journalArticle

Abstract

Purpose: To improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization. Methods/Materials: To solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step's accuracy is limited at low-contrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to fine-tune the intra-liver DVFs by finite element analysis-based biomechanical modeling. We evaluated Bio-CBCT-est's accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT ‘reference’ images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to simulation, we also performed a preliminary study to qualitatively evaluate the Bio-CBCT-est technique via clinically acquired cone beam projections. A quantitative study using an in-house deformable liver phantom was also performed. Results: Using 20 projections for image estimation, the average (±s.d.) DICE index increased from 0.48 ± 0.13 (by 2D-3D deformation) to 0.77 ± 0.08 (by Bio-CBCT-est), the average COME decreased from 7.7 ± 1.5 mm to 2.2 ± 1.2 mm, the average HD decreased from 10.6 ± 2.2 mm to 5.9 ± 2.0 mm, and the average CC increased from −0.004 ± 0.216 to 0.422 ± 0.206. The tumor/cyst trajectory solved by Bio-CBCT-est matched well with that manually obtained from 4D-CT reference images. Conclusions: Bio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.

LanguageEnglish (US)
JournalRadiotherapy and Oncology
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Cone-Beam Computed Tomography
Liver
Neoplasms
Cysts
Finite Element Analysis
Liver Neoplasms

Keywords

  • 2D-3D deformable registration
  • Biomechanical modeling
  • Liver CBCT
  • Liver tumor localization

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Radiology Nuclear Medicine and imaging

Cite this

4D liver tumor localization using cone-beam projections and a biomechanical model. / Zhang, You; Folkert, Michael R; Li, Bin; Huang, Xiaokun; Meyer, Jeffrey J; Chiu, Tsuicheng; Lee, Pam; Tehrani, Joubin Nasehi; Cai, Jing; Parsons, David; Jia, Xun; Wang, Jing.

In: Radiotherapy and Oncology, 01.01.2018.

Research output: Contribution to journalArticle

Zhang, You ; Folkert, Michael R ; Li, Bin ; Huang, Xiaokun ; Meyer, Jeffrey J ; Chiu, Tsuicheng ; Lee, Pam ; Tehrani, Joubin Nasehi ; Cai, Jing ; Parsons, David ; Jia, Xun ; Wang, Jing. / 4D liver tumor localization using cone-beam projections and a biomechanical model. In: Radiotherapy and Oncology. 2018.
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keywords = "2D-3D deformable registration, Biomechanical modeling, Liver CBCT, Liver tumor localization",
author = "You Zhang and Folkert, {Michael R} and Bin Li and Xiaokun Huang and Meyer, {Jeffrey J} and Tsuicheng Chiu and Pam Lee and Tehrani, {Joubin Nasehi} and Jing Cai and David Parsons and Xun Jia and Jing Wang",
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AU - Zhang, You

AU - Folkert, Michael R

AU - Li, Bin

AU - Huang, Xiaokun

AU - Meyer, Jeffrey J

AU - Chiu, Tsuicheng

AU - Lee, Pam

AU - Tehrani, Joubin Nasehi

AU - Cai, Jing

AU - Parsons, David

AU - Jia, Xun

AU - Wang, Jing

PY - 2018/1/1

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N2 - Purpose: To improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization. Methods/Materials: To solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step's accuracy is limited at low-contrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to fine-tune the intra-liver DVFs by finite element analysis-based biomechanical modeling. We evaluated Bio-CBCT-est's accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT ‘reference’ images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to simulation, we also performed a preliminary study to qualitatively evaluate the Bio-CBCT-est technique via clinically acquired cone beam projections. A quantitative study using an in-house deformable liver phantom was also performed. Results: Using 20 projections for image estimation, the average (±s.d.) DICE index increased from 0.48 ± 0.13 (by 2D-3D deformation) to 0.77 ± 0.08 (by Bio-CBCT-est), the average COME decreased from 7.7 ± 1.5 mm to 2.2 ± 1.2 mm, the average HD decreased from 10.6 ± 2.2 mm to 5.9 ± 2.0 mm, and the average CC increased from −0.004 ± 0.216 to 0.422 ± 0.206. The tumor/cyst trajectory solved by Bio-CBCT-est matched well with that manually obtained from 4D-CT reference images. Conclusions: Bio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.

AB - Purpose: To improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization. Methods/Materials: To solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step's accuracy is limited at low-contrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to fine-tune the intra-liver DVFs by finite element analysis-based biomechanical modeling. We evaluated Bio-CBCT-est's accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT ‘reference’ images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to simulation, we also performed a preliminary study to qualitatively evaluate the Bio-CBCT-est technique via clinically acquired cone beam projections. A quantitative study using an in-house deformable liver phantom was also performed. Results: Using 20 projections for image estimation, the average (±s.d.) DICE index increased from 0.48 ± 0.13 (by 2D-3D deformation) to 0.77 ± 0.08 (by Bio-CBCT-est), the average COME decreased from 7.7 ± 1.5 mm to 2.2 ± 1.2 mm, the average HD decreased from 10.6 ± 2.2 mm to 5.9 ± 2.0 mm, and the average CC increased from −0.004 ± 0.216 to 0.422 ± 0.206. The tumor/cyst trajectory solved by Bio-CBCT-est matched well with that manually obtained from 4D-CT reference images. Conclusions: Bio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.

KW - 2D-3D deformable registration

KW - Biomechanical modeling

KW - Liver CBCT

KW - Liver tumor localization

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