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

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.

Keywords: 2D-3D deformable registration; Biomechanical modeling; Liver CBCT; Liver tumor localization.

Figure 1:

(A). General scheme of the intensity-driven 2D-3D deformation technique. The DVF is iteratively updated so that the DRRs of the deformed I_prior (I_new ) will match the cone-beam projections in the intensity domain. (B). Workflow of the liver biomechanical modeling. A liver tetrahedral mesh is generated from the liver contour. The liver boundary DVF solved by 2D-3D deformation is then used as the boundary conditions to drive the finite element analysis of the liver tetrahedral mesh to solve biomechanically-corrected intra-liver DVF.

Figure 2.

(A). Anterior view of the soft, elastic liver phantom. (B). Posterior view of the liver phantom. (C). A hard body shell to house the liver. (D). The in-house motion platform. (E). The whole motion-enabled deformable liver phantom platform.

Figure 3:

From left to right: three-view slice cuts of the prior CT image at phase 0%, the CBCT image reconstructed by the clinical Feldkamp-Davis-Kress (FDK) algorithm at phase 50%, the CBCT image estimated by 2D-3D deformation at phase 50%, the CBCT image estimated by Bio-CBCT-est at phase 50%, and the “target” CT reference image at phase 50%. The CBCT reconstructions/estimations used 20 Monte-Carlo projections simulated from the “target” CT reference image. The arrows point to the tumor regions. The images are from (a) patient 3 and (b) patient 4.

Figure 4:

(A)-(D). Plots of the DICE, COME, HD, and CC metrics. Results of two methods (2D-3D deformation and Bio-CBCT-est) are shown for patients 1–4 (patient 4 has two image sets) at different phases (10%−90%), by using 20 Monte-Carlo projections simulated from 4D-CT. (E)-(H). Boxplots of the DICE, COME, HD, and CC metrics. Results of two methods (2D-3D deformation and Bio-CBCT-est) are shown for patients 1–7 at phase 50%, by using 5, 10, or 20 Monte-Carlo projections simulated from the “target” CT reference image at phase 50%.

Figure 5:

(A). Comparison of tumor/cyst centroid locations at different phases between manual localization (based on manually contoured tumors on 4D-CT reference images), automatic localization by 2D-3D deformation solved DVFs, and automatic localization by Bio-CBCT-est solved DVFs. The results are for patients 1 and 4 (rows 1–3), using 20 Monte-Carlo projections simulated from 4D-CT for automatic localization. Patient 4 has two 4D-CT sets: Pa4–1 shows the results of intra-scan localization, and Pa4–2 shows the results of inter-scan localization. (B). Comparison of tumor centroid locations at different phases between manual localization on simulation 4D-CT, automatic localization by 2D-3D deformation solved DVFs, and automatic localization by Bio-CBCT-est solved DVFs. Clinically acquired projections of Patient 4 were used for automatic localization. The manual localization was based on the 4D-CT set of Pa4–2.

Figure 6.

Boxplots of Euclidean errors of solved intra-liver DVFs for (A). phase 25% and (B). phase 50% of the liver phantom by different techniques, as indicated by (a-g) in each subfigure. Each boxplot contains results of 9 BBs implanted at different locations of the liver phantom.