4D liver tumor localization using cone-beam projections and a biomechanical model. (April 2019) - 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
Highlights: Delivering effective radiation therapy for liver requires daily tumor localization at each treatment, but the respiration-induced liver motion and the low tumor visibility complicate this task in the clinic. We have developed a biomechanical modeling-guided cone-beam CT (CBCT) estimation technique. It can generate 4D liver CBCT images and corresponding deformation vector fields (DVFs) from deforming a prior high-quality liver CT or CBCT image. The DVFs enable automatic 4D liver tumor localization by propagating the liver tumor contour from the prior image. Our study demonstrates that the new method can accurately localize liver tumors in 4D to ∼2 mm in center-of-mass-errors. 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.