(Sunday, 7/29/2018) 5:05 PM - 6:00 PM
Room: Room 207
Purpose: To comprehensively evaluate a biomechanical modeling-guided CBCT reconstruction technique (Bio-recon) for liver tumor localization.
Methods: Bio-recon generates new CBCTs by deforming a prior high-quality CT using deformation vector fields (DVFs). To solve the DVFs, Bio-recon employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior volume to 2D phase-sorted on-board projections in imaging intensities. This step’s accuracy is limited at low-contrast liver regions with insufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVF solved at higher-contrast liver boundaries to fine-tune the intra-liver DVF by finite element analysis-based biomechanical modeling. We evaluated Bio-recon with seven liver patient cases, using realistic cone-beam projections simulated by Monte-Carlo. We quantitatively compared automatic liver tumor/cyst contours propagated via solved DVFs with manual contours on CT reference images, using the DICE similarity index and the center-of-mass-error (COME) metric. To further evaluate Bio-recon for clinical applications, an in-house motion-enabled deformable liver phantom was also developed using the 3D printing technology. The motion of the liver phantom solved by Bio-recon was compared against the ‘ground-truth’ motion tracked with implanted ball-bearings (BBs).
Results: Using 20 projections for reconstruction, the average(± s.d.) DICE for the patient study increased from 0.358±0.103 (prior) to 0.772±0.080 (after Bio-recon), and the corresponding average COME decreased from 9.2±2.2 mm to 2.2±1.2 mm. For the in-house deformable liver phantom study, the average error of Bio-recon solved DVFs by using 20 projections was <3 mm, as evaluated by the motion of BBs.
Conclusion: Bio-recon substantially improves the accuracy of liver tumor tracking with limited projections. The DVFs solved can be applied to propagate organ contours and accumulate treatment dose, which fits well into the adaptive radiotherapy scheme.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by grants from the American Cancer Society (RSG-13-326-01-CCE), from the US National Institutes of Health (R01 EB020366), and from the Cancer Prevention and Research Institute of Texas (RP130109).
Keywords
Cone-beam CT, Reconstruction, Registration
Taxonomy
IM- Cone Beam CT: Image Reconstruction
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