A Biomechanical Modeling Guided CBCT Estimation Technique

Abstract

Two-dimensional-to-three-dimensional (2D-3D) deformation has emerged as a new technique to estimate cone-beam computed tomography (CBCT) images. The technique is based on deforming a prior high-quality 3D CT/CBCT image to form a new CBCT image, guided by limited-view 2D projections. The accuracy of this intensity-based technique, however, is often limited in low-contrast image regions with subtle intensity differences. The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures. Specifically, Bio-CBCT-est first extracts the 2D-3D deformation-generated displacement vectors at the high-contrast anatomical structure boundaries. The extracted surface deformation fields are subsequently used as the boundary conditions to drive structure-based FEA to correct and fine-tune the overall deformation fields, especially those at low-contrast regions within the structure. The resulting FEA-corrected deformation fields are then fed back into 2D-3D deformation to form an iterative loop, combining the benefits of intensity-based deformation and biomechanical modeling for CBCT estimation. Using eleven lung cancer patient cases, the accuracy of the Bio-CBCT-est technique has been compared to that of the 2D-3D deformation technique and the traditional CBCT reconstruction techniques. The accuracy was evaluated in the image domain, and also in the DVF domain through clinician-tracked lung landmarks.

Fig. 1

Work-flow of the Bio-CBCT-est technique.

Fig. 2

An example of a bifurcation identified by the clinician as a lung landmark.

Fig. 3

Simulated projections showing different levels of incorporated noise. The quantum noise increases as I₀ decreases.

Fig. 4

Work-flow of the lung biomechanical modeling process.

Fig. 5

Axial slice cuts of the reconstructed CBCT by the FDK algorithm, the reconstructed CBCT by the ART-TV algorithm., the estimated CBCT by the 2D-3D deformation technique, the estimated CBCT by the Bio-CBCT-est technique, and the ‘ground-truth’ 4D-CT EI image. The magnified images show the fine details of the lung. The display window is [0, 0.08 mm⁻¹] for the original images and [0, 0.04 mm⁻¹] for the enlarged images. The figure corresponds to patient 04, by using 10 projections for CBCT reconstruction/estimation.

Fig. 6

Comparison between three DVFs: the reference lung DVF computed from Demons registration, the lung DVF computed from 2D-3D deformation, and the lung DVF computed from Bio-CBCT-est. The color bar on the right denotes the vector magnitudes of the deformation (unit: mm).