Breast image registration for surgery: Insights on material mechanics modeling

Abstract

Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, more closely represents the surgical presentation compared to conventional diagnostic pendant positioning. Optimal utilization for surgical guidance, however, requires a fast and accurate image-to-physical registration from preoperative imaging to intraoperative surgical presentation. In this study, three registration methods were investigated on healthy volunteers' breasts (n=11) with the arm-down position simulating preoperative imaging and arm-up position simulating intraoperative data. The registration methods included: (1) point-based rigid registration using synthetic fiducials, (2) non-rigid biomechanical model-based registration using sparse data, and (3) a data-dense 3D diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. The average target registration errors (TRE) were 10.4 ± 2.3, 6.4 ± 1.5, and 2.8 ± 1.3 mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7, 2.5 ± 1.1, and 3.1 ± 1.1 mm (mean ± standard deviation) for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. Additionally, common mechanics-based deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field. The average metrics revealed anisotropic tissue behavior and a statistical difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Overall, registration accuracy significantly improved with increasingly flexible registration methods, which may inform future development of image guidance systems for lumpectomy procedures.

Keywords: breast cancer; deformable image registration; finite element modeling; lumpectomy; registration; surgical guidance.

Figure 1:

The process for rigid (left), nonrigid (middle), and ANTs (right) registrations shown on axial views of a segmented breast mesh. In the top row, the data utilized for each registration is visualized. Rigid registration used MR-visible skin fiducials (blue), nonrigid model-based registration used MR-visible skin fiducials (blue), skin surface (yellow), and chest wall surface (red), and ANTs registration used the 3D image volume. In the bottom row, the registered mesh (gray) and targets (black) are overlaid on the ground truth arm-up mesh and targets shown in green.

Figure 2:

Registration methods performance comparison showing (A) fiducial registration error $\left(\overline{\text{FRE}}\right)$ and (B) target registration error $\left(\overline{\text{TRE}}\right)$ from 11 breasts. Whiskers represent minimum and maximum FRE and TRE values.