Textual fiducial detection in breast conserving surgery for a near-real time image guidance system

Computational Imaging to Compensate for Soft-Tissue Deformations in Image-Guided Breast Conserving Surgery

OBJECTIVE

During breast conserving surgery (BCS), magnetic resonance (MR) images aligned to accurately display intraoperative lesion locations can offer improved understanding of tumor extent and position relative to breast anatomy. Unfortunately, even under consistent supine conditions, soft tissue deformation compromises image-to-physical alignment and results in positional errors.

METHODS

A finite element inverse modeling technique has been developed to nonrigidly register preoperative supine MR imaging data to the surgical scene for improved localization accuracy during surgery. Registration is driven using sparse data compatible with acquisition during BCS, including corresponding surface fiducials, sparse chest wall contours, and the intra-fiducial skin surface. Deformation predictions were evaluated at surface fiducial locations and subsurface tissue features that were expertly identified and tracked. Among n = 7 different human subjects, an average of 22 ± 3 distributed subsurface targets were analyzed in each breast volume.

RESULTS

The average target registration error (TRE) decreased significantly when comparing rigid registration to this nonrigid approach (10.4 ± 2.3 mm vs 6.3 ± 1.4 mm TRE, respectively). When including a single subsurface feature as additional input data, the TRE significantly improved further (4.2 ± 1.0 mm TRE), and in a region of interest within 15 mm of a mock biopsy clip TRE was 3.9 ± 0.9 mm.

CONCLUSION

These results demonstrate accurate breast deformation estimates based on sparse-data-driven model predictions.

SIGNIFICANCE

The data suggest that a computational imaging approach can account for image-to-surgery shape changes to enhance surgical guidance during BCS.

Impact of deformation on a supine-positioned image-guided breast surgery approach

PURPOSE

To reduce reoperation rates for image-guided breast-conserving surgery, the enhanced sensitivity of magnetic resonance (MR) supine imaging may be leveraged. However, accurate tissue correspondence between images and their physical counterpart in the surgical presentation is challenging due to breast deformations (e.g., from patient/arm position changes, and operating room table rotation differences). In this study, standard rigid registration methods are employed and tissue deformation is characterized.

METHODS

On n = 10 healthy breasts, surface displacements were measured by comparing intraoperative fiducial locations as the arm was moved from conventional MR scanning positions (arm-down and arm-up) to the laterally extended surgical configuration. Supine MR images in the arm-down and arm-up positions were registered to mock intraoperative presentations.

RESULTS

Breast displacements from a supine MR imaging configuration to a mock surgical presentation were 28.9 ± 9.2 mm with shifts occurring primarily in the inferior/superior direction. With respect to supine MR to surgical alignment, the average fiducial, target, and maximum target registration errors were 9.0 ± 1.7 mm, 9.3 ± 1.7 mm, and 20.0 ± 7.6 mm, respectively. Even when maintaining similar arm positions in the MR image and mock surgery, the respective averages were 6.0 ± 1.0 mm, 6.5 ± 1.1 mm, and 12.5 ± 2.8 mm.

CONCLUSION

From supine MR positioning to surgical presentation, the breast undergoes large displacements (9.9-70.1 mm). The data also suggest that significant nonrigid deformations (9.3 ± 1.7 mm with 20.0 mm average maximum) exist that need to be considered in image guidance and modeling applications.