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

Richey, Winona L.; Heiselman, Jon S.; Ringel, Morgan J.; Meszoely, Ingrid M.; Miga, Michael I.

doi:10.1109/tbme.2022.3177044

Cited by 11 publications

(10 citation statements)

References 44 publications

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“…and implemented for breast registration in Richey et al 26 – 28 Briefly, 45 control points were placed on the surface of the preoperative 3D breast mesh excluding the skin surface and individually perturbed in the x, y, and z directions to create a basis of displacement modes which were solved as forward homogeneous isotropic linear elastic boundary value problems with Young’s Modulus E=2100 Pa and Poisson ratio ν=0.45. These material properties were chosen based on previous implementations of this method and to model breast tissue as nearly incompressible 26 …”

Section: Methodsmentioning

confidence: 99%

“…These material properties were chosen based on previous implementations of this method and to model breast tissue as nearly incompressible 26 – 29 After perturbation, the local boundary region displacements surrounding each control point were recomputed to determine a statically equivalent, locally distributed load to eliminate single-point displacement artifacts in the deformation modes. Levenberg–Marquardt optimization was used to solve for a linear combination of these modes that minimized the distances between the deformed and intraoperative MR-visible skin fiducials, intrafiducial skin surface, and sparsely sampled chest wall surface while also incorporating a strain energy penalty to constrain deformations.…”

Section: Methodsmentioning

confidence: 99%

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Supine magnetic resonance image registration for breast surgery: insights on material mechanics

et al. 2022

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.PurposeBreast 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, is being explored since it more closely represents the surgical presentation compared to conventional diagnostic imaging positions. Despite this preoperative imaging position, deformation is still present between the supine imaging and surgical state. As a result, a fast and accurate image-to-physical registration approach is needed to realize image-guided breast surgery.ApproachIn this study, three registration methods were investigated on healthy volunteers’ breasts (n = 11) with the supine arm-down position simulating preoperative imaging and supine arm-up position simulating intraoperative presentation. The registration methods included (1) point-based rigid registration using synthetic fiducials, (2) nonrigid biomechanical model-based registration using sparse data, and (3) a data-dense three-dimensional diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. Additionally, deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field to better understand breast material mechanics.ResultsThe 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 for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. The mechanics-based deformation metrics revealed an overall anisotropic tissue behavior and a statistically significant difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy.ConclusionsOverall, registration accuracy significantly improved with increasingly flexible and data-dense registration methods. Analysis of these outcomes may inform the future development of image guidance systems for lumpectomy procedures.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Supine magnetic resonance image registration for breast surgery: insights on material mechanics

et al. 2022

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“…A mesh-splitting technique where nodes on either side of the incision plane were duplicated was used to separate the domain 7 . The linearized iterative boundary reconstruction (LIBR) method was used to reconstruct retraction conditions as driven by sparsely available mock-tissue deformation data 9,10 . Briefly, the LIBR method is a biomechanical modeling registration method that utilizes sparse data to reconstruct the optimal boundary conditions that minimizes model-data error by designating control point locations where forces are applied.…”

Section: Retraction Modelingmentioning

confidence: 99%

“…To model retraction, eight control points were placed on the inside surface of the incision cavity with four control points on either side of the incision plane. Five additional control points were placed on the posterior surface of the phantom which is consistent with a previous implementation of the LIBR method for modeling breast deformations 10 . Displacement modes were computed as FEM forward-solve, homogeneous isotropic elastic boundary value problems with Young's Modulus E = 2100 Pa and Poisson ratio ν = 0.45.…”

Section: Retraction Modelingmentioning

confidence: 99%

“…Computed tomography (CT) images of the phantom in an undeformed state and a retracted state are acquired to quantify surface and subsurface deformations. A biomechanical modeling approach that utilizes the linearized iterative boundary reconstruction (LIBR) method is proposed for reconstructing the retraction state under the constraints of sparsely available data acquirable in routine BCS 9,10 . The phantom imaging dataset is used to evaluate model performance.…”

Section: Introductionmentioning

confidence: 99%

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Modeling retraction for breast conserving surgery guidance

Espinosa

Ringel

Heiselman

et al. 2023

Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

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Breast conserving surgery is a common treatment option for early-stage breast cancer, and it relies on complete tumor excision such that no residual cancer is left in the resection cavity. However, the use of preoperative imaging to inform excision is compromised by intraoperative deformations that change the location, volume, and shape of the tumor compared to the imaging configuration. For intra-procedural guidance specifically, incision and retraction alter the tumor presentation and geometry. Being able to compensate for retraction deformations intraoperatively may increase the utility of image guidance technologies. In this work, a breast retraction phantom and deformation modeling approach are developed to explore the potential of modeling retraction for image guidance during BCS. Surface and subsurface beads were embedded in a realistic silicone breast phantom, and CT images were acquired in undeformed and retracted states. A reconstructive, sparse-data registration method was used to model retraction. Modeling accuracy was evaluated by comparing model-predicted and ground-truth bead displacements. The average surface bead registration error after retraction modeling in a region of interest was 0.5 ± 0.1 mm (maximum 0.5 mm). The average subsurface bead registration error in a region of interest was 1.2 ± 0.6 mm (maximum 2.6 mm). A biomechanical modeling method that includes retraction may improve the accuracy of image guidance for breast conserving surgery, but more work is needed to evaluate its utility.

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Regularized Kelvinlet Functions to Model Linear Elasticity for Image-to-Physical Registration of the Breast

Ringel,

Heiselman,

Richey

et al. 2023

Lecture Notes in Computer Science

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

Cited by 11 publications

References 44 publications

Supine magnetic resonance image registration for breast surgery: insights on material mechanics

Supine magnetic resonance image registration for breast surgery: insights on material mechanics

Modeling retraction for breast conserving surgery guidance

Regularized Kelvinlet Functions to Model Linear Elasticity for Image-to-Physical Registration of the Breast

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