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See detailA rigged model of the breast for preoperative surgical planning
Mazier, Arnaud UL; Ribes, Sophie; Testylier, Romain et al

E-print/Working paper (2021)

In breast surgical practice, various scans and medical examinations are performed before surgery. This includes identifying landmarks defining the operating procedure. In most cases, the position of the ... [more ▼]

In breast surgical practice, various scans and medical examinations are performed before surgery. This includes identifying landmarks defining the operating procedure. In most cases, the position of the patient during the scan is vastly different from the one encountered during the operation. We address the challenge of mapping preoperative information to the operating field, with the following constraints: registration has to be done in less than 10 seconds to be compatible with a clinical workflow; the cost of the device must be small and we assume data scarcity, i.e. that our database has twenty scans of patients at most. We build anatomical complexity through a skinning model comprised of scalable bones (to account for pose and morphological variations) and deformable organs (blendshapes, to account for anatomical variations). Similar to animation rigs used in computer graphics, and in contrast to statistical approaches, we manually design a model with some desirable properties, using a reduced number of well-chosen degrees of freedom. Meaningful constraints can be applied to the registration depending on the context, and the trade-off between precision and complexity can be optimized. The result is a surface mesh of the patient obtained in less than 1 minute (scan and reconstruction included) and a registration method that converges within a few seconds (3 maximum), reaching a mean absolute squared error of 2.3 mm for mesh registration and 8.0 mm for anatomical landmarks. The registered model is used to transfer surgical reference patterns on any patient in any position. [less ▲]

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See detailInverse deformation analysis: an experimental and numerical assessment using the FEniCS Project
Mazier, Arnaud UL; Bilger, Alexandre; Forte, Antonio E. et al

E-print/Working paper (2021)

In this paper, we develop a framework for solving inverse deformation problems using the FEniCS Project finite element software. We validate our approach with experimental imaging data acquired from a ... [more ▼]

In this paper, we develop a framework for solving inverse deformation problems using the FEniCS Project finite element software. We validate our approach with experimental imaging data acquired from a soft silicone beam under gravity. In contrast with inverse iterative algorithms that require multiple solutions of a standard elasticity problem, the proposed method can compute the undeformed configuration by solving only one modified elasticity problem. This modified problem has a complexity comparable to the standard one. The framework is implemented within an open-source pipeline enabling the direct and inverse deformation simulation directly from imaging data. We use the high-level Unified Form Language (UFL) of the FEniCS Project to express the finite element model in variational form and to automatically derive the consistent Jacobian. Consequently, the design of the pipeline is flexible: for example, it allows the modification of the constitutive models by changing a single line of code. We include a complete working example showing the inverse deformation of a beam deformed by gravity as supplementary material. [less ▲]

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See detailInverse simulation for retrieving the undeformed position for hyperelastic materials : application to breast simulations
Mazier, Arnaud UL; Bilger, Alexandre; Forte, Antonio et al

Scientific Conference (2020, July)

The rest position, as well as any associated internal stresses in soft organs, are usually unknown when solving biomechanics problems. In addition, the initial geometry of a specific organ, obtained from ... [more ▼]

The rest position, as well as any associated internal stresses in soft organs, are usually unknown when solving biomechanics problems. In addition, the initial geometry of a specific organ, obtained from medical images, is affected by external forces. An example is breast MRI performed prior to cancer surgery. During the imaging routine, the breast is elongated in prone position in order to better view the tumor. However, during surgery, the patient is in supine position, which causes the breast to rest in a completely different state. To simulate this state from the prone stance, the rest configuration is needed as well as the pre-stress mapping of the organs [1]. To tackle this problem, iterative algorithms have been proposed such as Sellier’s method [2]. In this fixed-point approach, the rest configuration is updated by multiple forward calculations then repeated until the error (between the updated and target configuration) reaches an established threshold. The method presents many benefits e.g. easy implementation and fast convergence. However, convergence issues appear at large deformations induced for instance by hyperelastic material formulations. In this work, we develop a simple formulation and a robust solution procedure for inverse deformation problems in soft-tissue biomechanics using the FEniCS Project finite element solver. In contrast with iterative algorithms, our method can solve with a single simulation the rest position without computing multiple solutions of the forward problem. For a fixed convergence tolerance, our physics-based algorithm is about ten times faster and better handles large deformations than Sellier’s method [2]. Moreover, no additional direct deformation simulations from the rest configuration are required to compute stresses in the organ. The framework is implemented within an open-source pipeline enabling the seamless, fully parallelized, direct and inverse deformation simulation of organs directly from segmented images. The pipeline is also designed to be flexible to user’s needs: for example, it allows the modification of the constitutive models by changing a single line of code. [less ▲]

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