Liesbet Roose

Biomechanically based elastic breast registration using mass tensor simulation

We present a new approach for the registration of breast MR images, which are acquired at different time points for observation of lesion evolution. In this registration problem, it is of utmost importance to correct only for differences in patient positioning and to preserve other diagnostically important differences between both images, resulting from anatomical and pathological changes between both acquisitions. Classical free form deformation algorithms are therefore less suited, since they allow too large local volume changes and their deformation is not biomechanically based. Instead of adding constraints or penalties to these methods in order to restrict unwanted deformations, we developed a truly biomechanically based registration method where the position of skin and muscle surface are used as the only boundary conditions. Results of our registration method show an important improvement in correspondence between the reference and the deformed floating image, without introducing physically implausible deformations and within a short computational time.

Pre-operative simulation and post-operative validation of soft-tissue deformations for breast implantation planning

Virtual surgery simulation plays an increasingly important role as a planning aid for the surgeon. A reliable simulation method to predict the surgical outcome of breast reconstruction and breast augmentation procedures does not yet exist. However, a method to pre-operatively assess the result of the procedure would be useful to ensure a symmetrical and naturally looking result, and could be a practical means of communication with the patient. In this paper, we present a basic framework to simulate a subglandular breast implantation. First, we propose a method to build a model of the patients anatomy, based on a 3D picture of the skin surface in combination with thickness estimates of the soft tissue surrounding the breast. This approach is cheap, fast and the picture can be taken while the patient is standing upright, which makes it advantageous compared to conventional CTor MR-based methods. Second, a set of boundary conditions is defined to mimic the effect of the implant. Finally, we compute the new equilibrium geometry using the iterative FEM-based Mass Tensor Method, which is computationally more effcient than the traditional FEM approach since sufficient precision can be achieved with a limited number of iterations. We illustrate our approach with a preliminary validation study on 4 patients. We obtain promising results with a mean error between the simulated and the true post-operative breast geometry below 4 mm and maximal error below 10 mm, which is found to be sufficiently accurate for visual assessment in clinical practice.

An Elasticity Penalty: Mixing FEM and Nonrigid Registration

Voxel-intensity based nonrigid image registration can be formulated as an optimization problem whose goal is to minimize a cost function consisting of two parts. One part characterizes the similarity between both images. The other part regularizes the transformation and/or penalizes improbable or impossible deformations. In this paper, we extend previous work on nonrigid registration by introducing a new penalty term expressing the elastic energy of the deformation, using the same expression as used in finite element modeling (FEM). We compare the new elasticity penalty, a volume penalty and a rigidity penalty with a biomechanical mass-tensor model (MTM), equivalent to FEM. Comparison is carried out on artificial images and volunteer breast MR images. We show that the results obtained using the elasticity penalty approximate the MTM registration up to less than 1 voxel for the artificial images and less than 3 voxels for the clinical images. The errors are mainly situated near the edges of the registered structures, and therefore can be attributed to differences in boundary conditions. We also show that the elasticity penalty, volume penalty and rigidity penalty give similar results.

Adaptive Boundary Conditions for Physically Based Follow-Up Breast MR Image Registration

This paper presents an algorithm for non-rigid registration of breast MRI follow-up images that compensates for differences in patient positioning while maintaining real anatomical and pathological changes. The proposed method uses a biomechanical model to constrain the deformation of the internal breast tissue according to elastic continuum mechanics, which is driven by suitable boundary conditions that align the breast surfaces in the images to be registered. Typically, such boundary conditions impose one-to-one surface point correspondences that are established a priori. We investigate alternative, more flexible boundary conditions that do not depend on fixed point correspondences and do not assume completely accurate breast surface segmentation in both images. More specifically, we allow for sliding motion of one surface over the other during deformation as well as for restricted motion perpendicular to the initially segmented boundary surface, based on the internal elastic forces and local intensity information. We evaluate the impact of different boundary conditions on registration quality from the subtraction images obtained for repeated scans of healthy volunteers with intermediate repositioning, using rigid body and free form whole volume intensity based registration for comparison, and also present initial results for actual patient data. Our results demonstrate a drastic reduction in subtraction artifacts using our model, without compromising the biomechanical validity of the deformation field such as unrealistically large local volume changes as with traditional voxel intensity based registration.