Lara M. Vigneron

Modelling surgical cuts, retractions, and resections via Extended Finite Element Method

We introduce a new, efficient approach for modelling the deformation of organs following surgical cuts, retractions, and resections. It uses the extended finite element method (XFEM), recently developed in “fracture mechanics” for dealing with cracks in mechanical parts. XFEM eliminates the computationally-expensive remeshing that would be required if the standard finite element method (FEM) was used. We report on the successful application of the method to the simulation of 2D retraction. The method may have significant impact on surgical simulators and navigators.

On Extended Finite Element Method (XFEM) for Modelling of Organ Deformations Associated with Surgical Cuts

The Extended Finite Element Method (XFEM) is a technique used in fracture mechanics to predict how objects deform as cracks form and propagate through them. Here, we propose the use of XFEM to model the deformations resulting from cutting through organ tissues. We show that XFEM has the potential for being the technique of choice for modelling tissue retraction and resection during surgery. Candidates applications are surgical simulators and image-guided surgery. A key feature of XFEM is that material discontinuities through FEM meshes can be handled without mesh adaptation or remeshing, as would be required in regular FEM. As a preliminary illustration, we show the result of XFEM calculation for a simple 2D shape in which a linear cut was made.

3D XFEM-based modeling of retraction for preoperative image update

Outcomes for neurosurgery patients can be improved by enhancing intraoperative navigation and guidance. Current navigation systems do not accurately account for intraoperative brain deformation. So far, most studies of brain deformation have focused on brain shift, whereas this paper focuses on the brain deformation due to retraction. The heart of our system is a 3D nonrigid registration technique using a biomechanical model driven by the deformations of key surfaces tracked between two intraoperative images. The key surfaces, e.g., the whole-brain region boundary and the lips of the retraction cut, thus deform due to the combination of gravity and retractor deployment. The tissue discontinuity due to retraction is handled via the eXtended Finite Element Method (XFEM), which has the appealing feature of being able to handle arbitrarily shaped discontinuity without any remeshing. Our approach is shown to significantly improve the alignment of intraoperative MRI.

Serial FEM/XFEM-based update of preoperative brain images using intraoperative MRI

Current neuronavigation systems cannot adapt to changing intraoperative conditions over time. To overcome this limitation, we present an experimental end-to-end system capable of updating 3D preoperative images in the presence of brain shift and successive resections. The heart of our system is a nonrigid registration technique using a biomechanical model, driven by the deformations of key surfaces tracked in successive intraoperative images. The biomechanical model is deformed using FEM or XFEM, depending on the type of deformation under consideration, namely, brain shift or resection. We describe the operation of our system on two patient cases, each comprising five intraoperative MR images, and we demonstrate that our approach significantly improves the alignment of nonrigidly registered images.

Enhanced FEM-based modeling of brain shift deformation in Image-Guided Neurosurgery

We consider the problem of improving outcomes for neurosurgery patients by enhancing intraoperative navigation and guidance. Current navigation systems do not accurately account for intraoperative brain deformation. We focus on the brain shift deformation that occurs just after the opening of the skull and dura. The heart of our system is a nonrigid registration technique using a biomechanical model. We specifically work on two axes: the representation of the structures in the biomechanical model and the evaluation of the surface landmark displacement fields between intraoperative MR images. Using the modified Hausdorff distance as an image similarity measure, we demonstrate that our approach significantly improves the alignment of the intraoperative images.

2D XFEM-based modeling of retraction and successive resections for preoperative image update.

This paper considers an approach to improving outcomes for neurosurgery patients by enhancing intraoperative navigation and guidance. Currently, intraoperative navigation systems do not accurately account for brain shift or tissue resection. We describe how preoperative images can be incrementally updated to take into account any type of brain tissue deformation that may occur during surgery, and thus to improve the accuracy of image-guided navigation systems. For this purpose, we have developed a non-rigid image registration technique using a biomechanical model, which deforms based on the Finite Element Method (FEM). While the FEM has been used successfully for dealing with deformations such as brain shift, it has difficulty with tissue discontinuities. Here, we describe a novel application of the eXtended Finite Element Method (XFEM) in the field of image-guided surgery in order to model brain deformations that imply tissue discontinuities. In particular, this paper presents a detailed account of the use of XFEM for dealing with retraction and successive resections, and demonstrates the feasibility of the approach by considering 2D examples based on intraoperative MR images. To evaluate our results, we compute the modified Hausdorff distance between Canny edges extracted from images before and after registration. We show that this distance decreases after registration, and thus demonstrate that our approach improves alignment of intraoperative images.

XFEM-based modeling of successive resections for preoperative image updating

We present a new method for modeling organ deformations due to successive resections. We use a biomechanical model of the organ, compute its volume-displacement solution based on the eXtended Finite Element Method (XFEM). The key feature of XFEM is that material discontinuities induced by every new resection can be handled without remeshing or mesh adaptation, as would be required by the conventional Finite Element Method (FEM). We focus on the application of preoperative image updating for image-guided surgery. Proof-of-concept demonstrations are shown for synthetic and real data in the context of neurosurgery.

Meeting the Needs of Mothers During the Postpartum Period: Using Co-Creation Workshops to Find Technological Solutions

Background The postnatal period is associated with many new needs for mothers. Objective The aim of this study was to find technological solutions that meet the needs of mothers during the year following childbirth. Methods Two co-creation workshops were undertaken with parents and professionals. The aim of the first workshop was to create a list of all the criteria the proposed solution would have to address to meet the needs of mothers after childbirth. The aim of the second workshop was to create solutions in response to the criteria selected during the first workshop. Results Parents and health professionals want solutions that include empathy (ie, to help fight against the feelings of abnormality and loneliness), that help mothers in daily life, that are personalized and adapted to different situations, that are educational, and that assures some continuity in their contact with health professionals. In practice, we found that parents and professionals think the solution should be accessible to everyone and available at all times. To address these criteria, technology experts proposed different solutions, such as a forum dedicated to the postpartum period that is supervised by professionals, a centralized website, a system of videoconferencing, an online exchange group, a “gift voucher” system, a virtual reality app, or a companion robot. Conclusions The human component seems to be very important during the postnatal period. Nevertheless, technology could be a great ally in helping mothers during the postpartum period. Technology can help reliably inform parents and may also give them the right tools to find supportive people. However, these technologies should be tested in clinical trials.

Identifying maternal needs following childbirth: A qualitative study among mothers, fathers and professionals

BackgroundPregnancy and childbirth are two critical stages in a woman’s life. Various studies have suggested that psychological distress is common during the year after childbirth. The objectives of this exploratory study were (1) to explore the needs of mothers in the year following childbirth; (2) to compare these needs between mothers who did not have the feeling of living a psychological disorder or a depression and mothers who lived a psychological disorder or had the impression of living a depression; and (3) to compare the needs expressed by mothers with the perception of professionals and fathers about the mothers’ needs.MethodsFirst, we proceeded to 22 individual qualitative interviews followed by one focus group, with mothers, with and without experience of psychological distress. Then, we conducted 2 focus groups: one with professionals and one with fathers.ResultsNeeds of mothers after childbirth have been indexed in four categories: need of information, need of psychological support, need to share experience, and need of practical and material support. Women do not feel sufficiently informed about this difficult period of life. They do not feel sufficiently supported, not only from a psychological point of view but also from a more practical point of view, for example with household chores. They need to share their experience of life, they need to be reassured and they need to feel understood. It seems that some differences exist between mothers’ and professionals’ experiences but also between mothers’ and fathers’ experiences.ConclusionYoung mothers apparently feel a lack of support at different levels in the year following childbirth. This study provides ways to meet women’s needs and to try to prevent the risk of postpartum psychological distress during this period of time.

Update of diagnostic preoperative images using low-field interventional MRI for navigation in neurosurgery: rigid-body registration

This study looks into the rigid-body registration of pre-operative anatomical high field and interventional low field magnetic resonance images (MRI). The accurate 3D registration of these modalities is required to enhance the content of interventional images with anatomical (CT, high field MRI, DTI), functional (DWI, fMRI, PWI), metabolic (PET) or angiography (CTA, MRA) pre-operative images. The specific design of the interventional MRI scanner used in the present study, a PoleStar N20, induces image artifacts, such as ellipsoidal masking and intensity inhomogeneities, which affect registration performance. On MRI data from eleven patients, who underwent resection of a brain tumor, we quantitatively evaluated the effects of artifacts in the image registration process based on a normalized mutual information (NMI) metric criterion. The results show that the quality of alignment of pre-operative anatomical and interventional images strongly depends on pre-processing carried out prior to registration. The registration results scored the highest in visual evaluation only if intensity variations and masking were considered in image registration. We conclude that the alignment of anatomical high field MRI and PoleStar interventional images is the most accurate when the PoleStars induced image artifacts are corrected for before registration.