Markus Fleute

Incorporating a statistically based shape model into a system for computer-assisted anterior cruciate ligament surgery.

This paper addresses the problem of extrapolating very sparse three-dimensional (3-D) data to obtain a complete surface representation. A new method that uses statistical shape models is proposed and its application to computer-assisted anterior cruciate ligament (ACL) reconstruction is detailed. The rupture of the ACL has become one of the most common knee injuries. One problem during reconstruction is to find the optimal attachment points for the graft. Therefore a system for computer-assisted reconstruction of the ACL has been proposed by TIMC laboratory. During surgery the surgeon collects several data points on the tibial and femoral joint surface with a 3-D localizer system. These 3-D data are used to find those attachment points resulting in a low anisometry of the graft, while preventing impingement between the graft and the femoral notch. As the collected data points only cover a small surface patch of the femur, it is desirable to extrapolate these data to also have a visualization in those areas where no data points are available. A sufficiently good approximation of the actual femur by the model would further allow us to better deal with the notch impingement problem of the graft. The chosen approach is to fit a deformable model to the data points, it can be subdivided into two steps, constructing the model and fitting this model to the data. To incorporate a priori knowledge into the model, the allowed deformations are determined by the statistics of the shape variation of a set of training objects. Matching the training objects together is obtained by elastic registration of surface points using octree splines. The fitting process of the sparse intra-operative data with the statistical model results in a non-linear multi-dimensional function minimization. A hybrid search strategy combining local and global methods is used to avoid local minima. First experimental results with a model generated from 10 femurs are presented, including fitting of the model with both simulated and real intra-operative data.

Nonrigid 3-D/2-D Registration of Images Using Statistical Models

This paper presents a new algorithm for reconstruction of 3D shapes using a few x-ray views and a statistical model. In many applications of surgery such as orthopedics, it is desirable to define a surgical planning on 3-D images and then to execute the plan using standard registration techniques and image-guided surgery systems. But the cost, time and x-ray dose associated with standard pre-operative Computed Tomography makes it difficult to use this methodology for rather standard interventions. Instead, we propose to use a few x-ray images generated from a C-Arm and to build the 3-D shape of the patient bones or organs intra-operatively, by deforming a statistical 3-D model to the contours segmented on the x-ray views. In this paper, we concentrate on the application of our method to bone reconstruction. The algorithm starts from segmented contours of the bone on the x-ray images and an initial estimate of the pose of the 3-D model in the common coordinate system of the set of x-ray projections. The statistical model is made of a few principal modes that are sufficient to represent the normal anatomy. Those modes are built by using a generalization of the Cootes and Taylor method to 3-D surface models, previously published in MICCAI’98 by the authors. Fitting the model to the contours is achieved by using a generalization of the Iterative Closest Point Algorithm to nonrigid 3D/2D registration. For pathological shapes, the statistical model is not valid and subsequent local refinement is necessary. First results are presented for a 3-D statistical model of the distal part of the femur.

Building a Complete Surface Model from Sparse Data Using Statistical Shape Models: Application to Computer Assisted Knee Surgery System

This paper addresses the problem of extrapolating very few range data to obtain a complete surface representation of an antomical structure. A new method that uses statistical shape models is proposed and its application to modeling a few points manually digitized on the femoral surface is detailed, in order to improve visualization of a system developped by TIMC laboratory for computer assisted anterior cruciate ligament (ACL) reconstruction. The model is built from a population of 11 femur specimen digitized manually. Data sets are registered together using an elastic registration method of Szeliski and Lavallee based on octree-splines. Principal Components Analysis (PCA) is performed on a field of surface deformation vectors. Fitting this statistical model to a few points is performed by non-linear optimisation. Results are presented for both simulated and real data. The method is very flexible and can be applied to any structures for which the shape is stable.

3D Interventional Imaging with 2D X-Ray Detectors

Pre-operative images, such as CT or MRI, are often necessary for CAMI. However, they could be replaced by interventional 3D reconstruction from 2D x-ray sensors. 3D reconstruction from classical image amplifiers needs the correction of geometric distortions due to the magnetic fields. We investigate new calibration marker schemes exploiting spectral properties of the x-ray transform. According to Shannon theory, no information is lost with these new schemes, even if the markers can be seen in each image. Numerical experiments from both phantom and real data are provided.

Registration of medical images for surgical action: use of optical sensors and matching algorithm

We propose in this paper to deal with the notion of medical image registration: both physicians and surgeons need in the patient room or in the operation theater images of the medical target to reach and treat; these images can be very precise and come from a pre-operative acquisition device (NMR, CT-scanner,...) or from a per-operative set of sensors (active or passive infra-red sensors, laser acquisition, numerical 2D X rays, 3D echography,...). The main problem is then how to compare these multi-modal images having different resolutions and extracting different features (anatomic or functional) from the patient medical reality? In a first section, we present the hard side with essentially optical sensing methods; after, in a second section, we present an example of matching algorithm built in order to compare and super-impose pre- and per-operative images.