Matthieu Ferrant

Registration of 3-d intraoperative MR images of the brain using a finite-element biomechanical model

We present a new algorithm for the nonrigid registration of three-dimensional magnetic resonance (MR) intraoperative image sequences showing brain shift. The algorithm tracks key surfaces of objects (cortical surface and the lateral ventricles) in the image sequence using a deformable surface matching algorithm. The volumetric deformation field of the objects is then inferred from the displacements at the boundary surfaces using a linear elastic biomechanical finite-element model. Two experiments on synthetic image sequences are presented, as well as an initial experiment on intraoperative MR images showing brain shift. The results of the registration algorithm show a good correlation of the internal brain structures after deformation, and a good capability of measuring surface as well as subsurface shift. We measured distances between landmarks in the deformed initial image and the corresponding landmarks in the target scan. Cortical surface shifts of up to 10 mm and subsurface shifts of up to 6 mm were recovered with an accuracy of 1 mm or less and 3 mm or less respectively.

Evaluation of three‐dimensional finite element‐based deformable registration of pre‐ and intraoperative prostate imaging

In this report we evaluate an image registration technique that can improve the information content of intraoperative image data by deformable matching of preoperative images. In this study, pretreatment 1.5 tesla (T) magnetic resonance (MR) images of the prostate are registered with 0.5 T intraoperative images. The method involves rigid and nonrigid registration using biomechanical finite element modeling. Preoperative 1.5 T MR imaging is conducted with the patient supine, using an endorectal coil, while intraoperatively, the patient is in the lithotomy position with a rectal obturator in place. We have previously observed that these changes in patient position and rectal filling produce a shape change in the prostate. The registration of 1.5 T preoperative images depicting the prostate substructure [namely central gland (CG) and peripheral zone (PZ)] to 0.5 T intraoperative MR images using this method can facilitate the segmentation of the substructure of the gland for radiation treatment planning. After creating and validating a dataset of manually segmented glands from images obtained in ten sequential MR-guided brachytherapy cases, we conducted a set of experiments to assess our hypothesis that the proposed registration system can significantly improve the quality of matching of the total gland (TG), CG, and PZ. The results showed that the method statistically-significantly improves the quality of match (compared to rigid registration), raising the Dice similarity coefficient (DSC) from prematched coefficients of 0.81, 0.78, and 0.59 for TG, CG, and PZ, respectively, to 0.94, 0.86, and 0.76. A point-based measure of registration agreement was also improved by the deformable registration. CG and PZ volumes are not changed by the registration, indicating that the method maintains the biomechanical topology of the prostate. Although this strategy was tested for MRI-guided brachytherapy, the preliminary results from these experiments suggest that it may be applied to other settings such as transrectal ultrasound-guided therapy, where the integration of preoperative MRI may have a significant impact upon treatment planning and guidance.

Serial registration of intraoperative MR images of the brain.

The increased use of image-guided surgery systems during neurosurgery has brought to prominence the inaccuracies of conventional intraoperative navigation systems caused by shape changes such as those due to brain shift. We propose a method to track the deformation of the brain and update preoperative images using intraoperative MR images acquired at different crucial time points during surgery. We use a deformable surface matching algorithm to capture the deformation of boundaries of key structures (cortical surface, ventricles and tumor) throughout the neurosurgical procedure, and a linear finite element elastic model to infer a volumetric deformation. The boundary data are extracted from intraoperative MR images using a real-time intraoperative segmentation algorithm. The algorithm has been applied to a sequence of intraoperative MR images of the brain exhibiting brain shift and tumor resection. Our results characterize the brain shift after opening of the dura and at the different stages of tumor resection, and brain swelling afterwards. Analysis of the average deformation capture was assessed by comparing landmarks identified manually and the results indicate an accuracy of 0.7+/-0.6 mm (mean+/-S.D.) for boundary surface landmarks, of 0.9+/-0.6 mm for landmarks inside the boundary surfaces, and 1.6+/-0.9 mm for landmarks in the vicinity of the tumor.

Registration and real-time visualization of transcranial magnetic stimulation with 3-D MR images

This paper describes a method for registering and visualizing in real-time the results of transcranial magnetic stimulations (TMS) in physical space on the corresponding anatomical locations in MR images of the brain. The method proceeds in three main steps. First, the patient scalp is digitized in physical space with a magnetic-field digitizer, following a specific digitization pattern. Second, a registration process minimizes the mean square distance between those points and a segmented scalp surface extracted from the magnetic resonance image. Following this registration, the physician can follow the change in coil position in real-time through the visualization interface and adjust the coil position to the desired anatomical location. Third, amplitude of motor evoked potentials can be projected onto the segmented brain in order to create functional brain maps. The registration has subpixel accuracy in a study with simulated data, while we obtain a point to surface root-mean-square error of 1.17/spl plusmn/0.38 mm in a 24 subject study.

Registration of 3D Intraoperative MR Images of the Brain Using a Finite Element Biomechanical Model

We present a new algorithm for the nonrigid registration of three-dimensional magnetic resonance (MR) intraoperative image sequences showing brain shift. The algorithm tracks key surfaces of objects (cortical surface and the lateral ventricles) in the image sequence using a deformable surface matching algorithm. The volumetric deformation field of the objects is then inferred from the displacements at the boundary surfaces using a linear elastic biomechanical finite-element model. Two experiments on synthetic image sequences are presented, as well as an initial experiment on intraoperative MR images showing brain shift. The results of the registration algorithm show a good correlation of the internal brain structures after deformation, and a good capability of measuring surface as well as subsurface shift. We measured distances between landmarks in the deformed initial image and the corresponding landmarks in the target scan. Cortical surface shifts of up to 10 mm and subsurface shifts of up to 6 mm were recovered with an accuracy of 1 mm or less and 3 mm or less respectively.

Real-Time Biomechanical Simulation of Volumetric Brain Deformation for Image Guided Neurosurgery

We aimed to study the performance of a parallel implementation of an intraoperative nonrigid registration algorithm that accurately simulates the biomechanical properties of the brain and its deformations during surgery. The algorithm was designed to allow for improved surgical navigation and quantitative monitoring of treatment progress in order to improve the surgical outcome and to reduce the time required in the operating room. We have applied the algorithm to two neurosurgery cases with promising results. High performance computing is a key enabling technology that allows the biomechanical simulation to be executed quickly enough for the algorithm to be practical. Our parallel implementation was evaluated on a symmetric multi-processor and two clusters and exhibited similar performance characteristics on each. The implementation was sufficiently fast to be used in the operating room during a neurosurgery procedure. It allowed a three-dimensional volumetric deformation to be simulated in less than ten seconds.

Tumor detection in the bladder wall with a measurement of abnormal thickness in CT scans

Virtual cystoscopy is a developing technique for bladder cancer screening. In a conventional cystoscopy, an optical probe is inserted into the bladder and an expert reviews the appearance of the bladder wall. Physical limitations of the probe place restrictions on the examination of the bladder wall. In virtual cystoscopy, a computed tomography (CT) scan of the bladder is acquired and an expert reviews the appearance of the bladder wall as shown by the CT. The task of identifying tumors in the bladder wall has often been done without extensive computational aid to the expert. We have developed an image processing algorithm that aids the expert in the detection of bladder tumors. Compared with an expert observer reading the CT, our algorithm achieves 89% sensitivity, 88% specificity, 48% positive predictive value, and 98% negative predictive value.

Deformable Modeling for Characterizing Biomedical Shape Changes

We present a new algorithm for modeling and characterizing shape changes in 3D image sequences of biomedical structures. Our algorithm tracks the shape changes of the objects depicted in the image sequence using an active surface algorithm. To characterize the deformations of the surrounding and inner volume of the objects surfaces, we use a physics-based model of the objects the image represents. In the applications we are presenting, our physics-based model is linear elasticity and we solve the corresponding equilibrium equations using the Finite Element (FE) method. To generate a FE mesh from the initial 3D image, we have developed a new multiresolution tetrahedral mesh generation algorithm specifically suited for labeled image volumes. The shape changes of the surfaces of the objects are used as boundary conditions to our physics-based FE model and allow us to infer a volumetric deformation field from the surface deformations. Physics-based measures such as stress tensor maps can then be derived from our model for characterizing the shape changes of the objects in the image sequence. Experiments on synthetic images as well as on medical data show the performances of the algorithm.

3D Image Matching Using a Finite Element Based Elastic Deformation Model

We present a new approach for the computation of the deformation field between three dimensional (3D) images. The deformation field minimizes the sum of the squared differences between the images to be matched and is constrained by the physical properties of the different objects represented by the image. The objects are modeled as elastic bodies. Compared to optical flow methods, this approach distinguishes itself by three main characteristics: it can account for the actual physical properties of the objects to be deformed, it can provide us with physical properties of the deformed objects (i.e. stress tensors), and computes a global solution to the deformation instead of a set of local solutions. This latter characteristic is achieved through a finite-element based scheme. The finite element approach requires the different objects in the images to be meshed. Therefore, a tetrahedral mesh generator using a pre-computed case table and specifically suited for segmented images has been developed. Preliminary experiments on simulated data as well as on medical data have been carried out successfully. Tested medical applications included muscle exercise imaging and ventricular deformation in multiple sclerosis.

Real-time simulation and visualization of volumetric brain deformation for image-guided neurosurgery

During neurosurgery, the challenge for the neurosurgeon is to remove as much as possible of a tumor without destroying healthy tissue. This can be difficult because healthy and diseased tissue can have the same visual appearance. To this aim, and because the surgeon cannot see underneath the brain surface, image-guided neurosurgery systems are being increasingly used. However, during surgery, deformation of the brain occurs (due to brain shift and tumor resection), therefore causing errors in the surgical planning with respect to preoperative imaging. In our previous work, we developed software for capturing the deformation of the brain during neurosurgery. The software also allows preoperative data to be updated according to the intraoperative imaging so as to reflect the shape changes of the brain during surgery. Our goal in this paper was to rapidly visualize and characterize this deformation over the course of surgery with appropriate tools. Therefore, we developed tools allowing the doctor to visualize (in 2D and 3D) deformations, as well as the stress tensors characterizing the deformation along with the updated preoperative and intraoperative imaging during the course of surgery. Such tools significantly add to the value of intraoperative imaging and hence could improve surgical outcomes.