Computed Tomography (CT) imaging provides a very promising technology for lung cancer detection and treatment. The newest multi-slice CT machines provide high-resolution scans quickly enough that the entire lung volume may be covered in a single breath-hold, and at a low enough radiation dose to make it practical for screening.However, the large amounts of data obtained by these machines present formidable challenges to physicians. A typical scan with slices taken 1 mm apart may have 300 or more image slices. If CT lung cancer screening becomes widespread, there will be tremendous demand for such examinations. Clearly, some sort of computer assistance will be highly beneficial to physicians in coping with such large amounts of data.We present a real time system for interactive assistance in diagnosing, monitoring, and measuring lung nodules in CT images. This interactive system allows physicians to incorporate their knowledge with the computer's ability to perform rapid computations, in order to improve decision-making about diagnosis and treatment. This system may allow for a more efficient workflow, and enhance the acceptance of Computer-Aided Diagnosis.

A new fast non rigid registration algorithm is presented. The algorithm estimates a dense deformation field by optimizing a criterion that measures image similarity by mutual information and regularizes with a linear elastic energy term. The optimal deformation field is found using a Simultaneous Perturbation Stochastic Approximation to the gradient. The implementation is parallelized for symmetric multi-processor architectures. This algorithm was applied to capture non-rigid brain deformations that occur during neurosurgery. Segmentation of the intra-operative data is not required but preoperative segmentation of the brain allows the algorithm to be robust to artifacts due to the craniotomy.

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 +/- 0.38 mm in a 24 subject study.

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.

The key challenge facing the neurosurgeon during neurosurgery is to be able to remove from the brain as much tumor tissue as possible while preserving healthy tissue and minimizing the disruption of critical anatomical structures. The purpose of this work was to demonstrate the use of biomechanical simulation of brain deformation to project preoperative fMRI and DTI data into the coordinate system of the patient brain deformed during neurosurgery. This projection enhances the visualization of relevant, critical structures available to the neurosurgeon.Our approach to tracking brain changes during neurosurgery has been previously described. We applied this procedure to warp preoperative fMRI and DTI to match intraoperative MRI. We constructed visualizations of preoperative fMRI and DTI, and intraoperative MRI showing a close correspondence between the matched data.We have previously demonstrated our biomechanical simulation of brain deformation can be executed entirely during neurosurgery. We previously used a generic atlas as a substitute for patient specific data. Here we report the successful alignment of patient-specific DTI and fMRI preoperative data into the intraoperative configuration of the patient's brain. This can significantly enhance the information available to the neurosurgeon.

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.

We describe a method for the registration of functional brain data acquired with transcranial magnetic stimulation (TMS) on MRI brain images. TMS is a non-invasive method largely used in the study of brain functions. For the registration process we acquire 150 points on the patient's scalp with a magnetic-field digitizer. Then, we minimize the mean square distance between those points and the segmented scalp surface drawn from the MR image. The distance to the scalp surface is computed with the help of a 3D Euclidean distance transformation. For each stimulation, the position of the TMS device is acquired with the digitizer. The registration transformation is applied to the TMS coordinate in order to map TMS data and anatomical information. The results show that the method is precise (4 mm) and reproducible (1 mm).

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.

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 object's 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.

Common problems in medical image analysis involve surface-based registration. The applications range from atlas matching to tracking an object's boundary in an image sequence, or segmenting anatomical structures out of images. Most proposed solutions are based on deformable surface algorithms. The main problem of such methods is that the local accuracy of the matching must often be traded off against global smoothness of the surface in order to reach global convergence of the deformation process. Our contribution is to first build a Multi-Resolution (M-R) surface from a reference segmented image, and then match this surface onto the target image in an M-R fashion using a deformable surface-like algorithm. As we proceed from lower to higher resolution, the smoothing effect of the deformable surface is more and more localized, and the surface gets closer and closer to the target boundary. We present initial results of our algorithm for atlas registration onto brain MRI showing improved convergence and accuracy over classical deformable surface methods.

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.

Transcranial Magnetic Stimulation (TMS) has been widely used in the mapping of the primary motor cortex, as well as in the study of language, memory, mood, auditory or visual perception [4]. Similarly to EEG or MEG, it provides information - Motor Evoked Potentials (MEP) - located on the surface of the scalp and requires a registration between the physical space (PhS) and a MRI for proper interpretation.

Image guided neurosurgery is employed to help surgeons distinguish between healthy and diseased brain tissues, which can have similar visual appearance. It has been our goal to increase the information-content of intraoperative images by tracking volumetric brain deformations intraoperatively, and using this to match preoperative data. A finite element based biomechanical model was used during two neurosurgical procedures to capture non-rigid deformations of critical structures, extracted from a deformable volumetric digital brain atlas. These structures were mapped onto intraoperative Magnetic Resonance (MR) images . Our method meets the real-time constraints of neurosurgery and allows the visualization of multi-modality data, not available during surgery, together with intraoperative MR data.

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.

We present a method based on multiresolution signal processing on meshes to create a thickness atlas. We applied this method to construct an atlas of bladder wall thickness. Bladder cancer is associated with increased bladder wall thickness. A thickness atlas helps to detect abnormal thickening in the bladder wall. Extracting inner and outer surface meshes from segmented images, we compute the thickness on the inner surface and map it to a sphere. We average the thickness at each position on the sphere to create a thickness atlas. We then compute Z-score values on the configuration of the patient's bladder to show regions of unusual thickness.

The automatic identification and localization of structures in magnetic resonance (MR) brain images are a major part of the processing work for the neuroradiologist in numerous clinical applications, such as functional mapping and surgical planning. To aid in this task, a considerable amount of research has been directed toward the development of 3D standardized atlases of the human brain (e.g. [5]). These provide an invariant reference system and the possibility of template matching, allowing anatomical and functional structures in new scans to be identified and analyzed.