RATIONALE AND OBJECTIVES: To perform a retrospective, quantitative assessment of the anatomic relationship between intra-axial, supratentorial, primary brain tumors, and adjacent white matter fiber tracts based on anatomic and diffusion tensor magnetic resonance imaging (MRI). We hypothesized that white matter infiltration may be common among different types of tumor. MATERIAL AND METHODS: Preoperative, anatomic (T1- and T2-weighted), and LINESCAN diffusion tensor MRI were obtained in 12 patients harboring supratentorial gliomas (World Health Organization [WHO] Grades II and III). The two imaging modalities were rigidly registered. The tumors were manually segmented from the T1- and T2-weighted MRI, and their volume calculated. A three-dimensional tractography was performed in each case. A second segmentation and volume measurement was performed on the tumor regions intersecting adjacent white matter fiber tracts. Statistical methods included summary statistics to examine the fraction of tumor volume infiltrating adjacent white matter. RESULTS: There were five patients with low-grade oligodendroglioma (WHO Grade II), one with low-grade mixed oligoastrocytoma (WHO Grade II), one with ganglioglioma, two with low-grade astrocytoma (WHO Grade II), and three with anaplastic astrocytoma (WHO Grade III). We identified white matter tracts infiltrated by tumor in all 12 cases. The median tumor volume (+/- standard deviation) in our patient population was 42.5 +/- 28.9 mL. The median tumor volume (+/- standard deviation) infiltrating white matter fiber tracts was 5.2 +/- 9.9 mL. The median percentage of tumor volume infiltrating white matter fiber tracts was 21.4% +/- 9.7%. CONCLUSIONS: The information provided by diffusion tensor imaging combined with anatomic MRI might be useful for neurosurgical planning and intraoperative guidance. Our results confirm previous reports that extensive white matter infiltration by primary brain tumors is a common occurrence. However, prospective, large population studies are required to definitively clarify this issue, and how infiltration relates to histologic tumor type, tumor size, and location.

In prostate cancer treatment, there is a move toward targeted interventions for biopsy and therapy, which has precipitated the need for precise image-guided methods for needle placement. This paper describes an integrated system for planning and performing percutaneous procedures with robotic assistance under MRI guidance. A graphical planning interface allows the physician to specify the set of desired needle trajectories, based on anatomical structures and lesions observed in the patient’s registered pre-operative and pre-procedural MR images, immediately prior to the intervention in an open-bore MRI scanner. All image-space coordinates are automatically computed, and are used to position a needle guide by means of an MRI-compatible robotic manipulator, thus avoiding the limitations of the traditional fixed needle template. Automatic alignment of real-time intra-operative images aids visualization of the needle as it is manually inserted through the guide. Results from in-scanner phantom experiments are provided.

In this paper a comprehensive framework for pre-operative planning, procedural skill training, and intraoperative navigation is presented. The goal of this system is to integrate surgical simulation with surgical planning in order to improve the individual treatment of patients. Various surgical approaches and new, more complex procedures can be assessed using a safe and objective platform that will allow the physicians to explore and discuss possible risks and benefits prior to the intervention. A simulation environment extends the pre-operative planning in a natural way, as it allows for direct evaluation of the surgical approach envisioned for each case. In addition, by providing intraoperative navigation based on this simulation, surgeons can carry out the previously optimized plan with higher precision and greater confidence.

This paper presents a novel multiscale shape representation and segmentation algorithm based on the spherical wavelet transform. This work is motivated by the need to compactly and accurately encode variations at multiple scales in the shape representation in order to drive the segmentation and shape analysis of deep brain structures, such as the caudate nucleus or the hippocampus. Our proposed shape representation can be optimized to compactly encode shape variations in a population at the needed scale and spatial locations, enabling the construction of more descriptive, nonglobal, nonuniform shape probability priors to be included in the segmentation and shape analysis framework. In particular, this representation addresses the shortcomings of techniques that learn a global shape prior at a single scale of analysis and cannot represent fine, local variations in a population of shapes in the presence of a limited dataset. Specifically, our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to two different brain structures, the caudate nucleus and the hippocampus, of interest in the study of schizophrenia. We show: 1) a reconstruction task of a test set to validate the expressiveness of our multiscale prior and 2) a segmentation task. In the reconstruction task, our results show that for a given training set size, our algorithm significantly improves the approximation of shapes in a testing set over the Point Distribution Model, which tends to oversmooth data. In the segmentation task, our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm, by capturing finer shape details.

The integration of medical devices with software applications is crucial for image-guided medical applications. This work describes a general device interface that has been designed for high-frequency streaming of multi-modal events, thus providing maximum performance and flexibility for such applications. Several sample applications and performance tests are provided to demonstrate the usability of the concept.