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We present a new algorithm to register 3-D preoperative magnetic resonance (MR) images to intraoperative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative(More)
In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard(More)
We introduce a method for combining fiber tracking from diffusion-tensor (DT) imaging with cortical gray matter parcellation from structural high-spatial-resolution 3D spoiled gradient-recalled acquisition in the steady state images. We applied this method to a tumor case to determine the impact of the tumor on white matter architecture. We conclude that(More)
PURPOSE To retrospectively assess the main variables that affect the complete magnetic resonance (MR) imaging-guided resection of supratentorial low-grade gliomas. MATERIALS AND METHODS Institutional review board approval was obtained for this retrospective HIPAA-compliant study, with the requirement for informed consent waived. Data from 101 patients (61(More)
The unambiguous localization of eloquent functional areas is necessary to decrease the neurological morbidity of neurosurgical procedures. We explored the minimum spatial resolution requirements for functional magnetic resonance imaging (fMRI) data acquisition when brain mapping is used in neurosurgical planning and navigation. Using a 1.5 Tesla clinical(More)
Since their introduction into surgical practice in the mid 1990s, intraoperative MRI systems have evolved into essential, routinely used tools for the surgical treatment of brain tumors in many centers. Clear delineation of the lesion, "under-the-surface" vision, and the possibility of obtaining real-time feedback on the extent of resection and the position(More)
BACKGROUND Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. OBJECTIVE This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor(More)
During neurosurgical procedures the objective of the neurosurgeon is to achieve the resection of as much diseased tissue as possible while achieving the preservation of healthy brain tissue. The restricted capacity of the conventional operating room to enable the surgeon to visualize critical healthy brain structures and tumor margin has lead, over the past(More)
In order to achieve its main goal of maximal tumor removal while avoiding postoperative neurologic deficits, neuro-oncological surgery is strongly dependent on image guidance. Among all currently available imaging modalities, MRI provides the best anatomic detail and is highly sensitive for intracranial pathology. However, conventional MRI does not detect(More)