A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients.

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 evolution is very slow and may be obscured by artifacts related to image acquisition.

A non-rigid registration framework that accommodates resection and retraction.

Traditional non-rigid registration algorithms are incapable of accurately registering intra-operative with pre-operative images whenever tissue has been resected or retracted. In this work we present methods for detecting and handling retraction and resection. The registration framework is based on the bijective Demons algorithm using an anisotropic diffusion smoother.

Computational neuroanatomy: ontology-based representation of neural components and connectivity.

Background: A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning.

Results: We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems.

A prototype symbolic model of canonical functional neuroanatomy of the motor system.

Recent advances in bioinformatics have opened entire new avenues for organizing, integrating and retrieving neuroscientific data, in a digital, machine-processable format, which can be at the same time understood by humans, using ontological, symbolic data representations. Declarative information stored in ontological format can be perused and maintained by domain experts, interpreted by machines, and serve as basis for a multitude of decision support, computerized simulation, data mining, and teaching applications. We have developed a prototype symbolic model of canonical neuroanatomy of the motor system.

Quantitative comparison of functional MRI and direct electrocortical stimulation for functional mapping.

Background: Mapping functional areas of the brain is important for planning tumour resections. With the increased use of functional magnetic resonance imaging (fMRI) for presurgical planning, there is a need to validate that fMRI activation mapping is consistent with the mapping obtained during surgery using direct electrocortical stimulation (DECS).

Methods: A quantitative comparison of DECS and fMRI mapping techniques was performed, using a patient-specific conductivity model to find the current distribution resulting from each stimulation site.

Volumetric assessment of tumor infiltration of adjacent white matter based on anatomic MRI and diffusion tensor tractography.

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).

Hybrid formulation of the model-based non-rigid registration problem to improve accuracy and robustness.

We present a new algorithm to register 3D pre-operative Magnetic Resonance (MR) images with intra-operative MR images of the brain. This algorithm relies on a robust estimation of the deformation from a sparse set of measured displacements. We propose a new framework to compute iteratively the displacement field starting from an approximation formulation (minimizing the sum of a regularization term and a data error term) and converging toward an interpolation formulation (least square minimization of the data error term).

Combining classifiers using their receiver operating characteristics and maximum likelihood estimation.

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.

Supratentorial low-grade glioma resectability: statistical predictive analysis based on anatomic MR features and tumor characteristics.

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 men, 40 women; mean age, 39 years; age range, 18-72 years) who had nonenhancing supratentorial mass lesions that were histopathologically diagnosed as low-grade (World Health Organization grade II) gliomas and consecutively underwent surgery with intraoperative MR imaging guidance were analyzed.

Magnetic resonance and the human brain: anatomy, function and metabolism.

The introduction and development, over the last three decades, of magnetic resonance (MR) imaging and MR spectroscopy technology for in vivo studies of the human brain represents a truly remarkable achievement, with enormous scientific and clinical ramifications. These effectively non-invasive techniques allow for studies of the anatomy, the function and the metabolism of the living human brain. They have allowed for new understandings of how the healthy brain works and have provided insights into the mechanisms underlying multiple disease processes which affect the brain.

Robust nonrigid registration to capture brain shift from intraoperative MRI.

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 process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term).

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

Objective: Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap.

Statistical validation of brain tumor shape approximation via spherical harmonics for image-guided neurosurgery.

Rationale And Objectives: Surgical planning now routinely uses both two-dimensional (2D) and three-dimensional (3D) models that integrate data from multiple imaging modalities, each highlighting one or more aspects of morphology or function. We performed a preliminary evaluation of the use of spherical harmonics (SH) in approximating the 3D shape and estimating the volume of brain tumors of varying characteristics.

Materials And Methods: Magnetic resonance (MR) images from five patients with brain tumors were selected randomly from our MR-guided neurosurgical practice.

Capturing intraoperative deformations: research experience at Brigham and Women's Hospital.

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 decade, to the development of sophisticated intraoperative imaging techniques to enhance visualization. However, both rigid motion due to patient placement and nonrigid deformations occurring as a consequence of the surgical intervention disrupt the correspondence between preoperative data used to plan surgery and the intraoperative configuration of the patient's brain.

Method for combining information from white matter fiber tracking and gray matter parcellation.

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 this new method for combining structural and DT imaging data is useful for understanding cortical connectivity and the localization of fiber tracts and their relationship with cortical anatomy and brain abnormalities.

The tale of Phineas Gage, digitally remastered.

The injury of Phineas Gage has fueled research on and fascination with the localization of cerebral functions in the past century and a half. Most physicians and anatomists believed that Gage sustained a largely bilateral injury to the frontal lobes. However, previous studies seem to have overlooked a few less obvious, but essential details.

Evaluating requirements for spatial resolution of fMRI for neurosurgical planning.

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.

Intraoperative magnetic resonance imaging and magnetic resonance imaging-guided therapy for brain tumors.

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 of residual tumor tissue (which may change during surgery due to "brain-shift") are the main strengths of this method. High-performance computing has further extended the capabilities of intraoperative MRI systems, opening the way for using multimodal information and 3D anatomical reconstructions, which can be updated in "near real time.

Surgical navigation in the open MRI.

The introduction of MRI into neurosurgery has opened multiple avenues, but also introduced new challenges. The open-configuration intraoperative MRI installed at the Brigham and Women's Hospital in 1996 has been used for more than 500 open craniotomies and beyond 100 biopsies. Furthermore the versatile applicability, employing the same principles, is evident by its frequent use in other areas of the body.

Neuronavigation in interventional MR imaging. Frameless stereotaxy.

The main thrust of diagnostic MR imaging is to discern normal and pathologic patient morphology and function. Intraprocedural imaging, however, serves a different goal: to furnish the surgeon or interventionalist with updates on intraoperative changes and how they may modify preintervention data. Although researchers have not established whether MR image-guided therapy can improve clinical outcomes and reduce complication rates definitively, the intraoperative and preoperative data generated will improve the ability of every neurosurgeon to navigate in the surgical field more accurately.

Necrotropic protective effect of Ca-Mg gluconolactate on allyl alcohol-induced liver necrosis in rats.

The prevention of liver damage in rats intoxicated with allyl alcohol was attempted with 5 per cent Ca--Mg gluconolactate in reiterated doses. This preparation prevented or reduced liver necrosis only in the presence of spleen: splenectomy annihilated the effect of Ca--Mg gluconolactate.