R.J. Frank

Brainvox: an interactive, multimodal visualization and analysis system for neuroanatomical imaging.

A study of cognition emerging from a neurobiological perspective, as opposed to one emerging from a purely computational or psychological perspective, begins with observations of the human brain in normal and pathological states and is furthered by the investigation of hypotheses which are articulated using neuroanatomical nomenclature. Brainvox is an interactive three-dimensional brain imaging software package designed to permit such research through the support of the description and quantification of brain pathology in magnetic resonance images and of the experimental investigation of human cognition in lesion and functional imaging studies. Important general features of Brainvox, for these purposes, are: (1) adaptation of volume rendering for brain lesions and for corendered datasets; (2) shared memory architecture, which enables the user to identify and label anatomical structures, while inspecting the brain in multiple views simultaneously; (3) modular program design, including interlocking command-line utilities, which make Brainvox extensible and empower users without programming expertise to implement new analysis techniques through Unix shell scripting; and (4) full integration of three-dimensional tools for visualization with tools for analysis. Specific features include a new object templating technique (MAP-3) for studies of groups of brain-lesioned subjects, a complete and extensible suite of command-line processing utilities, a three-dimensional optimal graph-searching tool, and a method for planning PET slices and matching MR and PET slices (MP_FIT).

Validation of partial tissue segmentation of single-channel magnetic resonance images of the brain.

We describe and evaluate a practical, automated algorithm based on local statistical mixture modeling for segmenting single-channel, T1-weighted volumetric magnetic resonance images of the brain into gray matter, white matter, and cerebrospinal fluid. We employed a stereological sampling method to assess, prospectively, the performance of the method with respect to human experts on 10 normal T1-weighted brain scans acquired with a three-dimensional gradient echo pulse sequence. The overall kappa statistic for the concordance of the algorithm with the human experts was 0.806, while that among raters, excluding the algorithm, was 0.802. The algorithm had better agreement with the modal expert decision (kappa = 0.878). The algorithm could not be distinguished from the experts by this measure. We also validated the algorithm on a simulated MR scan of a digital brain phantom with known tissue composition. Global gray matter and white matter errors were 1% and <1%, respectively, and correlation coefficients with the underlying tissue model were 0.95 for gray matter, 0.98 for white matter, and 0.95 for cerebrospinal fluid. In both approaches to validation, we evaluated both local and global performance of the algorithm. Human experts generated slightly higher global gray matter proportion estimates on the test brain scans relative to the algorithm (3.7%) and on the simulated MR scan relative to the true tissue model (4.4%). The algorithm underestimated gray in some subcortical nuclei which contain admixed gray and white matter. We demonstrate the reliability of the method on individual 1 NEX data sets of the test subjects, and its insensitivity to the precise values of initial model parameters. The output of this algorithm is suitable for quantifying cerebral cortical tissue, using a commonly performed commercial pulse sequence.

Real-time multiple linear regression for fMRI supported by time-aware acquisition and processing

Real‐time parametric statistical analysis of functional MRI (fMRI) data would potentially enlarge the scope of experimentation and facilitate its application to clinical populations. A system is described that addresses the need for rapid analysis of fMRI data and lays the foundation for dealing with problems that impede the application of fMRI to clinical populations. The system, I/OWA (Input/Output time‐aWare Architecture), combines a general architecture for sampling and time‐stamping relevant information channels in fMRI (image acquisition, stimulation, subject responses, cardiac and respiratory monitors, etc.) and an efficient approach to manipulating these data, featuring incremental subsecond multiple linear regression. The advantages of the system are the simplification of event timing and efficient and unified data formatting. Substantial parametric analysis can be performed and displayed in real‐time. Immediate (replay) and delayed off‐line analysis can also be performed with the same interface. The capabilities of the system are demonstrated in normal subjects using a polar visual angle phase mapping paradigm. The system provides a time‐accounting infrastructure that readily supports standard and innovative approaches to fMRI. Magn Reson Med 45:289–298, 2001.