DT imaging-based fiber tracking is a reliable and accurate method for mapping the course of subcortical PTs. Fiber tracking and intraoperative MEPs were useful for preserving motor function in patients with gliomas near the PT.

There is wide variation in brain shape among birds. Differences in brain dimensions reflect species-specific sensory capacities and behavioral repertoires that are shaped by environmental and biological factors during evolution. Most previous studies aimed at defining factors impacting brain shape have used volumetric or linear measurements. However, few have explored the quantitative indices of three-dimensional (3D) brain geometry that are absolutely imperative to understanding avian evolutionary history. This study aimed: (i) to explore the relationship between brain shape and overall brain size; and (ii) to assess the relationship between brain shape and orbital shape. Avian brain endocasts were reconstructed from computed tomography images and analyzed using 3D geometric morphometrics. Principal component analysis revealed dominant regional variations in avian brain shape and shape correlations between the telencephalon and cerebellum, between the cerebellum and myelencephalon, and between the diencephalon and optic tectum. Brain shape changes relative to total brain size were determined by multivariate regression analysis. Larger brain size was associated with a relatively slender telencephalon and differences in brain orientation. The correlation between brain shape and orbital shape was assessed by two-block partial least-squares analysis. Relatively round brains with a ventrally flexed brain base were associated with rounder orbits, while narrower brains with a flat brain base were associated with more elongated orbits. The shapes of functionally associated avian brain regions are correlated, and orbital size and shape are dominant factors influencing the overall shape of the avian brain.

An accurate determination of the arterial input function (AIF) is necessary for quantification of cerebral blood flow (CBF) using dynamic susceptibility contrast-enhanced magnetic resonance imaging. In this study, we developed a method for obtaining the AIF automatically using fuzzy c-means (FCM) clustering. The validity of this approach was investigated with computer simulations. We found that this method can automatically extract the AIF, even under very noisy conditions, e.g., when the signal-to-noise ratio is 2. The simulation results also indicated that when using a manual drawing of a region of interest (ROI) (manual ROI method), the contamination of surrounding pixels (background) into ROI caused considerable overestimation of CBF. We applied this method to six subjects and compared it with the manual ROI method. The CBF values, calculated using the AIF obtained using the manual ROI method [CBF ( Index terms: fuzzy c-means clustering; dynamic susceptibility contrast-enhanced MR imaging; arterial input function; cerebral blood flow; quantification MAGNETIC RESONANCE IMAGING (MRI) techniques that measure cerebral perfusion have become increasingly important. Compared with methods using single photon emission computed tomography (SPECT) or positron emission tomography (PET), quantification of cerebral perfusion using dynamic MRI has advantages such as improved spatial resolution, no patient exposure to ionizing radiation, and the opportunity to combine morphological and functional information during a single imaging session (1).The use of an intravascular contrast agent in combination with dynamic susceptibility-contrast (DSC) MRI for measurement of cerebral perfusion is an attractive concept, although not completely straightforward (1). For quantification of perfusion parameters such as cerebral blood flow (CBF) and cerebral blood volume (CBV), in terms of the absolute values using DSC-MRI, the arterial input function (AIF) of the contrast agent entering the tissue has to be determined (2,3,4). When an artery (e.g., internal carotid artery) runs through imaged slices of the cortex, the AIF can be obtained non-invasively from the arterial pixels. Traditional methods for the extraction of arterial pixels require the manual drawing of a region of interest (ROI) (5). Vonken et al (5) obtained the AIF by segmenting the arterial pixels manually from a slice through the neck with the internal carotid arteries. However, due to finite spatial resolution and large statistical noise, manual ROI analysis may lead to serious inconsistencies in the definition of arterial pixels. In addition, it may be subjective.Rempp et al developed an interactive computer program to determine AIF automatically (2). In their program, AIF is determined by following two steps. In the first step, certain parameters describing the concentration-time curves, such as the full width at half maximum (FWHM), the maximum concentration (MC), and the moment of maximum concentration (MMC), are calculated pixel by pixel for the whole brain. The mean ...

MR angiography is useful as the primary diagnostic tool for evaluating suspected intracranial steno-occlusive disease.

No abstract