Abstract:The Hinode satellite (formerly Solar-

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 ...

Three-dimensional (3D) dynamics of a large-scale magnetic loop is studied by precise magnetohydrodynamic simulations on the basis of the spontaneous fast reconnection model. Once a (current-driven) anomalous resistivity is ignited, the fast reconnection mechanism drastically evolves by the positive feedback between the (3D) global reconnection flow and the anomalous resistivity; on the nonlinear saturation phase, the global reconnection flow has grown so that the reconnection (diffusion) region shrinks to a small extent, and the fast reconnection mechanism involving a pair of standing slow shocks is established in the finite extent. When the 3D plasmoid, formed ahead of the fast reconnection jet, collides with the mirror plane boundary, the reconnected field lines are piled up, leading to formation of a large-scale 3D magnetic loop. Since the resulting 3D fast reconnection jet becomes supersonic, a definite fast shock builds up at the interface between the magnetic loop top and the fast reconnection jet. The 3D fast reconnection jet is limited in a narrow channel between the pair of slow shocks, so that the resulting fast shock is also limited to a small extent ahead of the magnetic loop top. On the other hand, for the uniform resistivity model the 3D fast reconnection mechanism cannot be realized without any vital positive feedback between the reconnection flow and the local magnetic diffusion; hence, such an effective resistivity that can be self-consistently enhanced locally at the X reconnection point by the global reconnection flow is essential for the fast reconnection mechanism to be realized in actual systems.

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The spontaneous evolution of fast reconnection is studied in three dimensions by extending (in the z direction) the previous two-dimensional model that considered only the x-y plane [M. Ugai, Phys. Fluids B 4, 2953 (1992)]. It is demonstrated that the reconnection development strongly depends on three-dimensional effects; only when the central current sheet is sufficiently long in the z direction, say more than a few times larger than the current sheet width, the fast reconnection mechanism fully develops by the self-consistent coupling between the global reconnection flow and the current-driven anomalous resistivity. In this case, the reconnection flow can grow so powerfully as to enhance the current density (the current-driven resistivity) locally near an X line; otherwise, such a vital reconnection flow cannot be caused. The resulting quasisteady fast reconnection mechanism is significantly confined in the z direction, where a strong (Alfvénic) plasma jet results from standing switch-off shocks; accordingly, a large-scale plasmoid is formed and propagates in the middle of the system. It is concluded that the well-known two-dimensional spontaneous fast reconnection model can reasonably be extended to three dimensions.