PCA Tomography: how to extract information from data cubes

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies, it is possible to obtain data cubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We present a method of analysis of data cube (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data on to these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal, and this information is fundamental for their handling and interpretation. When the data cube shows objects that present uncorrelated physical phenomena, the eigenvector`s orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms, one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge.

Arterial spin labeling of cerebral perfusion territories using a seperate labeling coil

Purpose: To obtain cerebral perfusion territories of the left, the right. and the posterior circulation in humans with high signal-to-noise ratio (SNR) and robust delineation. Materials and Methods: Continuous arterial spin labeling (CASL) was implemented using a dedicated radio frequency (RF) coil. positioned over the neck, to label the major cerebral feeding arteries in humans. Selective labeling was achieved by flow-driven adiabatic fast passage and by tilting the longitudinal labeling gradient about the Y-axis by theta = +/- 60 degrees. Results: Mean cerebral blood flow (CBF) values in gray matter (GM) and white matter (WM) were 74 +/- 13 mL center dot 100 g(-1) center dot minute(-1) and 14 +/- 13 mL center dot 100 g(-1) center dot minute(-1), respectively (N = 14). There were no signal differences between left and right hemispheres when theta = 0 degrees (P > 0.19), indicating efficient labeling of both hemispheres. When theta = +60 degrees, the signal in GM on the left hemisphere, 0.07 +/- 0.06%, was 92% lower than on the right hemisphere. 0.85 +/- 0.30% (P < 1 x 10(-9)). while for theta = -60 degrees, the signal in the right hemisphere. 0.16 +/- 0.13%, was 82% lower than on the contralateral side. 0.89 +/- 0.22% (P < 1 x 10(-10)). Similar attenuations were obtained in WM. Conclusion: Clear delineation of the left and right cerebral perfusion territories was obtained, allowing discrimination of the anterior and posterior circulation in each hemisphere.

A non-parametric vessel detection method for complex vascular structures

Modern medical imaging techniques enable the acquisition of in vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all, but the Most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder assumption. We directly exploit local neighborhood intensities and extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile. We present a new method to capture the common properties shared by polar neighborhood intensity profiles for all the types of vascular points belonging to the vascular system. The new method enables us to detect vessels even near complex extreme points, including branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and 3D animal and clinical vascular images, particularly close to vessel branching regions. (C) 2008 Elsevier B.V. All rights reserved.

Multi-Conjugate Adaptive Optics VLT imaging of the distant old open cluster FSR 1415

We employ the recently installed near-infrared Multi-Conjugate Adaptive Optics demonstrator (MAD) to determine the basic properties of a newly identified, old and distant, Galactic open cluster (FSR 1415). The MAD facility remarkably approaches the diffraction limit, reaching a resolution of 0.07 arcsec (in K), that is also uniform in a field of similar to 1.8 arcmin in diameter. The MAD facility provides photometry that is 50 per cent complete at K similar to 19. This corresponds to about 2.5 mag below the cluster main-sequence turn-off. This high-quality data set allows us to derive an accurate heliocentric distance of 8.6 kpc, a metallicity close to solar and an age of similar to 2.5 Gyr. On the other hand, the deepness of the data allows us to reconstruct (completeness-corrected) mass functions (MFs) indicating a relatively massive cluster, with a flat core MF. The Very Large Telescope/MAD capabilities will therefore provide fundamental data for identifying/analysing other faint and distant open clusters in the Galaxy III and IV quadrants.

Mapping low- and high-density clouds in astrophysical nebulae by imaging forbidden line emission

Emission line ratios have been essential for determining physical parameters such as gas temperature and density in astrophysical gaseous nebulae. With the advent of panoramic spectroscopic devices, images of regions with emission lines related to these physical parameters can, in principle, also be produced. We show that, with observations from modern instruments, it is possible to transform images taken from density-sensitive forbidden lines into images of emission from high- and low-density clouds by applying a transformation matrix. In order to achieve this, images of the pairs of density-sensitive lines as well as the adjacent continuum have to be observed and combined. We have computed the critical densities for a series of pairs of lines in the infrared, optical, ultraviolet and X-rays bands, and calculated the pair line intensity ratios in the high- and low-density limit using a four- and five-level atom approximation. In order to illustrate the method, we applied it to Gemini Multi-Object Spectrograph (GMOS) Integral Field Unit (GMOS-IFU) data of two galactic nuclei. We conclude that this method provides new information of astrophysical interest, especially for mapping low- and high-density clouds; for this reason, we call it `the ld/hd imaging method`.