46 research outputs found
Radial Surface Density Profiles of Gas and Dust in the Debris Disk around 49 Ceti
We present ~0.4 resolution images of CO(3-2) and associated continuum
emission from the gas-bearing debris disk around the nearby A star 49 Ceti,
observed with the Atacama Large Millimeter/Submillimeter Array (ALMA). We
analyze the ALMA visibilities in tandem with the broad-band spectral energy
distribution to measure the radial surface density profiles of dust and gas
emission from the system. The dust surface density decreases with radius
between ~100 and 310 au, with a marginally significant enhancement of surface
density at a radius of ~110 au. The SED requires an inner disk of small grains
in addition to the outer disk of larger grains resolved by ALMA. The gas disk
exhibits a surface density profile that increases with radius, contrary to most
previous spatially resolved observations of circumstellar gas disks. While ~80%
of the CO flux is well described by an axisymmetric power-law disk in Keplerian
rotation about the central star, residuals at ~20% of the peak flux exhibit a
departure from axisymmetry suggestive of spiral arms or a warp in the gas disk.
The radial extent of the gas disk (~220 au) is smaller than that of the dust
disk (~300 au), consistent with recent observations of other gas-bearing debris
disks. While there are so far only three broad debris disks with well
characterized radial dust profiles at millimeter wavelengths, 49 Ceti's disk
shows a markedly different structure from two radially resolved gas-poor debris
disks, implying that the physical processes generating and sculpting the gas
and dust are fundamentally different.Comment: 20 pages, 8 figures, accepted for publication in ApJ March 31, 2017
(submitted Nov 2016
Debris Disks: Probing Planet Formation
Debris disks are the dust disks found around ~20% of nearby main sequence
stars in far-IR surveys. They can be considered as descendants of
protoplanetary disks or components of planetary systems, providing valuable
information on circumstellar disk evolution and the outcome of planet
formation. The debris disk population can be explained by the steady
collisional erosion of planetesimal belts; population models constrain where
(10-100au) and in what quantity (>1Mearth) planetesimals (>10km in size)
typically form in protoplanetary disks. Gas is now seen long into the debris
disk phase. Some of this is secondary implying planetesimals have a Solar
System comet-like composition, but some systems may retain primordial gas.
Ongoing planet formation processes are invoked for some debris disks, such as
the continued growth of dwarf planets in an unstirred disk, or the growth of
terrestrial planets through giant impacts. Planets imprint structure on debris
disks in many ways; images of gaps, clumps, warps, eccentricities and other
disk asymmetries, are readily explained by planets at >>5au. Hot dust in the
region planets are commonly found (<5au) is seen for a growing number of stars.
This dust usually originates in an outer belt (e.g., from exocomets), although
an asteroid belt or recent collision is sometimes inferred.Comment: Invited review, accepted for publication in the 'Handbook of
Exoplanets', eds. H.J. Deeg and J.A. Belmonte, Springer (2018
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Background: Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images.
Methods: Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV).
Results: By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement was 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric was 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability.
Conclusions: We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures