Deep Learning How to Fit an Intravoxel Incoherent Motion Model to Diffusion-Weighted MRI

Purpose: This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted magnetic resonance imaging (DW-MRI) data and evaluates its performance. Methods: In May 2011, ten male volunteers (age range: 29 to 53 years, mean: 37 years) underwent DW-MRI of the upper abdomen on 1.5T and 3.0T magnetic resonance scanners. Regions of interest in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla were delineated independently by two readers. DNNs were trained for IVIM model fitting using these data; results were compared to least-squares and Bayesian approaches to IVIM fitting. Intraclass Correlation Coefficients (ICC) were used to assess consistency of measurements between readers. Intersubject variability was evaluated using Coefficients of Variation (CV). The fitting error was calculated based on simulated data and the average fitting time of each method was recorded. Results: DNNs were trained successfully for IVIM parameter estimation. This approach was associated with high consistency between the two readers (ICCs between 50 and 97%), low intersubject variability of estimated parameter values (CVs between 9.2 and 28.4), and the lowest error when compared with least-squares and Bayesian approaches. Fitting by DNNs was several orders of magnitude quicker than the other methods but the networks may need to be re-trained for different acquisition protocols or imaged anatomical regions. Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data. Suitable software is available at (1)

2-D and 3-D Radiation Transfer Models of High-Mass Star Formation

2-D and 3-D radiation transfer models of forming stars generally produce bluer 1-10 micron colors than 1-D models of the same evolutionary state and envelope mass. Therefore, 1-D models of the shortwave radiation will generally estimate a lower envelope mass and later evolutionary state than multidimensional models. 1-D models are probably reasonable for very young sources, or longwave analysis (wavelengths > 100 microns). In our 3-D models of high-mass stars in clumpy molecular clouds, we find no correlation between the depth of the 10 micron silicate feature and the longwave (> 100 micron) SED (which sets the envelope mass), even when the average optical extinction of the envelope is >100 magnitudes. This is in agreement with the observations of Faison et al. (1998) of several UltraCompact HII (UCHII) regions, suggesting that many of these sources are more evolved than embedded protostars. We have calculated a large grid of 2-D models and find substantial overlap between different evolutionary states in the mid-IR color-color diagrams. We have developed a model fitter to work in conjunction with the grid to analyze large datasets. This grid and fitter will be expanded and tested in 2005 and released to the public in 2006.Comment: 10 pages, 8 figures, to appear in the proceedings of IAU Symp 227, Massive Star Birth: A Crossroads of Astrophysics, (Cesaroni R., Churchwell E., Felli M., Walmsley C. editors

OVI, NV and CIV in the Galactic Halo: II. Velocity-Resolved Observations with Hubble and FUSE

We present a survey of NV and OVI (and where available CIV) in the Galactic halo, using data from the Far Ultraviolet Spectroscopic Explorer (FUSE) and the Hubble Space Telescope (HST) along 34 sightlines. These ions are usually produced in nonequilibrium processes such as shocks, evaporative interfaces, or rapidly cooling gas, and thus trace the dynamics of the interstellar medium. Searching for global trends in integrated and velocity-resolved column density ratios, we find large variations in most measures, with some evidence for a systematic trend of higher ionization (lower NV/OVI column density ratio) at larger positive line-of-sight velocities. The slopes of log[N(NV)/N(OVI)] per unit velocity range from -0.015 to +0.005, with a mean of -0.0032+/-0.0022(r)+/-0.0014(sys) dex/(km/s). We compare this dataset with models of velocity-resolved high-ion signatures of several common physical structures. The dispersion of the ratios, OVI/NV/CIV, supports the growing belief that no single model can account for hot halo gas, and in fact some models predict much stronger trends than are observed. It is important to understand the signatures of different physical structures to interpret specific lines of sight and future global surveys.Comment: ApJ in press 43 pages, 22 fig

Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models

We express the mean and variance terms in a double exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model, and whether they enter linearly or flexibly. When the variance term is null we obtain a generalized additive model, which becomes a generalized linear model if the predictors enter the mean linearly. The model is estimated using Markov chain Monte Carlo simulation and the methodology is illustrated using real and simulated data sets.Comment: 8 graphs 35 page