CANDELS Multi-wavelength Catalogs: Source Detection and Photometry in the GOODS-South Field

We present a UV-to-mid infrared multi-wavelength catalog in the CANDELS/GOODS-S field, combining the newly obtained CANDELS HST/WFC3 F105W, F125W, and F160W data with existing public data. The catalog is based on source detection in the WFC3 F160W band. The F160W mosaic includes the data from CANDELS deep and wide observations as well as previous ERS and HUDF09 programs. The mosaic reaches a 5$\sigma$ limiting depth (within an aperture of radius 0.17 arcsec) of 27.4, 28.2, and 29.7 AB for CANDELS wide, deep, and HUDF regions, respectively. The catalog contains 34930 sources with the representative 50% completeness reaching 25.9, 26.6, and 28.1 AB in the F160W band for the three regions. In addition to WFC3 bands, the catalog also includes data from UV (U-band from both CTIO/MOSAIC and VLT/VIMOS), optical (HST/ACS F435W, F606W, F775W, F814W, and F850LP), and infrared (HST/WFC3 F098M, VLT/ISAAC Ks, VLT/HAWK-I Ks, and Spitzer/IRAC 3.6, 4.5, 5.8, 8.0 $\mu$m) observations. The catalog is validated via stellar colors, comparison with other published catalogs, zeropoint offsets determined from the best-fit templates of the spectral energy distribution of spectroscopically observed objects, and the accuracy of photometric redshifts. The catalog is able to detect unreddened star-forming (passive) galaxies with stellar mass of 10^{10}M_\odot at a 50% completeness level to z$\sim$3.4 (2.8), 4.6 (3.2), and 7.0 (4.2) in the three regions. As an example of application, the catalog is used to select both star-forming and passive galaxies at z$\sim$2--4 via the Balmer break. It is also used to study the color--magnitude diagram of galaxies at 0<z<4.Comment: The full resolution article is now published in ApJS (2013, 207, 24). 22 pages, 21 figures, and 5 tables. The catalogue is available on the CANDELS website: http://candels.ucolick.org/data_access/GOODS-S.html MAST: http://archive.stsci.edu/prepds/candels and Rainbow Database: https://arcoiris.ucolick.org/Rainbow_navigator_public and https://rainbowx.fis.ucm.es/Rainbow_navigator_publi

New Techniques for High-Contrast Imaging with ADI: the ACORNS-ADI SEEDS Data Reduction Pipeline

We describe Algorithms for Calibration, Optimized Registration, and Nulling the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized software package to reduce high-contrast imaging data, and its application to data from the SEEDS survey. We implement several new algorithms, including a method to register saturated images, a trimmed mean for combining an image sequence that reduces noise by up to ~20%, and a robust and computationally fast method to compute the sensitivity of a high-contrast observation everywhere on the field-of-view without introducing artificial sources. We also include a description of image processing steps to remove electronic artifacts specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed analysis of the Locally Optimized Combination of Images (LOCI) algorithm commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in python. It is efficient and open-source, and includes several optional features which may improve performance on data from other instruments. ACORNS-ADI requires minimal modification to reduce data from instruments other than HiCIAO. It is freely available for download at www.github.com/t-brandt/acorns-adi under a BSD license.Comment: 15 pages, 9 figures, accepted to ApJ. Replaced with accepted version; mostly minor changes. Software update

Image correlation and sampling study

The development of analytical approaches for solving image correlation and image sampling of multispectral data is discussed. Relevant multispectral image statistics which are applicable to image correlation and sampling are identified. The general image statistics include intensity mean, variance, amplitude histogram, power spectral density function, and autocorrelation function. The translation problem associated with digital image registration and the analytical means for comparing commonly used correlation techniques are considered. General expressions for determining the reconstruction error for specific image sampling strategies are developed

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure

Quantifying Regional and Global Liver Function Via Gadoxetic Acid Uptake

Liver function is a dominant factor in the survival of patients with hepatocellular carcinoma (HCC). Measures of regional and global liver function are critical in guiding treatments for intrahepatic cancers. Regional and global liver function assessments important for defining the magnitude and spatial distribution of radiation dose to preserve functional liver parenchyma and reduce incidence of hepatotoxicity from radiation therapy (RT) for intrahepatic cancer treatment. This individualized liver function-guided RT strategy is critical for patients with heterogeneous and poor liver function, often observed in cirrhotic patients treated for HCC. Dynamic gadoxetic-acid enhanced (DGAE) magnetic resonance imaging (MRI) allows investigation of liver function through observation of the uptake of contrast agent into the hepatocytes. This work seeks to determine if gadoxetic uptake rate can be used as a reliable measure of liver function, and to develop robust methods for uptake estimation with an interest in the therapeutic application of this knowledge in the case of intrahepatic cancers. Since voxel-by voxel fitting of the preexisting nonlinear dual-input two-compartment model is highly susceptible to over fitting, and highly dependent on data that is both temporally very well characterized and low in noise, this work proposes and validates a new model for quantifying the voxel-wise uptake rate of gadoxetic acid as a measure of regional liver function. A linearized single-input two-compartment (LSITC) model is a linearization of the pre-existing dual-input model but is designed to perform uptake quantification in a more robust, computationally simpler, and much faster manner. The method is validated against the preexisting dual-input model for both real and simulated data. Simulations are used to investigate the effects of noise as well as issues related to the sampling of the arterial peak in the characteristic input functions of DGAE MRI. Further validation explores the relationship between gadoxetic acid uptake rate and two well established global measures of liver function, namely: Indocyanine Green retention (ICGR) and Albumin-Bilirubin (ALBI) score. This work also establishes the relationships between these scores and imaging derived measures of whole liver function using uptake rate. Additionally, the same comparisons are performed for portal venous perfusion, a pharmacokinetic parameter that has been observed to correlate with function in patients with relatively good liver function, and has been used as a guide for individualized liver function-guided RT. For the patients assessed, gadoxetic acid uptake rate performs significantly better as a predictor of whole liver function than portal venous perfusion. This work also investigates the possible gains that could be introduced through use of gadoxetic uptake rate maps in the creation of function-guided RT plans. To this end, plans were created using both perfusion and uptake, and both were compared to plans that did not use functional guidance. While the plans were generally broadly similar, significant differences were observed in patients with severely compromised uptake that did not correspond with compromised perfusion. This dissertation also deals with the problem of quantifying uptake rate in suboptimal very temporally sparse or short DGAE MRI acquisitions. In addition to testing the limits of the LSITC model for these limited datasets (both realistic and extreme), a neural network-based approach to quantification of uptake rate is developed, allowing for increased robustness over current models.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163264/1/jjsimeth_1.pd

A Non-Rigid Registration Method for Analyzing Myocardial Wall Motion for Cardiac CT Images

Cardiac resynchronization therapy (CRT) has a high percentage of non-responders. Successfully locating the optimal location for CRT lead placement on a priori images can increase efficiency in procedural preparation and execution and could potentially increase the rate of CRT responders. Registration has been used in the past to assess the motion of medical images. Specifically, one method of non-rigid registration has been utilized to assess the motion of left ventricular MR cardiac images. As CT imaging is often performed as part of resynchronization treatment planning and is a fast and accessible means of imaging, extending this registration method to assessing left ventricular motion of CT images could provide another means of reproducible contractility assessment. This thesis investigates the use of non-rigid registration to evaluate the myocardium motion in multi-phase multi-slice computed tomography (MSCT) cardiac imaging for the evaluation of mechanical contraction of the left ventricle

Effects of rigid and non-rigid image registration on test-retest variability of quantitative [18F]FDG PET/CT studies

ABSTRACT: BACKGROUND: [18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET) is a valuable tool for monitoring response to therapy in oncology. In longitudinal studies, however, patients are not scanned in exactly the same position. Rigid and non-rigid image registration can be applied in order to reuse baseline volumes of interest (VOI) on consecutive studies of the same patient. The purpose of this study was to investigate the impact of various image registration strategies on standardized uptake value (SUV) and metabolic volume test-retest variability (TRT). METHODS: Test-retest whole-body [18F]FDG PET/CT scans were collected retrospectively for 11 subjects with advanced gastrointestinal malignancies (colorectal carcinoma). Rigid and non-rigid image registration techniques with various degrees of locality were applied to PET, CT, and non-attenuation corrected PET (NAC) data. VOI were drawn independently on both test and retest scans. VOI drawn on test scans were projected onto retest scans and the overlap between projected VOI and manually drawn retest VOI was quantified using the Dice similarity coefficient (DSC). In addition, absolute (unsigned) differences in TRT of SUVmax, SUVmean, metabolic volume and total lesion glycolysis (TLG) were calculated in on one hand the test VOI and on the other hand the retest VOI and projected VOI. Reference values were obtained by delineating VOIs on both scans separately. RESULTS: Non-rigid PET registration showed the best performance (median DSC: 0.82, other methods: 0.71-0.81). Compared with the reference, none of the registration types showed significant absolute differences in TRT of SUVmax, SUVmean and TLG (p > 0.05). Only for absolute TRT of metabolic volume, significant lower values (p < 0.05) were observed for all registration strategies when compared to delineating VOIs separately, except for non-rigid PET registrations (p = 0.1). Non-rigid PET registration provided good volume TRT (7.7%) that was smaller than the reference (16%). CONCLUSION: In particular, non-rigid PET image registration showed good performance similar to delineating VOI on both scans separately, and with smaller TRT in metabolic volume estimates.van Velden F.H.P., van Beers P., Nuyts J., Velasquez L.M., Hayes W., Lammertsma A.A., Boellaard R., Loeckx D., ''Effects of rigid and non-rigid image registration on test-retest variability of quantitative [18F]FDG PET/CT studies'', EJNMMI research, vol. 2, no. 10, 2012.status: publishe

Correction of spatial distortion in magnetic resonance imaging

Dissertation to Obtain the Degree of Master in Biomedical EngineeringMagnetic Resonance Imaging (MRI) has been a major investigation and research focus among scientific and medical communities. So, new hardware with superior magnetic fields and faster sequences has been developed. However, these improvements result in intensity and spatial distortions, particularly in fast sequences, as Echo Plana Imaging (EPI), used in functional and diffusion-weighed MRI (fMRI and DW-MRI). Therefore, correction of spatial distortion is useful to obtain a higher quality in this kind of images. This project contains two major parts. The first part consists in simulating MRI data required for assessing the performance of Registration methods and optimizing parameters. To assess the methods five evaluation metrics were calculated between the corrected data and an undistorted EPI, namely: Root Mean Square (RMS); Normalized Mutual Information (NMI), Squared Correlation Coefficient(SCC); Euclidean Distance of Centres of Mass (CM) and Dice Coefficient of segmented images. In brief, this part validates the applied Registration correction method. The project’s second part includes correction of real images, obtained at a Clinical Partner. Real images are diffusion weighted MRI data with different b-values (gradient strength coefficient), allowing performance assessment of different methods on images with increasing b-values and decreasing SNR. The methods tested on real data were Registration, Field Map correction and a new proposed pipeline, which consists in performing a Field Map correction after a registration process. To assess the accuracy of these methods on real data, we used the same evaluation metrics, as for simulated data, except RMS and Dice Coefficient. At the end, it was concluded that Registration-based methods are better than Field Map, and that the new proposed pipeline produces some improvements in the registration. Regarding the influence of b-value on the correction, it is important to say that the methods performed using images with higher b’s showed more improvements in regarding metric values, but the behaviour is similar for all b-values