Multiresolution spatiotemporal mechanical model of the heart as a prior to constrain the solution for 4D models of the heart.

In several nuclear cardiac imaging applications (SPECT and PET), images are formed by reconstructing tomographic data using an iterative reconstruction algorithm with corrections for physical factors involved in the imaging detection process and with corrections for cardiac and respiratory motion. The physical factors are modeled as coefficients in the matrix of a system of linear equations and include attenuation, scatter, and spatially varying geometric response. The solution to the tomographic problem involves solving the inverse of this system matrix. This requires the design of an iterative reconstruction algorithm with a statistical model that best fits the data acquisition. The most appropriate model is based on a Poisson distribution. Using Bayes Theorem, an iterative reconstruction algorithm is designed to determine the maximum a posteriori estimate of the reconstructed image with constraints that maximizes the Bayesian likelihood function for the Poisson statistical model. The a priori distribution is formulated as the joint entropy (JE) to measure the similarity between the gated cardiac PET image and the cardiac MRI cine image modeled as a FE mechanical model. The developed algorithm shows the potential of using a FE mechanical model of the heart derived from a cardiac MRI cine scan to constrain solutions of gated cardiac PET images

Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation

Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and generative adversarial networks (GAN), GIN discovers the pathophysiologic meaning of the feature space. Moreover, a test of predicting the surgical outcome directly using the selected features results in a high accuracy of 81.55%, which suggests little pathophysiologic information has been lost while conducting the feature selection. This demonstrates GIN can generate virtual patients not only visually authentic but also pathophysiologically interpretable

Patient Specific Dosimetry Phantoms Using Multichannel LDDMM of the Whole Body

This paper describes an automated procedure for creating detailed patient-specific pediatric dosimetry phantoms from a small set of segmented organs in a child's CT scan. The algorithm involves full body mappings from adult template to pediatric images using multichannel large deformation diffeomorphic metric mapping (MC-LDDMM). The parallel implementation and performance of MC-LDDMM for this application is studied here for a sample of 4 pediatric patients, and from 1 to 24 processors. 93.84% of computation time is parallelized, and the efficiency of parallelization remains high until more than 8 processors are used. The performance of the algorithm was validated on a set of 24 male and 18 female pediatric patients. It was found to be accurate typically to within 1-2 voxels (2–4 mm) and robust across this large and variable data set

Impact of Poultry Mortality Pits on Farm Groundwater Quality

Proceedings of the 1999 Georgia Water Resources Conference, March 30 and 31, Athens, Georgia.Results of a 15-county survey revealed that intensive animal agriculture may impact shallow groundwater resources. Objectives of this study are to assess water quality on poultry farms and determine if there is a relationship between waste disposal practices and groundwater quality. Twenty poultry farms representing concentrated areas of commercial poultry production and four major soil provinces were evaluated using site assessments, questionnaires, electromagnetic (EM) survey readings, and chemical and microbiological analysis of domestic well water. Based upon the EM survey results, five farms were instrumented with lysimeters and test wells to determine possible nutrient and microbiological movement to groundwater. Site evaluations revealed that 10 of the 47 (21 %) domestic wells did not have appropriate well head protection to prevent surface water contamination. Five of the 47 (11 %) wells were located downslope and/or within 100 ft. of a nitrogen source other than pits and averaged nitrate-N (N03-N) levels above background (3 ppm). Thirty-eight percent had elevated coliform levels and 10.6% contained Salmonella in at least one sample during the sampling period. EM surveys and monitoring data indicated that nutrients migrate less than 100 ft. laterally down gradient from the pits. Poultry mortality pits on the 20 farms did not appear to elevate nitrate levels above background. Groundwater nitrate-N levels were higher on farms containing uncovered litter stacks. Preliminary results indicate that uncovered litter stacks may have a greater impact on groundwater quality than poultry mortality pits. Additional testing on various soil types is needed.Sponsored and Organized by: U.S. Geological Survey, Georgia Department of Natural Resources, The University of Georgia, Georgia State University, Georgia Institute of TechnologyThis book was published by the Institute of Ecology, The University of Georgia, Athens, Georgia 30602-2202 with partial funding provided by the U.S. Department of Interior, geological Survey, through the Georgia Water Research Insttitute as authorized by the Water Research Institutes Authorization Act of 1990 (P.L. 101-397). The views and statements advanced in this publication are solely those of the authors and do not represent official views or policies of the University of Georgia or the U.S. Geological Survey or the conference sponsors

XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms

Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with new anatomical variations remains unexplored. We propose a novel method for synthesizing cardiac magnetic resonance (CMR) images on a population of virtual subjects with a large anatomical variation, introduced using the 4D eXtended Cardiac and Torso (XCAT) computerized human phantom. We investigate two conditional image synthesis approaches grounded on a semantically-consistent mask-guided image generation technique: 4-class and 8-class XCAT-GANs. The 4-class technique relies on only the annotations of the heart; while the 8-class technique employs a predicted multi-tissue label map of the heart-surrounding organs and provides better guidance for our conditional image synthesis. For both techniques, we train our conditional XCAT-GAN with real images paired with corresponding labels and subsequently at the inference time, we substitute the labels with the XCAT derived ones. Therefore, the trained network accurately transfers the tissue-specific textures to the new label maps. By creating 33 virtual subjects of synthetic CMR images at the end-diastolic and end-systolic phases, we evaluate the usefulness of such data in the downstream cardiac cavity segmentation task under different augmentation strategies. Results demonstrate that even with only 20% of real images (40 volumes) seen during training, segmentation performance is retained with the addition of synthetic CMR images. Moreover, the improvement in utilizing synthetic images for augmenting the real data is evident through the reduction of Hausdorff distance up to 28% and an increase in the Dice score up to 5%, indicating a higher similarity to the ground truth in all dimensions.Comment: Accepted for MICCAI 202

Catch rates and demographics of loggerhead sea turtles (Caretta caretta) captured from the Charleston, South Carolina, shipping channel during the period of mandatory use of turtle excluder devices (TEDs)

Trawling was conducted in the Charleston, South Carolina, shipping channel between May and August during 2004–07 to evaluate loggerhead sea turtle (Caretta caretta) catch rates and demographic distributions. Two hundred and twenty individual loggerheads were captured in 432 trawling events during eight sampling periods lasting 2–10 days each. Catch was analyzed by using a generalized linear model. Data were fitted to a negative binomial distribution with the log of standardized sampling effort (i.e., an hour of sampling with a net head rope length standardized to 30.5 m) for each event treated as an offset term. Among 21 variables, factors, and interactions, five terms were significant in the final model, which accounted for 45% of model deviance. Highly significant differences in catch were noted among sampling periods and sampling locations within the channel, with greatest catch furthest seaward consistent with historical observations. Loggerhead sea turtle catch rates in 2004–07 were greater than in 1991–92 when mandatory use of turtle excluder devices was beginning to be phased in. Concurrent with increased catch rates, loggerheads captured in 2004–07 were larger than in 1991–92. Eighty-five percent of loggerheads captured were ≤75.0 cm straight-line carapace length (nuchal notch to tip of carapace) and there was a 3.9:1 female-to-male bias, consistent with limited data for this location two decades earlier. Only juvenile loggerheads ≤75.0 cm possessed haplotypes other than CC-A01 or CC-A02 that dominate in the region. Six rare and one un-described haplotype were predominantly found in June 2004

Advances in estrogen receptor biology: prospects for improvements in targeted breast cancer therapy

Estrogen receptor (ER) has a crucial role in normal breast development and is expressed in the most common breast cancer subtypes. Importantly, its expression is very highly predictive for response to endocrine therapy. Current endocrine therapies for ER-positive breast cancers target ER function at multiple levels. These include targeting the level of estrogen, blocking estrogen action at the ER, and decreasing ER levels. However, the ultimate effectiveness of therapy is limited by either intrinsic or acquired resistance. Identifying the factors and pathways responsible for sensitivity and resistance remains a challenge in improving the treatment of breast cancer. With a better understanding of coordinated action of ER, its coregulatory factors, and the influence of other intracellular signaling cascades, improvements in breast cancer therapy are emerging