TractEM: Evaluation of protocols for deterministic tractography white matter atlas.

Reproducible identification of white matter pathways across subjects is essential for the study of structural connectivity of the human brain. One of the key challenges is anatomical differences between subjects and human rater subjectivity in labeling. Labeling white matter regions of interest presents many challenges due to the need to integrate both local and global information. Clearly communicating the manual processes to capture this information is cumbersome, yet essential to lay a solid foundation for comprehensive atlases. Segmentation protocols must be designed so the interpretation of the requested tasks as well as locating structural landmarks is anatomically accurate, intuitive and reproducible. In this work, we quantified the reproducibility of a first iteration of an open/public multi-bundle segmentation protocol. This allowed us to establish a baseline for its reproducibility as well as to identify the limitations for future iterations. The protocol was tested/evaluated on both typical 3 T research acquisition Baltimore Longitudinal Study of Aging (BLSA) and high-acquisition quality Human Connectome Project (HCP) datasets. The results show that a rudimentary protocol can produce acceptable intra-rater and inter-rater reproducibility. However, this work highlights the difficulty in generalizing reproducible results and the importance of reaching consensus on anatomical description of white matter pathways. The protocol has been made available in open source to improve generalizability and reliability in collaboration. The goal is to improve upon the first iteration and initiate a discussion on the anatomical validity (or lack thereof) of some bundle definitions and the importance of reproducibility of tractography segmentation.

Towards Portable Large-Scale Image Processing with High-Performance Computing.

High-throughput, large-scale medical image computing demands tight integration of high-performance computing (HPC) infrastructure for data storage, job distribution, and image processing. The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has constructed a large-scale image storage and processing infrastructure that is composed of (1) a large-scale image database using the eXtensible Neuroimaging Archive Toolkit (XNAT), (2) a content-aware job scheduling platform using the Distributed Automation for XNAT pipeline automation tool (DAX), and (3) a wide variety of encapsulated image processing pipelines called "spiders." The VUIIS CCI medical image data storage and processing infrastructure have housed and processed nearly half-million medical image volumes with Vanderbilt Advanced Computing Center for Research and Education (ACCRE), which is the HPC facility at the Vanderbilt University. The initial deployment was natively deployed (i.e., direct installations on a bare-metal server) within the ACCRE hardware and software environments, which lead to issues of portability and sustainability. First, it could be laborious to deploy the entire VUIIS CCI medical image data storage and processing infrastructure to another HPC center with varying hardware infrastructure, library availability, and software permission policies. Second, the spiders were not developed in an isolated manner, which has led to software dependency issues during system upgrades or remote software installation. To address such issues, herein, we describe recent innovations using containerization techniques with XNAT/DAX which are used to isolate the VUIIS CCI medical image data storage and processing infrastructure from the underlying hardware and software environments. The newly presented XNAT/DAX solution has the following new features: (1) multi-level portability from system level to the application level, (2) flexible and dynamic software development and expansion, and (3) scalable spider deployment compatible with HPC clusters and local workstations.

Magnetic Extraction of Acinetobacter baumannii Using Colistin-Functionalized γ-Fe2O3/Au Core/Shell Composite Nanoclusters.

Acinetobacter baumannii is a Gram-negative bacterium of increasing concern due to its virulence and persistence in combat and healthcare environments. The incidence of both community-acquired and nosocomial A. baumannii infections is on the rise in foreign and domestic healthcare facilities. Treatment options are limited due to the acquisition of multidrug resistance to the few effective antibiotics. Currently, the most effective pharmaceutically based treatment for multidrug-resistant A. baumannii infections is the antibiotic colistin (polymyxin E). To minimize side effects associated with administration of colistin or other toxic antimicrobial agents, we propose the development of a nanotechnology-mediated treatment strategy. In this design-based effort, colistin-functionalized multilayered, inorganic, magnetoplasmonic nanoconstructs were fabricated to bind to the surface of A. baumannii. This result, for the first time, demonstrates a robust, pharmaceutical-based motif for high affinity, composite nanoparticulates targeting the A. baumannii surface. The antibiotic-activated nanomaterials demonstrated cytocompatibility with human cells and no acute bacterial toxicity at nanoparticle to bacterial concentrations <10 000:1. The magnetomotive characteristics of the nanomaterial enabled magnetic extraction of the bacteria. In a macroscale environment, maximal separation efficiencies exceeding 38% were achieved. This result demonstrates the potential for implementation of this technology into micro- or mesofluidic-based separation environments to enhance extraction efficiencies. The future development of such a mesofluidic-based, nanotechnology-mediated platform is potentially suitable for adjuvant therapies to assist in the treatment of sepsis.