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Chen Z.,The Mind Research Network and LBERI | Calhoun V.D.,University of New Mexico
Reports in Medical Imaging | Year: 2014

The underlying source of brain imaging by T2&z.ast;-weighted magnetic resonance imaging (T2&z.ast;MRI) is mainly due to the intracranial inhomogeneous magnetic susceptibility distribution (denoted by χ). We can reconstruct the source χ by two computational steps: first, calculate a fieldmap from a T2&z.ast; phase image and then second, calculate a χ map from the fieldmap. The internal χ distribution reconstruction from observed T2&z.ast; phase images is termed χtomography, which connotes the digital source reproduction with spatial conformance by solving inverse problems in the context of medical imaging. In the small phase angle regime, the T2&z.ast; phase image remains unwrapped (&π,phase angle,π) and it is linearly related to the fieldmap by a scaling factor. However, the second inverse step (calculating a χ map from a fieldmap) is a severely ill-posed 3D deconvolution problem due to an unusual bipolar-valued kernel (dipole field kernel). We have reported on a 3-subproblem split Bregman iteration algorithm for total variation-regularized 3D χ reconstruction; in this paper, we report on a 2-subproblem split Bregman iteration algorithm with easy implementation. We validate the 3D χ tomography algorithms by numerical simulations and phantom experiments. We also demonstrate the feasibility of 3D χ tomography for obtaining in vivo brain χ states at 2 mm spatial resolution. © 2014 Chen and Calhoun. This work is published by Dove Medical Press Limited. Source


Lin D.,Tulane University | Cao H.,U.S. National Institutes of Health | Calhoun V.D.,The Mind Research Network and LBERI | Calhoun V.D.,University of New Mexico | Wang Y.-P.,Tulane University
Journal of Neuroscience Methods | Year: 2014

The development of advanced medical imaging technologies and high-throughput genomic measurements has enhanced our ability to understand their interplay as well as their relationship with human behavior by integrating these two types of datasets. However, the high dimensionality and heterogeneity of these datasets presents a challenge to conventional statistical methods; there is a high demand for the development of both correlative and integrative analysis approaches. Here, we review our recent work on developing sparse representation based approaches to address this challenge. We show how sparse models are applied to the correlation and integration of imaging and genetic data for biomarker identification. We present examples on how these approaches are used for the detection of risk genes and classification of complex diseases such as schizophrenia. Finally, we discuss future directions on the integration of multiple imaging and genomic datasets including their interactions such as epistasis. © 2014 Elsevier B.V. Source


Calhoun V.D.,The Mind Research Network and LBERI | Calhoun V.D.,University of New Mexico
GigaScience | Year: 2015

Efforts to expand sharing of neuroimaging data have been growing exponentially in recent years. There are several different types of data sharing which can be considered to fall along a spectrum, ranging from simpler and less informative to more complex and more informative. In this paper we consider this spectrum for three domains: data capture, data density, and data analysis. Here the focus is on the right end of the spectrum, that is, how to maximize the information content while addressing the challenges. A summary of associated challenges of and possible solutions is presented in this review and includes: 1) a discussion of tools to monitor quality of data as it is collected and encourage adoption of data mapping standards; 2) sharing of time-series data (not just summary maps or regions); and 3) the use of analytic approaches which maximize sharing potential as much as possible. Examples of existing solutions for each of these points, which we developed in our lab, are also discussed including the use of a comprehensive beginning-to-end neuroinformatics platform and the use of flexible analytic approaches, such as independent component analysis and multivariate classification approaches, such as deep learning. © 2015 Calhoun. Source


Chen Z.,The Mind Research Network and LBERI | Calhoun V.,The Mind Research Network and LBERI | Calhoun V.,University of New Mexico
Magnetic Resonance Imaging | Year: 2015

The underlying source of brain imaging by T2*-weighted magnetic resonance imaging (T2*MRI) is the intracranial inhomogeneous tissue magnetic susceptibility (denoted by χ) that causes an inhomogeneous field map (via magnetization) in a main field. By decomposing T2 *MRI into two steps, we understand that the 1st step from a χ source to a field map is a linear but non-isomorphic spatial mapping, and the 2nd step from the field map to a T2*image is a nonlinear mapping due to the trigonometric behavior of spin precession signals. The magnitude and phase calculations from a complex T2*image introduce additional nonlinearities. In this report, we look into the magnitude and phase behaviors of a T2* image (signal) by theoretical approximation and Monte Carlo simulation. We perform the 1st-order Taylor expansion on intravoxel dephasing formula of a T2*signal and show that the T2*magnitude is a quadratic mapping of the field map and T2*phase is a linear isomorphic mapping. By Monte Carlo simulation of T2*MRI for a span of echo times (with B0=3T and TE=[0,120] ms), we first confirm the quadratic magnitude and linear phase behaviors in small phase angle regime (via TE <30ms), and then provide more general magnitude and phase nonlinear behaviors in large phase angle scenarios (via TE >30ms). By solving the inverse problem of T2 MRI, we demonstrate χ tomography and conclude that the χ source can be reliably reconstructed from a T2*phase image in a small phase angle regime. © 2015 Elsevier Inc. Source


Wood D.,The Mind Research Network and LBERI | King M.,The Mind Research Network and LBERI | Landis D.,The Mind Research Network and LBERI | Courtney W.,The Mind Research Network and LBERI | And 6 more authors.
Frontiers in Neuroinformatics | Year: 2014

Neuroscientists increasingly need to work with big data in order to derive meaningful results in their field. Collecting, organizing and analyzing this data can be a major hurdle on the road to scientific discovery. This hurdle can be lowered using the same technologies that are currently revolutionizing the way that cultural and social media sites represent and share information with their users. Web application technologies and standards such as RESTful webservices, HTML5 and high-performance in-browser JavaScript engines are being utilized to vastly improve the way that the world accesses and shares information. The neuroscience community can also benefit tremendously from these technologies. We present here a web application that allows users to explore and request the complex datasets that need to be shared among the neuroimaging community. The COINS (Collaborative Informatics and Neuroimaging Suite) Data Exchange uses web application technologies to facilitate data sharing in three phases: Exploration, Request/Communication, and Download. This paper will focus on the first phase, and how intuitive exploration of large and complex datasets is achieved using a framework that centers around asynchronous client-server communication (AJAX) and also exposes a powerful API that can be utilized by other applications to explore available data. First opened to the neuroscience community in August 2012, the Data Exchange has already provided researchers with over 2500 GB of data. © 2014 Wood, King, Landis, Courtney, Wang, Kelly, Turner and Calhoun. Source

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