Three-dimensional reduced field-of-view imaging (3D-rFOVI).

PURPOSE: This study aimed at developing a 3D reduced field-of-view imaging (3D-rFOVI) technique using a 2D radiofrequency (RF) pulse, and demonstrating its ability to achieve isotropic high spatial resolution and reduced image distortion in echo planar imaging (EPI). METHODS: The proposed 3D-rFOVI technique takes advantage of a 2D RF pulse to excite a slab along the conventional slice-selection direction (i.e., z-direction) while limiting the spatial extent along the phase-encoded direction (i.e., y-direction) within the slab. The slab is phase-encoded in both through-slab and in-slab phase-encoded directions. The 3D-rFOVI technique was implemented at 3T in gradient-echo and spin-echo EPI pulse sequences for functional MRI (fMRI) and diffusion-weighted imaging (DWI), respectively. 3D-rFOVI experiments were performed on a phantom and human brain to illustrate image distortion reduction, as well as isotropic high spatial resolution, in comparison with 3D full-FOV imaging. RESULTS: In both the phantom and the human brain, image voxel dislocation was substantially reduced by 3D-rFOVI when compared with full-FOV imaging. In the fMRI experiment with visual stimulation, 3D isotropic spatial resolution of (2 × 2 × 2 mm3 ) was achieved with an adequate signal-to-noise ratio (81.5) and blood oxygen level-dependent (BOLD) contrast (2.5%). In the DWI experiment, diffusion-weighted brain images with an isotropic resolution of (1 × 1 × 1 mm3 ) was obtained without appreciable image distortion. CONCLUSION: This study indicates that 3D-rFOVI is a viable approach to 3D neuroimaging over a zoomed region.

Neutrophil extracellular traps (NETs) contribute to pathological changes of ocular graft-vs.-host disease (oGVHD) dry eye: Implications for novel biomarkers and therapeutic strategies.

PURPOSE: To investigate the role of neutrophil extracellular traps (NETs) and NET-associated proteins in the pathogenesis of oGVHD and whether dismantling of NETs with heparin reduces those changes. METHODS: Ocular surface washings from oGVHD patients and healthy subjects were analyzed. Isolated peripheral blood human neutrophils were stimulated to generate NETs and heparinized NETs. We performed in vitro experiments using cell lines (corneal epithelial, conjunctival fibroblast, meibomian gland (MG) epithelial and T cells), and in vivo experiments using murine models, and compared the effects of NETs, heparinized NETs, NET-associated proteins and neutralizing antibodies to NET-associated proteins. RESULTS: Neutrophils, exfoliated epithelial cells, NETs and NET-associated proteins (extracellular DNA, Neutrophil Elastase, Myeloperoxidase, Oncostatin M (OSM), Neutrophil gelatinase-associated lipocalin (NGAL) and LIGHT/TNFSF14) are present in ocular surface washings (OSW) and mucocellular aggregates (MCA). Eyes with high number of neutrophils in OSW have more severe signs and symptoms of oGVHD. NETs (and OSM) cause epitheliopathy in murine corneas. NETs (and LIGHT/TNFSF14) increase proliferation of T cells. NETs (and NGAL) inhibit proliferation and differentiation of MG epithelial cells. NETs enhance proliferation and myofibroblast transformation of conjunctival fibroblasts. Sub-anticoagulant dose Heparin (100 IU/mL) dismantles NETs and reduces epithelial, fibroblast, T cell and MG cell changes induced by NETs. CONCLUSION: NETs and NET-associated proteins contribute to the pathological changes of oGVHD (corneal epitheliopathy, conjunctival cicatrization, ocular surface inflammation and meibomian gland disease). Our data points to the potential of NET-associated proteins (OSM or LIGHT/TNFSF14) to serve as biomarkers and NET-dismantling biologics (heparin eye drops) as treatment for oGVHD.

Detection of axonal degeneration in a mouse model of Huntington's disease: comparison between diffusion tensor imaging and anomalous diffusion metrics.

OBJECTIVE: The goal of this work is to study the changes in white matter integrity in R6/2, a well-established animal model of Huntington's disease (HD) that are captured by ex vivo diffusion imaging (DTI) using a high field MRI (17.6 T). MATERIALS AND METHODS: DTI and continuous time random walk (CTRW) models were used to fit changes in the diffusion-weighted signal intensity in the corpus callosum of controls and in R6/2 mice. RESULTS: A significant 13% decrease in fractional anisotropy, a 7% increase in axial diffusion, and a 33% increase in radial diffusion were observed between R6/2 and control mice. No change was observed in the CTRW beta parameter, but a significant decrease in the alpha parameter (- 21%) was measured. Histological analysis of the corpus callosum showed a decrease in axonal organization, myelin alterations, and astrogliosis. Electron microscopy studies demonstrated ultrastructural changes in degenerating axons, such as an increase in tortuosity in the R6/2 mice. CONCLUSIONS: DTI and CTRW diffusion models display quantitative changes associated with the microstructural alterations observed in the corpus callosum of the R6/2 mice. The observed increase in the diffusivity and decrease in the alpha CTRW parameter providing support for the use of these diffusion models for non-invasive detection of white matter alterations in HD.

Incorporating relaxivities to more accurately reconstruct MR images.

PURPOSE: To develop a mathematical model that incorporates the magnetic resonance relaxivities into the image reconstruction process in a single step. MATERIALS AND METHODS: In magnetic resonance imaging, the complex-valued measurements of the acquired signal at each point in frequency space are expressed as a Fourier transformation of the proton spin density weighted by Fourier encoding anomalies: T2(⁎), T1, and a phase determined by magnetic field inhomogeneity (∆B) according to the MR signal equation. Such anomalies alter the expected symmetry and the signal strength of the k-space observations, resulting in images distorted by image warping, blurring, and loss in image intensity. Although T1 on tissue relaxation time provides valuable quantitative information on tissue characteristics, the T1 recovery term is typically neglected by assuming a long repetition time. In this study, the linear framework presented in the work of Rowe et al., 2007, and of Nencka et al., 2009 is extended to develop a Fourier reconstruction operation in terms of a real-valued isomorphism that incorporates the effects of T2(⁎), ∆B, and T1. This framework provides a way to precisely quantify the statistical properties of the corrected image-space data by offering a linear relationship between the observed frequency space measurements and reconstructed corrected image-space measurements. The model is illustrated both on theoretical data generated by considering T2(⁎), T1, and/or ∆B effects, and on experimentally acquired fMRI data by focusing on the incorporation of T1. A comparison is also made between the activation statistics computed from the reconstructed data with and without the incorporation of T1 effects. RESULT: Accounting for T1 effects in image reconstruction is shown to recover image contrast that exists prior to T1 equilibrium. The incorporation of T1 is also shown to induce negligible correlation in reconstructed images and preserve functional activations. CONCLUSION: With the use of the proposed method, the effects of T2(⁎) and ∆B can be corrected, and T1 can be incorporated into the time series image-space data during image reconstruction in a single step. Incorporation of T1 provides improved tissue segmentation over the course of time series and therefore can improve the precision of motion correction and image registration.

Quantification of the statistical effects of spatiotemporal processing of nontask FMRI data.

Nontask functional magnetic resonance imaging (fMRI) has become one of the most popular noninvasive areas of brain mapping research for neuroscientists. In nontask fMRI, various sources of "noise" corrupt the measured blood oxygenation level-dependent signal. Many studies have aimed to attenuate the noise in reconstructed voxel measurements through spatial and temporal processing operations. While these solutions make the data more "appealing," many commonly used processing operations induce artificial correlations in the acquired data. As such, it becomes increasingly more difficult to derive the true underlying covariance structure once the data have been processed. As the goal of nontask fMRI studies is to determine, utilize, and analyze the true covariance structure of acquired data, such processing can lead to inaccurate and misleading conclusions drawn from the data if they are unaccounted for in the final connectivity analysis. In this article, we develop a framework that represents the spatiotemporal processing and reconstruction operations as linear operators, providing a means of precisely quantifying the correlations induced or modified by such processing rather than by performing lengthy Monte Carlo simulations. A framework of this kind allows one to appropriately model the statistical properties of the processed data, optimize the data processing pipeline, characterize excessive processing, and draw more accurate functional connectivity conclusions.