Multi-parametric neuroimaging reproducibility: a 3-T resource study.

Modern MRI image processing methods have yielded quantitative, morphometric, functional, and structural assessments of the human brain. These analyses typically exploit carefully optimized protocols for specific imaging targets. Algorithm investigators have several excellent public data resources to use to test, develop, and optimize their methods.

Simultaneous Truth and Performance Level Estimation with Incomplete, Over-complete, and Ancillary Data.

Image labeling and parcellation are critical tasks for the assessment of volumetric and morphometric features in medical imaging data. The process of image labeling is inherently error prone as images are corrupted by noise and artifact. Even expert interpretations are subject to subjectivity and the precision of the individual raters.

q-space and conventional diffusion imaging of axon and myelin damage in the rat spinal cord after axotomy.

Parallel and perpendicular diffusion properties of water in the rat spinal cord were investigated 3 and 30 days after dorsal root axotomy, a specific insult resulting in early axonal degeneration followed by later myelin damage in the dorsal column white matter. Results from q-space analysis (i.e.

Resolution of Crossing Fibers with Constrained Compressed Sensing using Traditional Diffusion Tensor MRI.

Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture and brain connectivity. Yet, DTI suffers serious limitations in regions of crossing fibers because traditional tensor techniques cannot represent multiple, independent intra-voxel orientations. Compressed sensing has been proposed to resolve crossing fibers using a tensor mixture model (e.

The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software.

Non-invasive neuroimaging techniques enable extraordinarily sensitive and specific in vivo study of the structure, functional response and connectivity of biological mechanisms. With these advanced methods comes a heavy reliance on computer-based processing, analysis and interpretation. While the neuroimaging community has produced many excellent academic and commercial tool packages, new tools are often required to interpret new modalities and paradigms.

Reproducibility of tract-specific magnetization transfer and diffusion tensor imaging in the cervical spinal cord at 3 tesla.

Damage to specific white matter tracts within the spinal cord can often result in the particular neurological syndromes that characterize myelopathies such as traumatic spinal cord injury. Noninvasive visualization of these tracts with imaging techniques that are sensitive to microstructural integrity is an important clinical goal. Diffusion tensor imaging (DTI)- and magnetization transfer (MT)-derived quantities have shown promise in assessing tissue health in the central nervous system.

Complex geometric models of diffusion and relaxation in healthy and damaged white matter.

Which aspects of tissue microstructure affect diffusion weighted MRI signals? Prior models, many of which use Monte-Carlo simulations, have focused on relatively simple models of the cellular microenvironment and have not considered important anatomic details. With the advent of higher-order analysis models for diffusion imaging, such as high angular resolution diffusion imaging (HARDI), more realistic models are necessary. This paper presents and evaluates the reproducibility of simulations of diffusion in complex geometries.

Orthogonal diffusion-weighted MRI measures distinguish region-specific degeneration in cerebellar ataxia subtypes.

The cerebellar peduncles are excellent candidates for composite indicators of regional degeneration in posterior fossa structures, as the peduncles show histopathological changes in degenerative ataxia. We postulate that magnetic resonance imaging will reveal evidence of disease specific peduncle degeneration through macrostructural (cross-sectional area) and microstructural (fractional anisotropy, mean diffusivity) measures. This study presents a "proof of principle" using orthogonal diffusion tensor imaging cross-sections of the cerebellar peduncles to distinguish categories of cerebellar disease.

Robust estimation of spatially variable noise fields.

Consideration of spatially variable noise fields is becoming increasingly necessary in MRI given recent innovations in artifact identification and statistically driven image processing. Fast imaging methods enable study of difficult anatomical targets and improve image quality but also increase the spatial variability in the noise field. Traditional analysis techniques have either assumed that the noise is constant across the field of view (or region of interest) or have relied on separate MRI acquisitions to measure the noise field.

Water saturation shift referencing (WASSR) for chemical exchange saturation transfer (CEST) experiments.

Chemical exchange saturation transfer (CEST) is a contrast mechanism that exploits exchange-based magnetization transfer (MT) between solute and water protons. CEST effects compete with direct water saturation and conventional MT processes, and generally can only be quantified through an asymmetry analysis of the water saturation spectrum (Z-spectrum) with respect to the water frequency, a process that is exquisitely sensitive to magnetic field inhomogeneities. Here it is shown that direct water saturation imaging allows measurement of the absolute water frequency in each voxel, allowing proper centering of Z-spectra on a voxel-by-voxel basis independently of spatial B(0) field variations.

Estimation and application of spatially variable noise fields in diffusion tensor imaging.

Optimal interpretation of magnetic resonance image content often requires an estimate of the underlying image noise, which is typically realized as a spatially invariant estimate of the noise distribution. This is not an ideal practice in diffusion tensor imaging because the noise distribution is usually spatially varying due to the use of fast imaging and noise suppression techniques. A new estimation approach for spatially varying noise fields (NFs) is proposed in this article.

Effect of oral appliances on genioglossus muscle tonicity seen with diffusion tensor imaging: a pilot study.

Objective: The purpose of this study was to examine whether the diffusion tensor imaging (DTI) technique can be used as a modality to represent the structural deformation in the in vivo genioglossus (GG) muscle fibers with oral appliances (OAs).

Study Design: Three healthy subjects were recruited for the pilot study. A custom-made OA, which is modified from a tongue retaining device (TRD), was constructed for each subject before the DTI acquisitions.

Diffusion tensor imaging at low SNR: nonmonotonic behaviors of tensor contrasts.

Diffusion tensor imaging (DTI) provides measurements of directional diffusivities and has been widely used to characterize changes in the tissue microarchitecture of the brain. DTI is gaining prominence in applications outside of the brain, where resolution, motion and short T2 values often limit the achievable signal-to-noise ratio (SNR). Consequently, it is important to revisit the topic of tensor estimation in low-SNR regimes.

AUTOMATICALLY IDENTIFYING WHITE MATTER TRACTS USING CORTICAL LABELS.

Diffusion tensor imaging (DTI) has become a standard clinical procedure in assessing the health of white matter in the brain. Tractography, the tracing of individual fibers in the brain using DTI data, has begun to play a more central role in neuroscience research, particularly in understanding the relationships between brain connectivity and behavior. The measuring of features related to bundles of fibers, i.

Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T.

Purpose: To develop an experimental protocol to calculate the precision and accuracy of fractional anisotropy (FA), mean diffusivity (MD), and the orientation of the principal eigenvector (PEV) as a function of the signal-to-noise ratio (SNR) in vivo.

Materials And Methods: A healthy male volunteer was scanned in three separate scanning sessions, yielding a total of 45 diffusion tensor imaging (DTI) scans. To provide FA, MD, and PEV as a function of SNR, sequential scans from a scan session were grouped into nonintersecting sets.

Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T.

Diffusion tensor imaging (DTI) is used to study tissue composition and architecture in vivo. To increase the signal to noise ratio (SNR) of DTI contrasts, studies typically use more than the minimum of 6 diffusion weighting (DW) directions or acquire repeated observations of the same set of DW directions. Simulation-based studies have sought to optimize DTI acquisitions and suggest that increasing the directional resolution of a DTI dataset (i.

Diffusion Tensor Estimation by Maximizing Rician Likelihood.

Diffusion tensor imaging (DTI) is widely used to characterize white matter in health and disease. Previous approaches to the estimation of diffusion tensors have either been statistically suboptimal or have used Gaussian approximations of the underlying noise structure, which is Rician in reality. This can cause quantities derived from these tensors - e.

Brain atrophy in long-term abstinent alcoholics who demonstrate impairment on a simulated gambling task.

We recently demonstrated impairment on the Simulated Gambling Task (SGT) in long-term abstinent alcoholics (AbsAlc). Brain regions that have been shown to be necessary for intact SGT performance are the ventromedial prefrontal cortex (VMPFC) and the amygdala; patients with VMPFC or amygdalar damage demonstrate SGT impairments similar to those of substance abusing populations. We examined these brain regions, using T1-weighted MRIs, in the 101 participants from our previous study using voxel-based morphometry (VBM).

Statistical parametric mapping of brain morphology: sensitivity is dramatically increased by using brain-extracted images as inputs.

A major attraction of voxel-based morphometry (VBM) is that it allows researchers to explore large datasets with minimal human intervention. However, the validity and sensitivity of the Statistical Parametric Mapping (SPM2) approach to VBM are the subject of considerable debate. We visually inspected the SPM2 gray matter segmentations for 101 research participants and found a gross inclusion of non-brain tissue surrounding the entire brain as gray matter in five subjects and focal areas bordering the brain in which non-brain tissue was classified as gray matter in many other subjects.

Treated and treatment-naive alcoholics come from different populations.

In most research on alcoholism, convenience samples of individuals who have been in some type of treatment are used. Berkson's fallacy results when the associations found in studies of select samples are incorrectly presumed to apply to all alcoholics (i.e.

Treated and treatment-naive alcoholics come from different populations.

In most research on alcoholism, convenience samples of individuals who have been in some type of treatment are used. Berkson's fallacy results when the associations found in studies of select samples are incorrectly presumed to apply to all alcoholics (i.e.

Controlling for premorbid brain size in imaging studies: T1-derived cranium scaling factor vs. T2-derived intracranial vault volume.

Intracranial vault (ICV) volume, obtained from T2-weighted magnetic resonance imaging (MRI), is generally used to estimate premorbid brain size in imaging studies. T1-weighted sequences lack the signal characteristics for ICV measurements [they have poor contrast at the outer boundary of sulcal cranium scaling factor (CSF)] but are valuable in imaging studies due to their excellent gray vs. white matter contrast.