Evaluation of diffusion models of fiber tracts using diffusion tensor magnetic resonance imaging

Modeling of water diffusion in white matter is useful for revealing microstructure of the brain tissue and hence diagnosis and evaluation of white matter diseases. Researchers have modeled diffusion in white matter using mathematical and mechanical analysis at the cellular level. However, less work has been devoted to evaluate these models using macroscopic real data such as diffusion tensor magnetic resonance imaging (DTMRI) data. DTMRI is a noninvasive tool for evaluating white matter microstructure by measuring random motion of water molecules referred to as diffusion. It reflects directional information of microscopic structures such as fibers. Thus, it is applicable for evaluation and modification of mathematical models of white matter. Nevertheless, a realistic relation between a fiber model and imaging data does not exist. This work opens a promising avenue for relating DTMRI data to microstructural parameters of white matter. First, we propose a strategy for relating DTMRI and fiber model parameters to evaluate mathematical models in light of real data. The proposed strategy is then applied to evaluate and extend an existing model of white matter based on clinically available DTMRI data. Next, the proposed strategy is used to estimate microstructural characteristics of fiber tracts. We illustrate this approach through its application to approximation of myelin sheath thickness and fraction of volume occupied by fibers. Using sufficiently small imaging voxels, the proposed approach is capable of estimating model parameters with desirable precision.     

Fast functional brain signal changes detected by diffusion weighted fMRI

Functional magnetic resonance imaging (fMRI) has become the method of choice in the study of system neuroscience, as evidenced by an explosion of such literature in the past decade. Contrast mechanisms based on the blood oxygenation level, volume, and flow changes have been used to non-invasively detect brain activation secondary to the neuronal activity. However, because of the hemodynamic modulations inherent in these signals, their spatial and temporal characteristics are influenced by the complex geometry and varying delivery speed of the brain vasculature. Consequently, spatial dispersions and temporal delays are commonly seen in the brain activity using fMRI. It is thus of critical importance to investigate alternative contrast mechanisms that may offer shorter temporal delays and more direct spatial localization. In light of a recent phantom study which demonstrated the possibility to detect the destructive phase addition from the spatially incoherent, yet temporally synchronized, displacements caused by the Lorentz force experienced during electrical conduction within a strong magnetic field, we seek to apply similar imaging technique to investigate the functional signal changes that may provide alternative temporal and spatial characteristics. It is found that by using heavy diffusion weighting, which is one form of displacement encoding strategies, to remove the vascular signal and sensitize the minute and incoherent displacement, one can detect fast dynamic signal changes synchronized to the task. This finding may help take an initial step toward direct non-invasive MRI detection of the neuronal activity with improved temporal accuracy.     

Complementary aspects of diffusion imaging and fMRI; I: structure and function

Studying the intersection of brain structure and function is an important aspect of modern neuroscience. The development of magnetic resonance imaging (MRI) over the last 25 years has provided new and powerful tools for the study of brain structure and function. Two tools in particular, diffusion imaging and functional MRI (fMRI), are playing increasingly important roles in elucidating the complementary aspects of brain structure and function. In this work, we review basic technical features of diffusion imaging and fMRI for studying the integrity of white matter structural components and for determining the location and extent of cortical activation in gray matter, respectively. We then review a growing body of literature in which the complementary aspects of diffusion imaging and fMRI, applied as separate examinations but analyzed in tandem, have been exploited to enhance our knowledge of brain structure and function.     

Decreases in ADC observed in tissue areas during activation in the cat visual cortex at 9.4 T using high diffusion sensitization

Recent studies in the human visual cortex using diffusion-weighted functional magnetic resonance imaging (fMRI) have suggested that the apparent diffusion coefficient (ADC) decreases, in contrast to earlier studies that consistently reported ADC increases during neuronal activation. The changes, in either case, are hypothesized to provide the ability to improve the spatial specificity of fMRI over conventional blood-oxygenation-level-dependent (BOLD) methods. Most recently, the ADC decreases have been suggested as originating from transient cell swelling caused by either shrinkage of the extracellular space or some intracellular neuronal process that precedes the hemodynamic response. All of these studies have been conducted in humans and at lower magnetic fields, which can be limited by the signal-to-noise ratio (SNR). The low SNR can lead to significant partial-volume effects because of the lower spatial resolutions required to attain sufficient SNR in diffusion-weighted images. Human studies also have the potential confound of motion. At high magnetic fields and in animal model studies, these limitations are alleviated. At high fields, SNR increases, tissue signals are enhanced and signal changes inside the blood are significantly reduced compared to lower fields. In this work, we were able to measure a small but significant ADC decrease in tissue areas, in conjunction with brain activation in the cat visual cortex at 9.4 T when using highly diffusion-weighted images (b>1200 s/mm2) where intravascular effects are minimal. When using low b-values, delayed increases in the tissue ADC during activation were observed. No significant changes in ADC were observed in surface vessels for any diffusion weighting. Furthermore, we did not observe any temporal differences in the highly diffusion-weighted data compared to BOLD; however, although the changes may likely be vascular in nature, they are highly localized to the tissue areas.     

磁共振扩散张量成像中扩散敏感梯度磁场方向分布方案的研究进展

磁共振扩散张量成像可以定量无创研究人体内水分子在三维空间中的各向异性扩散规律,进而获取重要的病理及生理信息.为了得到水分子各向异性扩散信息,需要按照一定的方案依次施加不同方向的扩散敏感梯度磁场,测量水分子在这些方向上的扩散系数用以估算扩散张量.扩散张量成像测量结果的准确程度受梯度磁场方向分布方案的影响,本文对扩散敏感梯度磁场方向分布方案进行综述,包括完全随机方案、启发式方案、规则多面体式方案和数值优化方案等,分析这些方案的优势与局限性,并提出需进一步研究的问题.     

Diffusion-weighted imaging in epilepsy

Diffusion-weighted imaging (DWI) is a relatively new magnetic resonance imaging (MRI) technique that can be used to probe the microenvironment of water. Contrast in DWI depends on properties different from traditional T₁ and T₂ constrast, and is derived from the translational motion of water molecules. Since it is reasonable to think that a change in the microenvironment of water might be reflected in a change in water diffusion characteristics, the quantitative assessment of the (apparent) diffusion coefficient ADCw may represent a unique means of assessing tissue status. DWI has already shown great utility in the study of cerebral ischemia in animal models and has proved useful in the early identification of cerebral ischemia in patients. More recent reports have indicated a potential for DWI in studying epilepsy. Here, we briefly review some of what is known about the measurement of ADCw in ischemia and compare these results with what has recently been reported for epilepsy. In this manner we hope to better understand the underlying mechanisms behind changes in water diffusion associated with specific pathologies.     

Component structure of event-related fMRI responses in the different neurovascular compartments

In most functional magnetic resonance imaging (fMRI) studies, brain activity is localized by observing changes in the blood oxygenation level-dependent (BOLD) signal that are believed to arise from capillaries, venules and veins in and around the active neuronal population. However, the contribution from veins can be relatively far downstream from active neurons, thereby limiting the ability of BOLD imaging methods to precisely pinpoint neural generators. Hemodynamic measures based on apparent diffusion coefficients (ADCs) have recently been used to identify more upstream functional blood flow changes in the capillaries, arterioles and arteries. In particular, we recently showed that, due to the complementary vascular sensitivities of ADC and BOLD signals, the voxels conjointly activated by both measures may identify the capillary networks of the active neuronal areas. In this study, we first used simultaneously acquired ADC and BOLD functional imaging signals to identify brain voxels activated by ADC only, by both ADC and BOLD and by BOLD only, thereby delineating voxels relatively dominated by the arterial, capillary, and draining venous neurovascular compartments, respectively. We then examined the event-related fMRI BOLD responses in each of these delineated neurovascular compartments, hypothesizing that their event-related responses would show different temporal componentries. In the regions activated by both the BOLD and ADC contrasts, but not in the BOLD-only areas, we observed an initial transient signal reduction (an initial dip), consistent with the local production of deoxyhemoglobin by the active neuronal population. In addition, the BOLD-ADC overlap areas and the BOLD-only areas showed a clear poststimulus undershoot, whereas the compartment activated by only ADC did not show this component. These results indicate that using ADC contrast in conjunction with BOLD imaging can help delineate the various neurovascular compartments, improve the localization of active neural populations, and provide insight into the physiological mechanisms underlying the hemodynamic signals.     

Quantitative characterization of tissue microstructure with temporal diffusion spectroscopy

The signals recorded by diffusion-weighted magnetic resonance imaging (DWI) are dependent on the micro-structural properties of biological tissues, so it is possible to obtain quantitative structural information non-invasively from such measurements. Oscillating gradient spin echo (OGSE) methods have the ability to probe the behavior of water diffusion over different time scales and the potential to detect variations in intracellular structure. To assist in the interpretation of OGSE data, analytical expressions have been derived for diffusion-weighted signals with OGSE methods for restricted diffusion in some typical structures, including parallel planes, cylinders and spheres, using the theory of temporal diffusion spectroscopy. These analytical predictions have been confirmed with computer simulations. These expressions suggest how OGSE signals from biological tissues should be analyzed to characterize tissue microstructure, including how to estimate cell nuclear sizes. This approach provides a model to interpret diffusion data obtained from OGSE measurements that can be used for applications such as monitoring tumor response to treatment in vivo.     

A two-compartment gel phantom for optimization and quality assurance in clinical BOLD fMRI

Clinical applications of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) depend heavily on robust paradigms, imaging methods and analysis procedures. In this work, as a means to optimize and perform quality assurance of the entire imaging and analysis chain, a phantom that provides a well known and reproducible signal change similar to a block type fMRI experiment is presented. It consists of two gel compartments with slightly different T2 that dynamically enter and leave the imaged volume. The homogeneous gel in combination with a cylindrical geometry results in a well-defined T*2 difference causing a signal difference between the two compartments in T*2-weighted MR images. From time series data obtained with the phantom, maps of percent signal change (PSC) and t-values are calculated. As an example of image parameter optimisation, the phantom is demonstrated to be useful for accurate determination of the influence of echo time (TE) on BOLD fMRI results, taking the t-value as a measure of sensitivity. In addition, the phantom is proposed as a tool for quality assurance (QA) since reproducible time series and t-maps are obtained in a series of independent repeat experiments. The phantom is relatively simple to build and can therefore be used by any clinical fMRI center.     

Repeatability of echo-planar-based diffusion measurements of the human prostate at 3 T

Echo-planar-based diffusion-weighted imaging (DWI) of the prostate is increasingly being suggested as a viable technique, complementing information derived from conventional magnetic resonance imaging methods for use in tissue discrimination. DWI has also been suggested as a potentially useful tool in the assessment of tumor response to treatment. In this study, the repeatability of apparent diffusion coefficient (ADC) values obtained from both DWI and diffusion tensor imaging (DTI) has been assessed as a precursor to determining the magnitude of treatment-induced changes required for reliable detection. The repeatability values of DWI and DTI were found to be similar, with ADC values repeatable to within 35% or less over a short time period of a few minutes and a longer time period of a month. Fractional anisotropy measurements were found to be less repeatable (between 26% and 71%), and any changes duly recorded in longitudinal studies must therefore be treated with a degree of caution.     

Simulation and experimental verification of the diffusion in an anisotropic fiber phantom

Diffusion weighted magnetic resonance imaging enables the visualization of fibrous tissues such as brain white matter. The validation of this non-invasive technique requires phantoms with a well-known structure and diffusion behavior. This paper presents anisotropic diffusion phantoms consisting of parallel fibers. The diffusion properties of the fiber phantoms are measured using diffusion weighted magnetic resonance imaging and bulk NMR measurements. To enable quantitative evaluation of the measurements, the diffusion in the interstitial space between fibers is modeled using Monte Carlo simulations of random walkers. The time-dependent apparent diffusion coefficient and kurtosis, quantifying the deviation from a Gaussian diffusion profile, are simulated in 3D geometries of parallel fibers with varying packing geometries and packing densities. The simulated diffusion coefficients are compared to the theory of diffusion in porous media, showing a good agreement. Based on the correspondence between simulations and experimental measurements, the fiber phantoms are shown to be useful for the quantitative validation of diffusion imaging on clinical MRI-scanners.     

Bootstrap generation and evaluation of an fMRI simulation database

Computer simulations have played a critical role in functional magnetic resonance imaging (fMRI) research, notably in the validation of new data analysis methods. Many approaches have been used to generate fMRI simulations, but there is currently no generic framework to assess how realistic each one of these approaches may be. In this article, a statistical technique called parametric bootstrap was used to generate a simulation database that mimicked the parameters found in a real database, which comprised 40 subjects and five tasks. The simulations were evaluated by comparing the distributions of a battery of statistical measures between the real and simulated databases. Two popular simulation models were evaluated for the first time by applying the bootstrap framework. The first model was an additive mixture of multiple components and the second one implemented a non-linear motion process. In both models, the simulated components included the following brain dynamics: a baseline, physiological noise, neural activation and random noise. These models were found to successfully reproduce the relative variance of the components and the temporal autocorrelation of the fMRI time series. By contrast, the level of spatial autocorrelation was found to be drastically low using the additive model. Interestingly, the motion process in the second model intrisically generated some slow time drifts and increased the level of spatial autocorrelations. These experiments demonstrated that the bootstrap framework is a powerful new tool that can pinpoint the respective strengths and limitations of simulation models.     

Quantitative analysis of spatial distortions of diffusion techniques at 3T

Diffusion has been widely adopted in the clinical setting to study the microstructural tissue changes in conjunction with anatomic imaging and metabolic imaging to offer insights on the status of the tissue injury or lesion. However, geometric distortions caused by magnetic susceptibility effects, eddy currents and gradient imperfections greatly affect the clinical utility of the diffusion images. Several diffusion methods have been proposed in the recent years to obtain diffusion parameters with increased accuracy. In most cases, the comparisons to the clinical standard echo-planar imaging (EPI) diffusion are done visually without quantitative measurements. In this study, we present three simple, complementary quantitative methods of nonrigid image registration and shape analyses for evaluating spatial distortions on magnetic resonance images with application in comparing single-shot fast spin-echo (SSFSE) and EPI based diffusion measurements. These methods have confirmed the SSFSE based diffusion method is less distorted than the EPI based one, which is generally accepted through visual inspection.     

Earlier detection of tumor treatment response using magnetic resonance diffusion imaging with oscillating gradients

An improved method for detecting early changes in tumors in response to treatment, based on a modification of diffusion-weighted magnetic resonance imaging, has been demonstrated in an animal model. Early detection of therapeutic response in tumors is important both clinically and in pre-clinical assessments of novel treatments. Noninvasive imaging methods that can detect and assess tumor response early in the course of treatment, and before frank changes in tumor morphology are evident, are of considerable interest as potential biomarkers of treatment efficacy. Diffusion-weighted magnetic resonance imaging is sensitive to changes in water diffusion rates in tissues that result from structural variations in the local cellular environment, but conventional methods mainly reflect changes in tissue cellularity and do not convey information specific to microstructural variations at sub-cellular scales. We implemented a modified imaging technique using oscillating gradients of the magnetic field for evaluating water diffusion rates over very short spatial scales that are more specific for detecting changes in intracellular structure that may precede changes in cellularity. Results from a study of orthotopic 9L gliomas in rat brains indicate that this method can detect changes as early as 24 h following treatment with 1,3-bis(2-chloroethyl)-1-nitrosourea, when conventional approaches do not find significant effects. These studies suggest that diffusion imaging using oscillating gradients may be used to obtain an earlier indication of treatment efficacy than previous magnetic resonance imaging methods.     

Functional phantom for fMRI: a feasibility study

Functional magnetic resonance imaging (fMRI) reveals changes in blood oxygen level-dependent (BOLD) signal after considerable processing. This paper describes the implementation and testing of an fMRI phantom where electric current applied to a thin wire within a proton-rich medium substituted BOLD distortion of the magnetic field; the scanner detects these two distortions as practically identical signal changes. The magnitude of the change depended on the current strength. The phantom has a number of possible applications. Signal changes across sessions, days, instruments and individuals could be monitored. Placing the phantom close to a subject during an fMRI experiment could allow differentiating sensitivity changes in the scanner due to instrumentation from changes in the subject's state and performance during the experiment. The spatial extent of brain activations and effects of various changes in the chain of image formation could be analyzed using current-induced "activations". Furthermore, the phantom could expedite fMRI sequence development by reducing the need to scan human subjects, who introduce uncertainty to the signal. Thus, this fMRI phantom could be useful for both cognitive fMRI studies and scanner calibration.     

Removal of partial volume averaging with free water in MR diffusion tensor imaging using inversion recovery for b0 image

Purpose

To remove the partial volume averaging effect of free water in MR diffusion imaging of neural tissues by use of the fluid attenuated inversion recovery (FLAIR) without the penalty of an extended scan time.

Materials and methods

The magnetic resonance images were obtained from a normal volunteer in a coronal slice orientation at 3 T with the 20-channel rf coil. In diffusion imaging only the b0 images were obtained with the FLAIR contrast while the diffusion weighted images were obtained without the FLAIR contrast. A composition of FLAIR b0 and non-FLAIR diffusion weighted images was used in calculating the diffusion tensor and fractional anisotropy after compensating the reduced signal amplitude due to the inversion recovery in the FLAIR b0 images. The fractional anisotropy of the non-FLAIR, FLAIR, and the composite methods were analyzed for the mean and histogram in the corpus callosum, cervical spine, and the fornix tracts.

Results

The partial volume averaging effect was observed in the corpus callosum, the cervical spine, and the fornix tracts in the non-FLAIR b0 and diffusion images. The partial volume averaging effect was removed in the FLAIR diffusion images which took more than twice the scan time than the non-FLAIR diffusion imaging. The proposed composite FLAIR diffusion imaging removed the partial volume averaging effect as in the FLAIR diffusion imaging. The distribution of the FA histogram was very different between the non-FLAIR and FLAIR diffusion images, while it was very similar between the FLAIR and the composite FLAIR after correcting the white matter signal in the FLAIR b0 images.

Conclusions

The proposed composite FLAIR diffusion imaging method was equally effective in removing the partial volume averaging effect as the FLAIR diffusion imaging at a limited increase of the scan time since only a small number of b0 images needed to be obtained with the FLAIR contrast.     

Realignment strategies for awake-monkey fMRI data

Functional magnetic resonance imaging (fMRI) experiments with awake nonhuman primates (NHPs) have recently seen a surge of applications. However, the standard fMRI analysis tools designed for human experiments are not optimal for NHP data collected at high fields. One major difference is the experimental setup. Although real head movement is impossible for NHPs, MRI image series often contain visible motion artifacts. Animal body movement results in image position changes and geometric distortions. Since conventional realignment methods are not appropriate to address such differences, algorithms tailored specifically for animal scanning become essential. We have implemented a series of high-field NHP specific methods in a software toolbox, fMRI Sandbox (http://kyb.tuebingen.mpg.de/~stoewer/), which allows us to use different realignment strategies. Here we demonstrate the effect of different realignment strategies on the analysis of awake-monkey fMRI data acquired at high field (7 T). We show that the advantage of using a nonstandard realignment algorithm depends on the amount of distortion in the dataset. While the benefits for less distorted datasets are minor, the improvement of statistical maps for heavily distorted datasets is significant.     

The heterogeneous distribution of the liquid phase in partially filled porous glasses and its effect on self-diffusion

The spatial distribution of the liquid phase in a typical, partially filled, porous glass (VitraPor #5) has been examined with the aid of magnetic resonance microscopy and field gradient nuclear magnetic resonance diffusometry techniques. The correlation length of the material turned out to be long enough to permit the visualization of the microscopic heterogeneity of the material by magnetic resonance imaging. Contrasts are dominated by transverse relaxation depending on local filling degree, which in turn depends on local microstructure. The bimodal heterogeneity of the latter was also visualized by scanning electron microscopy. The effect of heterogeneity on an effective diffusion coefficient has been examined for polar (water) and nonpolar (cyclohexane) molecules.     

Fast spectroscopic imaging strategies for potential applications in fMRI

Technical aspects of two general fast spectroscopic imaging (SI) strategies, one based on gradient echo trains and the other on spin echo trains, are reviewed within the context of potential applications in the field of functional magnetic resonance imaging (fMRI). Fast spectroscopic imaging of water may prove useful for identifying mechanisms underlying the blood oxygenation level dependence (BOLD) of the water signal during brain activation studies. Reasonably rapid mapping of changes in proton signals from brain metabolites, like lactate, creatine or even neurotransmitter associated metabolites like GABA, is substantially more challenging but technically feasible particularly as higher field strengths become available. Fast spectroscopic methods directed towards the ³¹P signals from phosphocreatine (PCr) and adenosine tri-phosphates (ATP) are also technically feasible and may prove useful for studying cerebral energetics within fMRI contexts.