Quantification of non-Gaussianity for water diffusion in brain by means of diffusional kurtosis imaging (DKI) is reviewed. Diffusional non-Gaussianity is a consequence of tissue structure that creates diffusion barriers and compartments. The degree of non-Gaussianity is conveniently quantified by the diffusional kurtosis and derivative metrics, such as the mean, axial, and radial kurtoses. DKI is a diffusion-weighted MRI technique that allows the diffusional kurtosis to be estimated with clinical scanners using standard diffusion-weighted pulse sequences and relatively modest acquisition times. DKI is an extension of the widely used diffusion tensor imaging method, but requires the use of at least 3 b-values and 15 diffusion directions. This review discusses the underlying theory of DKI as well as practical considerations related to data acquisition and post-processing. It is argued that the diffusional kurtosis is sensitive to diffusional heterogeneity and suggested that DKI may be useful for investigating ischemic stroke and neuropathologies, such as Alzheimer's disease and schizophrenia. Copyright (C) 2010 John Wiley & Sons, Ltd.
Obtaining reliable data and drawing meaningful and robust inferences from diffusion MRI can be challenging and is subject to many pitfalls. The process of quantifying diffusion indices and eventually comparing them between groups of subjects and/or correlating them with other parameters starts at the acquisition of the raw data, followed by a long pipeline of image processing steps. Each one of these steps is susceptible to sources of bias, which may not only limit the accuracy and precision, but can lead to substantial errors. This article provides a detailed review of the steps along the analysis pipeline and their associated pitfalls. These are grouped into (1) pre-processing of data; (2) estimation of the tensor; (3) derivation of voxelwise quantitative parameters; (4) strategies for extracting quantitative parameters; and finally (5) intra-subject and inter-subject comparison, including region of interest, histogram, tract-specific and voxel-based analyses. The article covers important aspects of diffusion MRI analysis, such as motion correction, susceptibility and eddy current distortion correction, model fitting, region of interest placement, histogram and voxel-based analysis. We have assembled 25 pitfalls (several previously unreported) into a single article, which should serve as a useful reference for those embarking on new diffusion MRI-based studies, and as a check for those who may already be running studies but may have overlooked some important confounds. While some of these problems are well known to diffusion experts, they might not be to other researchers wishing to undertake a clinical study based on diffusion MRI. Copyright (C) 2010 John Wiley & Sons, Ltd.
A theoretical triglyceride model was developed for in vivo human liver fat H-1 MRS characterization, using the number of double bonds (-CH=CH-), number of methylene-interrupted double bonds (-CH=CH-CH2-CH=CH-) and average fatty acid chain length. Five 3 T, single-voxel, stimulated echo acquisition mode spectra (STEAM) were acquired consecutively at progressively longer TEs in a fat-water emulsion phantom and in 121 human subjects with known or suspected nonalcoholic fatty liver disease. T-2-corrected peak areas were calculated. Phantom data were used to validate the model. Human data were used in the model to determine the complete liver fat spectrum. In the fat-water emulsion phantom, the spectrum predicted by the model (based on known fatty acid chain distribution) agreed closely with spectroscopic measurement. In human subjects, areas of CH2 peaks at 2.1 and 1.3 ppm were linearly correlated (slope, 0.172; r = 0.991), as were the 0.9 ppm CH3 and 1.3 ppm CH2 peaks (slope, 0.125; r = 0.989). The 2.75 ppm CH2 peak represented 0.6% of the total fat signal in high-liver-fat subjects. These values predict that 8.6% of the total fat signal overlies the water peak. The triglyceride model can characterize human liver fat spectra. This allows more accurate determination of liver fat fraction from MRI and MRS. Copyright (C) 2010 John Wiley & Sons, Ltd.
Endogenous chemical exchange saturation transfer (CEST) effects are always diluted by competing effects, such as direct water proton saturation (spillover) and semi-solid macromolecular magnetization transfer (MT). This leads to unwanted T-2 and MT signal contributions that lessen the CEST signal specificity to the underlying biochemical exchange processes. A spillover correction is of special interest for clinical static field strengths and protons resonating near the water peak. This is the case for all endogenous CEST agents, such as amide proton transfer, -OH-CEST of glycosaminoglycans, glucose or myo-inositol, and amine exchange of creatine or glutamate. All CEST effects also appear to be scaled by the T-1 relaxation time of water, as they are mediated by the water pool. This forms the motivation for simple metrics that correct the CEST signal. Based on eigenspace theory, we propose a novel magnetization transfer ratio (MTRRex), employing the inverse Z-spectrum, which eliminates spillover and semi-solid MT effects. This metric can be simply related to R-ex, the exchange-dependent relaxation rate in the rotating frame, and k(a), the inherent exchange rate. Furthermore, it can be scaled by the duty cycle, allowing for simple translation to clinical protocols. For verification, the amine proton exchange of creatine in solutions with different agar concentrations was studied experimentally at a clinical field strength of 3T, where spillover effects are large. We demonstrate that spillover can be properly corrected and that quantitative evaluation of pH and creatine concentration is possible. This proves that MTRRex is a quantitative and biophysically specific CEST-MRI metric. Applied to acute stroke induced in rat brain, the corrected CEST signal shows significantly higher contrast between the stroke area and normal tissue, as well as less B-1 dependence, than conventional approaches. Copyright (c) 2014 John Wiley & Sons, Ltd.
In general, multiple components such as water direct saturation, magnetization transfer (MT), chemical exchange saturation transfer (CEST) and aliphatic nuclear Overhauser effect (NOE) contribute to the Z-spectrum. The conventional CEST quantification method based on asymmetrical analysis may lead to quantification errors due to the semi-solid MT asymmetry and the aliphatic NOE located on a single side of the Z-spectrum. Fitting individual contributors to the Z-spectrum may improve the quantification of each component. In this study, we aim to characterize the multiple exchangeable components from an intracranial tumor model using a simplified Z-spectral fitting method. In this method, the Z-spectrum acquired at low saturation RF amplitude (50Hz) was modeled as the summation of five Lorentzian functions that correspond to NOE, MT effect, bulk water, amide proton transfer (APT) effect and a CEST peak located at +2ppm, called CEST@2ppm. With the pixel-wise fitting, the regional variations of these five components in the brain tumor and the normal brain tissue were quantified and summarized. Increased APT effect, decreased NOE and reduced CEST@2ppm were observed in the brain tumor compared with the normal brain tissue. Additionally, CEST@2ppm decreased with tumor progression. CEST@2ppm was found to correlate with the creatine concentration quantified with proton MRS. Based on the correlation curve, the creatine contribution to CEST@2ppm was quantified. The CEST@2ppm signal could be a novel imaging surrogate for in vivo creatine, the important bioenergetics marker. Given its noninvasive nature, this CEST MRI method may have broad applications in cancer bioenergetics. Copyright (c) 2014 John Wiley & Sons, Ltd.
This article gives an overview of microstructure imaging of the brain with diffusion MRI and reviews the state of the art. The microstructure‐imaging paradigm aims to estimate and map microscopic properties of tissue using a model that links these properties to the voxel scale MR signal. Imaging techniques of this type are just starting to make the transition from the technical research domain to wide application in biomedical studies. We focus here on the practicalities of both implementing such techniques and using them in applications. Specifically, the article summarizes the relevant aspects of brain microanatomy and the range of diffusion‐weighted MR measurements that provide sensitivity to them. It then reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure, as well as the expanding areas of application. Next we focus on practicalities of designing a working microstructure imaging technique: model selection, experiment design, parameter estimation, validation, and the pipeline of development of this class of technique. The article concludes with some future perspectives on opportunities in this topic and expectations on how the field will evolve in the short‐to‐medium term. The central vision in microstructure imaging is of virtual histology: estimating and mapping histological features of tissue using non‐invasive imaging techniques, such as MRI. This article reviews the state of the art in this emerging paradigm. It focusses on the practicalities of both developing such techniques and using them in applications. We also provide some future perspectives on opportunities in this topic and expectations on how the field will evolve in the short‐to‐medium term.
Why has diffusion MRI become a principal modality for mapping connectomes in vivo ? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI‐based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments. Why has diffusion MRI become a principal modality for mapping connectomes in vivo ? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we address in this review. We provide an overview of the key methods to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and limitations.
Understanding typical, healthy brain development provides a baseline from which to detect and characterize brain anomalies associated with various neurological or psychiatric disorders and diseases. Diffusion MRI is well suited to study white matter development, as it can virtually extract individual tracts and yield parameters that may reflect alterations in the underlying neural micro‐structure (e.g. myelination, axon density, fiber coherence), though it is limited by its lack of specificity and other methodological concerns. This review summarizes the last decade of diffusion imaging studies of healthy white matter development spanning childhood to early adulthood (4–35 years). Conclusions about anatomical location, rates, and timing of white matter development with age are discussed, as well as the influence of image acquisition, analysis, age range/sample size, and statistical model. Despite methodological variability between studies, some consistent findings have emerged from the literature. Specifically, diffusion studies of neurodevelopment overwhelmingly demonstrate regionally varying increases of fractional anisotropy and decreases of mean diffusivity during childhood and adolescence, some of which continue into adulthood. While most studies use linear fits to model age‐related changes, studies with sufficient sample sizes and age range provide clear evidence that white matter development (as indicated by diffusion) is non‐linear. Several studies further suggest that maturation in association tracts with frontal‐temporal connections continues later than commissural and projection tracts. The emerging contributions of more advanced diffusion methods are also discussed, as they may reveal new aspects of white matter development. Although non‐specific, diffusion changes may reflect increases of myelination, axonal packing, and/or coherence with age that may be associated with changes in cognition. This paper reviews diffusion MRI studies of neurodevelopment in childhood and adolescence. Despite large variation in methods and age ranges, several key themes have emerged: (i) studies overwhelmingly find increases of fractional anisotropy and decreases of mean diffusivity in white matter during development; (ii) changes are widespread throughout the brain; (iii) development is non‐linear, occurring faster at younger ages; and (iv) rates/plateaus are regionally dependent, with the most prolonged development observed in frontal/temporal areas.
The ability of fiber tractography to delineate non‐invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience. In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter. For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery. Fiber tracking has also claimed a central role in the field of “connectomics,” a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide. Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation. As neuroscientists are eager to study the brain's connectivity, the quantification of tractography‐derived “connection strengths” between distant brain regions is becoming increasingly popular. However, this practice is often frowned upon by fiber‐tracking experts. In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking. While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state‐of‐the‐art techniques, many issues that severely bias the resulting structural “connectomes” remain unresolved. The ability of fiber tractography to delineate the fiber pathways of the brain non‐invasively has raised possibilities for clinical applications and offers enormous potential for neuroscience. Despite its unique abilities and exciting applications, fiber tracking is not without controversy. This paper reviews the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking.