SUMMARY:In this article, the underlying theory of clinical diffusion MR imaging, including diffusion tensor imaging (DTI) and fiber tractography, is reviewed. First, a brief explanation of the basic physics of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping is provided. This is followed by an overview of the additional information that can be derived from the diffusion tensor, including diffusion anisotropy, color-encoded fiber orientation maps, and 3D fiber tractography. This article provides the requisite background for the second article in this 2-part review to appear next month, which covers the major technical factors that affect image quality in diffusion MR imaging, including the acquisition sequence, magnet field strength, gradient amplitude and slew rate, and multichannel radio-frequency coils and parallel imaging. The emphasis is on optimizing these factors for state-of-the-art DWI and DTI based on the best available evidence in the literature. Diffusion MR imaging of the brain was first adopted for use in clinical neuroradiology during the early 1990s and was found to have immediate utility for the evaluation of suspected acute ischemic stroke. Since that time, enormous strides forward in the technology of diffusion imaging have greatly improved image quality and enabled many new clinical applications. These include the diagnosis of intracranial pyogenic infections, masses, trauma, and vasogenic-versus-cytotoxic edema. Furthermore, the advent of diffusion tensor imaging (DTI) and fiber tractography has opened an entirely new noninvasive window on the white matter connectivity of the human brain. DTI and fiber tractography have already advanced the scientific understanding of many neurologic and psychiatric disorders and have been applied clinically for the presurgical mapping of eloquent white matter tracts before intracranial mass resections.This 2-part review explores the current state of the art for the acquisition and analysis of clinical diffusion imaging, including DTI and fiber tractography. This first article begins with an explanation of the basic physics and underlying theory of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping. This is followed by an overview of the additional information that can be derived from the diffusion tensor, including diffusion anisotropy, color-encoded fiber-orientation maps, and 3D fiber tractography. This article provides the necessary background for the second article, which focuses on the major technical factors that affect image quality in diffusion imaging, with an emphasis on optimizing these factors for state-of-the-art DWI and DTI based on the best available evidence in the literature. Theoretic Underpinnings of Diffusion ImagingBrownian Motion and Tissue Ultrastructure Diffusion, also known as "Brownian motion," refers to constant random microscopic molecular motion due to heat. At a fixed temperature, the rate of diffusion can be described by the Einstein equation 1 :where Ͻr 2 Ͼ refers...

SUMMARY:This second article of the 2-part review builds on the theoretic background provided by the first article to cover the major technical factors that affect image quality in diffusion imaging, including the acquisition sequence, magnet field strength, gradient amplitude, and slew rate as well as multichannel radio-frequency coils and parallel imaging. The sources of many common diffusion image artifacts are also explored in detail. The emphasis is on optimizing these technical factors for stateof-the-art diffusion-weighted imaging and diffusion tensor imaging (DTI) based on the best available evidence in the literature. An overview of current methods for quantitative analysis of DTI data and fiber tractography in clinical research is also provided. In this article, the major technical factors that affect image quality in diffusion MR imaging are evaluated in detail. The first half focuses on diffusion-weighted imaging (DWI). The strengths and weaknesses of single-shot echo-planar imaging, by far the most popular sequence for brain DWI, are considered, and alternative sequences are presented for special purpose applications. The effect of hardware and software variables such as magnetic field strengths, gradient amplitudes and slew rates, radio-frequency coils, and parallel imaging reconstruction methods is reviewed. The causes of common DWI artifacts are explained, and strategies are provided for minimizing artifacts and optimizing image quality.The second half of the article focuses on technical considerations specific to diffusion tensor imaging (DTI) and fiber tractography, including optimizing the b-values, the number and orientations of diffusion-weighted acquisitions, as well as the fiber tracking parameters. The undesirable effects of common problems such as low signal intensity-to-noise ratio (SNR) and pulsation artifact are reviewed. An overview is provided of current methods for analyzing quantitative DTI data for clinical research, including the reproducibility of DTI measurements. Throughout this review, the emphasis is on optimizing the many technical factors needed for state-of-the-art DWI and DTI based on the best available evidence in the literature. Technical Considerations for State-of-the-Art DWI Echo-Planar DWIAdvantages of Single-Shot Echo-Planar DWI. Because even minimal bulk patient motion during acquisition of DWIs can obscure the effects of the much smaller microscopic water motion due to diffusion, ultrafast imaging sequences are necessary for successful clinical DWI. Most commonly, diffusion imaging is performed by using spin-echo single-shot echoplanar imaging (SS-EPI) techniques. The term "single shot" means that an entire 2D image is formed from a single radiofrequency excitation pulse. Images can be acquired in a fraction of a second; therefore, artifact from physiologic cardiac and respiratory pulsatility and from patient motion is greatly reduced, including motion between acquisitions with different orientations of the diffusion-sensitizing gradients. Another advantage of S...

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Diffusion tensor imaging (DTI) studies of human brain development have consistently shown widespread, but nonlinear increases in white matter anisotropy through childhood, adolescence, and into adulthood. However, despite its sensitivity to changes in tissue microstructure, DTI lacks the specificity to disentangle distinct microstructural features of white and gray matter. Neurite orientation dispersion and density imaging (NODDI) is a recently proposed multi-compartment biophysical model of brain microstructure that can estimate non-collinear properties of white matter, such as neurite orientation dispersion index (ODI) and neurite density index (NDI). In this study, we apply NODDI to 66 healthy controls aged 7–63 years to investigate changes of ODI and NDI with brain maturation, with comparison to standard DTI metrics. Using both region-of-interest and voxel-wise analyses, we find that NDI exhibits striking increases over the studied age range following a logarithmic growth pattern, while ODI rises following an exponential growth pattern. This novel finding is consistent with well-established age-related changes of FA over the lifespan that show growth during childhood and adolescence, plateau during early adulthood, and accelerating decay after the fourth decade of life. Our results suggest that the rise of FA during the first two decades of life is dominated by increasing NDI, while the fall in FA after the fourth decade is driven by the exponential rise of ODI that overcomes the slower increases of NDI. Using partial least squares regression, we further demonstrate that NODDI better predicts chronological age than DTI. Finally, we show excellent test—retest reliability of NODDI metrics, with coefficients of variation below 5% in all measured regions of interest. Our results support the conclusion that NODDI reveals biologically specific characteristics of brain development that are more closely linked to the microstructural features of white matter than are the empirical metrics provided by DTI.