Advanced MR Neuroimaging

From Theory to Clinical Practice

Ioannis Tsougos

• Publisher: CRC Press
• Year: 2017
• Print ISBN: 9780367876500
• eBook ISBN: 9781351216524
• Edition: 1
• Topic: Physical Sciences
• Subtopic: Physics
• Index: Physics

Over the last decade, some of the greatest achievements in the field of neuroimaging have been related to remarkable advances in magnetic resonance techniques, including diffusion, perfusion, magnetic resonance spectroscopy, and functional MRI. Such techniques have provided valuable insights into tissue microstructure, microvasculature, metabolism and brain connectivity.

Previously available mostly in research environments, these techniques are now becoming part of everyday clinical practice in a plethora of clinical MR systems. Nevertheless, despite growing interest and wider acceptance, there remains a lack of a comprehensive body of knowledge on the subject, exploring the intrinsic complexity and physical difficulty of the techniques.

This book focuses on the basic principles and theories of diffusion, perfusion, magnetic resonance spectroscopy, and functional MRI. It also explores their clinical applications and places emphasis on the associated artifacts and pitfalls with a comprehensive and didactic approach.

This book aims to bridge the gap between research applications and clinical practice. It will serve as an educational manual for neuroimaging researchers and radiologists, neurologists, neurosurgeons, and physicists with an interest in advanced MR techniques. It will also be a useful reference text for experienced clinical scientists who wish to optimize their multi-parametric imaging approach.

1

Diffusion MR Imaging

1.1Introduction

1.1.1Diffusion

Focus Point

•Particles suspended in a fluid (liquid or gas) are forced to move in a random motion called “Brownian motion.”

•Diffusion is “Brownian motion.”

Diffusion refers to the random, microscopic movement of particles due to thermal collisions. Particles suspended in a fluid (liquid or gas) are forced to move in a random motion, which is often called “Brownian motion” or pedesis (from Greek: πήδησις [meaning “leaping”]) resulting from their collision with the atoms or molecules in the gas or liquid.

This diffuse motion was named after Robert Brown, the famous English botanist, who observed under a microscope that pollen grains in water were in a constant state of agitation. It was as early as 1827 and, unfortunately, he was never able to fully explain the mechanisms that caused this motion. He initially assumed that he was observing something “alive,” but later he realized that something else was the cause of this motion since he had detected the same fluctuations when studying dead matter such as dust.

Atoms and molecules had long been theorized as the constituents of matter, and many decades later (in 1905) Albert Einstein published a paper explaining in precise detail how the motion that Brown had observed was a result of the pollen being moved by individual water molecules (Einstein, 1905). In the introduction of his paper, it is stated that

… according to the molecular-kinetic theory of heat, bodies of a microscopically visible size suspended in liquids must, as a result of thermal molecular motions, perform motions of such magnitudes that they can be easily observed with a microscope. It is possible that the motions to be discussed here are identical with so-called Brownian molecular motion; however, the data available to me on the latter are so imprecise that I could not form a judgment on the question ….

To get a feeling of the physical meaning of diffusion, consider a diffusing particle that is subjected to a variety of collisions that we can consider random, in the sense that each such event is virtually unrelated to its previous event. It makes no difference whether the particle is a molecule of perfume diffusing in air, a solute molecule in a solution, or a water molecule inside a medium diffusing due to the medium’s thermal energy.

Einstein described the mathematics behind Brownian motion and presented it as a way to indirectly confirm the existence of atoms and molecules in the formulation of a diffusion equation, in which the diffusion coefficient is related to the mean squared displacement of a Brownian particle.

In other words, Einstein sought to determine how far a Brownian particle travels in a given time interval.

For this purpose, he introduced the “displacement distribution,” which quantifies the fraction of particles that will traverse a certain distance within a particular timeframe, or equivalently, the likelihood that a single given particle will undergo that displacement.

Using this concept, Einstein was able to derive an explicit relationship between displacement and diffusion time in the following equation:

$$\langle x^2\rangle =6Dt$$(1.1)

where 〈x²〉 is the mean-squared displacement of particles during a diffusion time t, and D is the diffusion coefficient. The distribution of squared displacements takes a Gaussian form, with the peak being at zero displacement and with equal probability of displacing a given distance from the origin no matter in which direction it is measured. Actually, the Gaussian diffusion can be calculated in one, two, or three dimensions. The form of the Gaussian in one dimension is the familiar bell-shaped curve and the displacement is 2Dt. In two dimensions, if the medium is isotropic, the cross-section of the curve is circular, with the radius given by 4Dt, centered on the origin. When extended to three dimensions, the iso-probability surface is a sphere, of radius 6Dt as in Equation 1.1, and again centered on the origin.

The concept of diffusion can be easily demonstrated by adding a few drops of ink to a glass of water. The only pre-requirement is for the water in the glass to be still. Initially, the ink will be concentrated in a very small volume, and then with time, it will diffuse into the rest of the water until the concentration of the ink is uniform throughout the glass. The speed of this process of diffusion, or the rate of change of concentration of the ink, gives a measure of the property of medium where diffusion takes place. In that sense, if we could follow the diffusion of water molecules into the brain, we would reveal aspects of functionality of the normal brain tissue itself. More importantly, by understanding in more detail normal brain functionality, we would then be able to analyze the kind of changes that may occur in the brain when it is affected by various disease processes.

In other words, diffusion properties represent the microscopic motion of water molecules of the tissue; hence it can be used to probe local microstructure. As water molecules are agitated by thermal energy, they diffuse inside the body, hindered by the boundaries of the surrounding tissues or other biological barriers. By probing this movement, the reconstruction of the boundaries that hinder this motion can be visualized.

1.1.2Diffusion in Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) with its excellent soft tissue visualization and variety of imaging sequences has evolved to one of the most important noninvasive diagnostic tools for the detection and evaluation of the treatment response of cerebral tumors. Nevertheless, conventional MRI presents limitations regarding certain tumor properties, such as infiltration and grading (Hakyemez et al., 2010). It is evident that a more accurate detection of infiltrating cells beyond the tumoral margin and a more precise tumor grading would strongly enhance the efficiency of differential diagnosis. Diffusion-weighted imaging (DWI) provides noninvasively significant structural information at a cellular level, highlighting aspects of the underlying brain pathophysiology.

In theory, DWI is based on the freedom of motion of water molecules, which can reflect tissue microstructure; hence the possibility to characterize tumoral and peritumoral microarchitecture, based on water diffusion findings, may provide clinicians a whole new perspective on improving the management of brain tumors. Although, initially, DWI was established as an important method in the assessment of stroke (Schellinger et al., 2001), a large number of studies have been conducted in order to assess whether the quantitative information derived by DWI may aid differential diagnosis and tumor grading (Fan et al., 2006; Lam et al., 2002; Kono et al., 2001; Yamasaki et al., 2005), especially in cases of ambiguous cerebral neoplasms (Nagar et al., 2008). Moreover, DWI may also have a significant role in therapeutic follow-up and prognosis establishment in various brain lesions. Given its important clinical role, DWI should be an integral part of diagnostic brain imaging protocols (Schmainda, 2012; Zakaria et al., 2014).

1.2Diffusion Imaging: Basic Principles

1.2.1Diffusion-Weighted Imaging

Focus Point

•Particles suspended in a fluid (liquid or gas) are forced to move in a random motion, which is often called “Brownian motion.”

•Diffusion is considered the result of the random motion of water molecules.

•Molecular diffusion in tissues is not free, but reflects interactions with many obstacles, such as macromolecules, fibers, membranes, etc.

•By understanding normal brain diffusion, we would be able to analyze the kind of changes that may occur in the brain when it is affected by various disease processes.

•DWI represents the microscopic motion of water molecules hence probes local tissue microstructure.

As already explained, diffusion...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication Page
• Contents
• Series Preface
• Preface
• About the Author
• 1 Diffusion MR Imaging
• 2 Artifacts and Pitfalls in Diffusion MRI
• 3 Perfusion MR Imaging
• 4 Artifacts and Pitfalls of Perfusion MRI
• 5 Magnetic Resonance Spectroscopy
• 6 Artifacts and Pitfalls of MRS
• 7 Functional Magnetic Resonance Imaging (fMRI)
• 8 Artifacts and Pitfalls of fMRI
• 9 The Role of Multiparametric MR Imaging—Advanced MR Techniques in the Assessment of Cerebral Tumors
• Index