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Assessment of Abdominal Adiposity and Organ Fat with Magnetic Resonance Imaging

As the prevalence of obesity and type II diabetes continues to rise, accurate tools for quantifying abdominal body and organ fat are critically needed to assist researchers investigating therapeutic and preventive measures against obesity and its comorbidities. Fat accumulation in the liver, pancreas, and skeletal muscles are indicators of diabetes, the metabolic syndrome, and obesity. In addition to organ fat, the distribution of abdominal subcutaneous and visceral adipose tissue play critical roles in determining one’s metabolic profile and health risks. Rapid comprehensive human body fat quantification remains an unsolved problem and unmet need. Magnetic resonance imaging (MRI) is the most promising modality to address such challenge. MRI is non-invasive, utilizes no ionizing radiation, provides 3D visualization of the anatomy, has immense flexibility in tissue contrast mechanisms, is safely repeatable across longitudinal studies without restrictions, and is applicable to cohorts of all ages, particularly children and adolescents. This chapter provides the reader with an overview of MRI techniques for measuring abdominal adiposity and organ fat. The described methods are useful in preventive medicine for early detection of health risks, in studies for identifying changes in subcutaneous and visceral adipose tissue volumes due to intervention, such as bariatric surgery and diet, and in comparing trends between gender and ethnicity groups. The chapter is divided into several sections. It begins with a description of MRI principles. This provides the reader with basic knowledge and familiarizes them with terminology. Next, emphasis is placed on specific MRI approaches such as T1-weighted imaging and magnetic resonance spectroscopy (MRS) as well as state-of-the-art chemical shift imaging (CSI) techniques that are used to assess body and organ fat. Each technique is discussed in detail and compared to its counterparts. Following this description of imaging methodologies, post-processing and image analysis procedures for extracting adipose tissue depot volumes and percent organ fat content from abdominal MRI data using commercially available software are explained. The advantages and disadvantages of each MRI approach with respect to imaging parameters, spatial resolution, subject motion, scan time, and appropriate fat quantitative endpoints are emphasized. The benefits and challenges of contiguous 3D


Introduction
As the prevalence of obesity and type II diabetes continues to rise, accurate tools for quantifying abdominal body and organ fat are critically needed to assist researchers investigating therapeutic and preventive measures against obesity and its comorbidities. Fat accumulation in the liver, pancreas, and skeletal muscles are indicators of diabetes, the metabolic syndrome, and obesity. In addition to organ fat, the distribution of abdominal subcutaneous and visceral adipose tissue play critical roles in determining one's metabolic profile and health risks. Rapid comprehensive human body fat quantification remains an unsolved problem and unmet need. Magnetic resonance imaging (MRI) is the most promising modality to address such challenge. MRI is non-invasive, utilizes no ionizing radiation, provides 3D visualization of the anatomy, has immense flexibility in tissue contrast mechanisms, is safely repeatable across longitudinal studies without restrictions, and is applicable to cohorts of all ages, particularly children and adolescents. This chapter provides the reader with an overview of MRI techniques for measuring abdominal adiposity and organ fat. The described methods are useful in preventive medicine for early detection of health risks, in studies for identifying changes in subcutaneous and visceral adipose tissue volumes due to intervention, such as bariatric surgery and diet, and in comparing trends between gender and ethnicity groups. The chapter is divided into several sections. It begins with a description of MRI principles. This provides the reader with basic knowledge and familiarizes them with terminology. Next, emphasis is placed on specific MRI approaches such as T 1 -weighted imaging and magnetic resonance spectroscopy (MRS) as well as state-of-the-art chemical shift imaging (CSI) techniques that are used to assess body and organ fat. Each technique is discussed in detail and compared to its counterparts. Following this description of imaging methodologies, post-processing and image analysis procedures for extracting adipose tissue depot volumes and percent organ fat content from abdominal MRI data using commercially available software are explained. The advantages and disadvantages of each MRI approach with respect to imaging parameters, spatial resolution, subject motion, scan time, and appropriate fat quantitative endpoints are emphasized. The benefits and challenges of contiguous 3D acquisitions over 2D multi-slice approaches are reviewed. Finally, practical considerations in implementing these methods in the clinical setting are offered, along with an outlook on future fat imaging research. Throughout the chapter, illustrative figures accompany each section to highlight the topics.

Signal origin, magnetization, and precession: The "M"
The signal in MRI arises from the nucleus of an atom, which consists of one or more positively charged protons. Figure 1a illustrates a naturally spinning nucleus, which can be considered as a charged particle in motion (e.g. an electric current). The movement of this electric current, albeit an infinitesimally small one, produces a magnetic dipole M about the nucleus. While the inherent magnetic dipole of a single atom is small, a large population of atoms in an object or human body can be significant and detectable. The magnetization of hydrogen whose nucleus consists of a single proton ( 1 H) is frequently measured in MRI, due primarily to its high natural abundance in organic life forms. The two most common hydrogen-containing molecules are water and fat (e.g. triglycerides), as shown in Figure 1d. Their magnetization signals within an imaging object or patient are detected, excited, measured, and reconstructed into diagnostic images. The superconducting MRI magnets used for human imaging generates a magnetic field (B 0 ) strength between 0.5 and 3.0 Tesla (T). Routine imaging is performed at 1.5T, and in more recent years at 3.0T. By comparison, these strengths are much greater than the natural North-South magnetic fields of Earth's poles, which varies between 0.3 and 0.6 Gauss (G), where 1T=10,000G. The B 0 field is oriented along the longitudinal axis of the magnet bore, as shown in Figure 1c. Prior to placing a subject within the magnet bore, each proton spin within a population has its magnetic dipole oriented in a random direction (Fig. 1b). The net magnetization of this population is zero and not detectable. Figure 1b illustrates spin behaviors after the subject enters the B 0 field. The magnetic field forces the individual dipoles to either align with or against B 0 . Only a small fraction of spins preferentially align with B 0 than against it. Nevertheless, it is this slightly greater population of dipoles that align with B 0 and the abundance of hydrogen atoms in vivo that is responsible for producing a measurable net signal. Since this net magnetization is oriented along the longitudinal axis of the magnet bore, it is referred to as M longitudinal . With higher B 0 fields, the proportion of aligned spins increases and gives rise to a larger M longitudinal and hence a stronger signal. Interactions between the magnetization and the B 0 field causes precession. The Larmor frequency or rate of precession f Larmor is determined by Equation 1, where /(2) is the gyromagnetic ratio of the nuclei of interest. For protons, it is 4,257 Hz/ G.
The Larmor frequency is nuclei specific and linearly proportional to B 0 . At B 0 = 1.5T, proton f Larmor is 63.86 MHz; at 3.0T, it doubles to 127.6 MHz (1MHz = 10 6 Hz, 1Hz=1 cycle/ second). It is precession, the continuous motion of spin magnetization M at the Larmor frequency that induces signals needed for image generation. Thus, analogous to a radio transmitter and receiver, MRI signal detection is based on frequency, and as will be discussed later, it is precisely the Larmor frequency difference between hydrogens in water and fat that underpins several approaches to body and organ fat quantification.

Radiofrequency excitation and relaxation: the "R"
Once M longitudinal is generated, it must be tipped, or nutated, into the orthogonal transverse plane in order for signals from precessing spins to be detected by local receiver coil(s). This is shown in Figure 2 After M transverse is created and the RF pulse is turned off, the spins will gradually begin to " relax" . Two simultaneous and independent relaxation processes occur. First, the signal will recover towards the longitudinal axis, progressively restoring the M longitudinal vector to its original state prior to RF excitation. The rate recovery is denoted by the T 1 spin-lattice term, an exponential constant. Second, spins in the transverse plane will gradually lose signal coherence. This causes a progressive reduction of M transverse at an exponential decay rate of T 2 , also known as spin-spin relaxation. T 1 and T 2 values are material, tissue, and B 0 dependent (De Bazelaire et al., 2004;Stanisz et al., 2005).  In an MRI experiment, RF pulses are repeated in the form of a pulse sequence to gather adequate data for each imaging slice of interest. After each RF excitation, a signal or echo is measured at a specified echo time (TE). The time between successive RF excitations is the repetition time (TR). By varying the flip angle, TR, and TE, an operator has immense flexibility over tissue signal contrast in the resultant image. Figure 3 illustrates two axial sections of a human brain, acquired with different pairs of TR and TE values. Note that the signal contrast between gray and white matter changes. TR and TE pairs that exploit the difference in T 1 values between tissues yield T 1 -weighted images (Fig. 3a), where white matter (shorter T 1 , faster recovery) is brighter than gray matter (longer T 1 , slower recovery). On the contrary, TR/ TE pairs that exploit differences in T 2 yield T 2 -weighted images (Fig. 3b), where white matter (shorter T 2 , faster decay) is darker than gray matter (longer T 2 , slower decay). Similar tissue contrasts can be achieved by changing only the flip angle (Fig. 3c, d).

Magnetic gradients, k-space, and the Fourier transform: the "I"
In a pulse sequence, each echo gathers signal contrast and spatial information about the imaging object and its spin ensemble. A pulse sequence ( Fig. 4) contains not only the RF component, but also three magnetic gradients, G slice , G phase, and G read . These gradients perturb the B 0 field linearly as a function of distance. In synchrony with G slice , a RF pulse can excite an isolated section of the imaging object (e.g. the head, abdomen, or knee). This is known as volume or slice-selection. G phase and G read then provide information on the spatial locations of spins from the excited section by establishing a one-to-one relationship between their locations and their unique precessing frequencies and phases (spatial-encoding). Aligning the three gradients in various combinations yields the desired image orientation. Fig. 4. A pulse sequence diagram. Gradient G slice is applied in conjunction with the RF pulse to specifically excite a desired section of the imaging object at a given orientation. This is followed by 2D or 3D spatial-encoding (*), where locations of spins are determined based on their precessing frequency and their phase accrual in the presence of G read , G phase , and G slice .
Each acquired echo provides a snapshot, or " view" of the underlying image. By iterating through multiple spatial (frequency and phase) encoding loops, differential " views" of the www.intechopen.com imaging object are obtained. Each echo is an oscillatory function that represents the sum of a spin ensemble observed from a particular " view" , and each of the involved spins are precessing at a slightly different gradient-tuned frequency and phase. To separate the signal into its individual components, each echo is first stored as a line of data points in the spatial frequency domain, or k-space. Once all echoes are acquired, a Fourier Transform (FT) operation generates the image (Fig. 5).
Fig. 5. Image formation. A pulse sequence collects echoes and fills the data in k-space after each RF pulse. A Fourier Transform yields the net image result after the pulse sequence iterates through multiple spatial-encoding " view" loops. An echo example is shown in red. Resolution improves with more " views" . (b) k-space can be considered in two parts, a central region containing low-spatial-frequency data inside the dashed ellipse, and a highspatial-frequency region outside the ellipse. Images show that low-spatial-frequencies give rise to gross contrast, while high-spatial-frequencies contribute to edges and details. When an echo is acquired, it samples both central and peripheral k-space.

Field of view, spatial resolution, and acquisition time
Field of view (FOV) describes the size of the imaging volume and is set by the MRI operator. Spatial resolution refers to the physical dimensions of each 2D pixel or 3D voxel. Smaller dimensions provide greater spatial resolution and resolving power to delineate fine www.intechopen.com structures. Spatial resolution is determined by the number of acquired k-space data samples (N), and can be computed as the ratio of FOV and N along each axis. Higher resolution usually demands a greater number of samples, which consequently implies a longer acquisition time of the MRI experiment. This is because it typically takes one TR repetition to acquire each of the N phase and N slice phase-encoding " views" . Figure 6a illustrates a series of images of a circular object. N read was set constant (horizontal axis) while N phase (vertical axis) was varied. With only one phase-encoding " view" , vertical spatial resolution is non-existent. With nine " views" , the circular outline and embedded parallel bars become visible. With 32, 64, and 128 " views" , the detail of the bars are improved. Figure 6b illustrates a key property of k-space. A full k-space data set is shown along with its image. If only central k-space is reconstructed, the resultant image exhibits the overall shape and signal contrast of the object, but is blurry and lacks spatial detail. Conversely if only the peripheral data points are reconstructed, the resultant image is devoid of any signal contrast, but shows edge detail. Thus, both central and peripheral k-space are critical for high image quality.

Magnetic resonance imaging of fat
As the prevalence of obesity and type II diabetes continue to rise (Flegal et al., 2010), evidence has attributed obesity-related health risks to the accumulation of subcutaneous and visceral adipose tissue (SAT, VAT) and ectopic organ fat (Choudhary et al., 2007;Despres et al., 2008). Non-invasive assessment of body and organ fat has become an important component in obesity research and fat measures are useful as biomarkers to stratify risks and evaluate the efficacy of therapies. Many methods have been historically available for body fat assessment (Ellis, 2000;Mattsson, 2006). Anthropometry, hydrodensitometry, air displacement plethysmography (ADP), bioelectric impedance (BIA), and dual energy X-ray absorptiometry (DEXA) have been widely used. The first four are indirect techniques because they measure body density or resistance, which are then converted into percent body fat using generalized equations (Jackson, 1982). Hydrodensitometry and ADP are limited to estimating total body fat, while BIA and DEXA are limited to total and regional body fat measures. Most importantly, these indirect methods are not able to differentiate between SAT and VAT or identify ectopic fat. An increased utilization of computed tomography (CT) , Kvist et al., 1988Seidell et al., 1990), quantitative magnetic resonance (QMR) (Napolitano et al., 2008), and particularly MRI for body fat assessment (Abate et al., 1997;Berglund et al., 2010;Brix et al., 1993;Lancaster et al., 1991;Machann, et al., 2005;Ross et al., 2000;Schick et al., 2002;Siegel et al., 2007;Thomas et al., 2005;) has been reported in recent years. Along with DEXA, CT, QMR, and MRI are direct techniques because they identify fat based on the tissue's unique properties and signals in each modality. QMR does not yield images and is limited to measuring total body fat. In contrast, CT and MRI can differentiate SAT and VAT with multi-dimensional images. However, only MRI, with its sensitive contrast mechanisms for water and fat-based tissues, can accurately quantify ectopic fat. MRI is emerging as a comprehensive tool for fat quantification. The following sections describe fundamental MRI concepts that underpin T 1 -weighted imaging, MR spectroscopy (MRS), and state-of-the-art chemical shift imaging (CSI).

T 1 -Weighted MRI
As discussed in Section 2.2, MRI signal intensities are influenced by many factors including proton density, tissue-specific relaxation rates, the flip angle, and pulse sequence timings.
The T 1 of fat is one of the shortest amongst tissues in vivo. This indicates a very rapid recovery of its longitudinal magnetization between successive RF excitations. Therefore, by using a T 1 -weighted sequence, strong tissue contrast can be achieved between short-T 1 fat tissues and muscles and organs with longer T 1 values (Fig. 7). Bright fat can thus be easily identified and delineated by simple signal intensity thresholding (e.g. cutoff) from darker structures. T 1 -weighted sequences are very common in clinical MRI and are a part of nearly every protocol and anatomy. They have been used by several investigators as the standard workhorse tool for assessing SAT and VAT distributions (Machann et al., 2005;Ross et al. 2000;Siegel et al., 2007). T 1 -weighted sequences are available as standard software on all commercial MRI scanners (GE, Philips, Siemens, Hitachi, and Toshiba) and can be easily implemented by the operator. Tissue T 1 values increase slightly with B 0 field, such that optimization in pulse sequence parameters are needed to maintain comparable tissue contrast between magnetic field strengths. Intensities with values below the set threshold are " discarded" and set to zero. For well-optimized T 1 -weighted pulse sequences, this simple step can be quite robust in identifying abdominal SAT (middle) and VAT depots (right).

Single-voxel magnetic resonance spectroscopy
Single-voxel MRS has been the gold-standard for ectopic fat quantification Kim et al., 2008;Lingvay et al., 2009;van Werven et al., 2009;Weis et al., 2008). Unlike MRI, MRS usually does not provide anatomical information in the form of an image. It instead yields a precise spectrum of chemical composition within one voxel. MRS relies on chemical shift, which refers to differences in the Larmor frequencies of water and fat protons (Fig. 8). Water protons from (-OH) hydroxyl groups are characterized by a spectral peak at 4.7 ppm (parts-per-million). In contrast, the predominant protons of triglycerides are from the (-CH 2 ) n methylene groups (black H in Fig. 1d). Due to different chemical environments surrounding the protons (oxygen in water versus carbon in triglycerides), methylene protons have a slightly lower resonant frequency. The frequency separation between water and the methylene fat peak is linearly proportional to B 0 , such that larger chemical shift separations are achieved with increasing field strengths. At 1.5 and 3.0 Teslas and body temperature, the water-fat chemical shift is approximately 220 and 440 Hz, respectively (Brix et al., 1993;Cassidy et al., 2009;Ren et al., 2008). Note that this frequency difference is miniscule in comparison to the MHz Larmor values. Nevertheless, water-fat chemical shift is detectable via MRS and the resulting spectrum provides an intuitive visualization of the www.intechopen.com presence and relative quantity of chemical species. MRS packages are available on most commercial MRI scanners and typically require some operator expertise to implement. Fig. 8. Example of a spectrum from 1 H MRS containing water and fat. The water peak is at 4.7 ppm. The fat methylene peak (#5) is shifted 3.4 ppm downfield, at 1.3 ppm. While water is a symmetrical molecule and has a true single peak, fat exhibits additional minor peaks from other moieties, some of which are in close proximity to the water peak. The area under each peak represents the amount of each species present, such that a fat:water ratio (e.g. a fat fraction) can be measured. The Larmor frequency separation between water and fat can be computed by multiplying the ppm difference by the right-side of Equation 1. Color notation corresponds to Figure 1.

Frequency-selective MRI
Since MRI involves RF pulses tuned to specific resonant frequencies, a novel technique that exploits the Larmor difference between water and fat to quantify SAT, VAT, and intermuscular fat has been developed (Kaldoudi et al., 1993;Machann J et al., 2003;Peng et al., 2007, Schick et al., 2002. One approach is to selectively excite fat spins by tuning a narrow bandwidth RF pulse specifically to fat's resonant frequency. As a result, only fat protons are nutated and detected by signal receivers while those of water remain in the longitudinal plane. The encoded data and image exhibit only signals from fat and the appearance resemble T 1 -weighted contrast where fat appears recognizably bright (Fig. 9). The sharp contrast between water and fat-dominant tissues and organs readily facilitates thresholding and fat volume quantification. Selective-fat-excitation and water-suppression are terms used interchangeably to refer to this technique in literature. A disadvantage of selective-fat-excitation is that the narrow bandwidth RF pulse can only account for the methylene fat peak and minor peaks that are in its close proximity. The fat peaks near water are neglected. This issue limits the applicability of frequency-selective MRI to SAT and VAT quantification only, and not ectopic fat content. Frequency-selective techniques can be implemented on all commercial scanners. The method works well at 1.5T, but can be unreliable at higher fields due to its sensitivity to B 0 inhomogeneities. B 0 inhomogeneity refers to unknown object-dependent and spatially-varying deviations of the magnetic field from its intended value, and can cause unintentional resonant frequency drifts on the order of 10 2 -10 3 Hz. Inhomogeneities arise from manufacturing imperfections, perturbations by metals, and the mere placement of a human inside the magnet bore. They are quite significant around air-tissue-bowel interfaces. B 0 inhomogeneity, also known as off-resonance, globally shifts the ppm locations (and Larmor frequencies) of water and fat peaks such that they are no longer at 4.7 and 1.3 ppm, respectively. However, the difference between them remains unchanged at 3.4 ppm. In the presence of significant B 0 inhomogeneity, an RF pulse intended for fat's resonance can erroneously excite water's resonance. Although higher field strength leads to greater water-fat peak separation, the worsening B 0 inhomogeneity counteracts the benefit and degrades overall performance. In (a), SAT and VAT exhibit high signal intensity, in contrast to water-dominant muscles and organs. In (b), the dashed box encloses the left oblique and transversus abdominis muscles. There is appreciable signal from the muscles, in comparison to the darker right side. Due to B 0 inhomogeneity, selective-fat-excitation was ineffective in this region. In (c), performance is severely hindered in the right anterior SAT region (arrow). Significant B 0 inhomogeneity has caused the selective-fat-excitation RF pulse to suppress fat signal.

Chemical shift MRI
Chemical shift imaging (CSI) integrates sensitive water-fat spectral detection from MRS with imaging (Bley et al., 2010;Hood & Ho, 1999;Ma, 2008). CSI consists of a family of approaches that have been developed over the past 25 years. Dixon was the first to demonstrate that by controlling the echo time when data was acquired after RF excitation, the net detected MRI signal can comprise either of water and fat in-phase (IP=W+F, aligned, phase=0 degrees) or out-of-phase (OP=W-F, anti-aligned, phase=180 degrees), as illustrated in Figure 10 ( Dixon, 1984). Dixon recognized that by using this two-point (IP and OP) approach, separated water and fat images could be obtained by image algebra (Cassidy et al., 2009). By reconstructing separated water and fat images, a subsequent percent fat fraction map (fat:water ratio) can be computed, which would facilitate measurement of fat accumulation in organs on a voxel-by-voxel basis. Intuitively, one can realize that for a voxel containing only water or fat, its net signal will be the same on IP and OP acquisitions as one component's signal will be zero. In contrast, a voxel containing both water and fat will have different signals from the two acquisitions (Fig. 11). Recent developments in water-fat separation and image reconstruction algorithms have advanced the traditional two-point www.intechopen.com Dixon method beyond IP/ OP to arbitrary echo time choices (Berglund et al., 2011;Bydder et al., 2011;Eggers et al., 2011;Ma, 2008;Xiang, 2006). With the signal receivers tuned to the Larmor frequency of water, the slower precessing chemically-shifted fat signal will accrue a phase of 180 degrees, or OP, with respect to water after some time. An echo acquired at this point would reflect a net signal of W-F. As time proceeds, the signal will become IP and OP periodically. At 1.5T, the periodicity is 2.4 msec; at 3.0T, it is 1.2 msec. For this approach, only the methylene fat peak is considered. Note in (c) that both vectors are smaller in amplitude, due to T 2 relaxation. Fig. 11. Two-point Dixon MRI. In-phase (IP) and out-of-phase (OP) images from two subjects are shown. In the first subject (top row), the signal intensities within the liver between IP and OP images are similar. This signifies very little presence of hepatic fat. In the second subject (bottom row), the liver signal intensities is lower in the OP image (dashed regions). This indicates presence of hepatic fat. The difference image of the top subject is nearly zero in the liver, whereas the lower subject shows appreciable signal. As determined by MRS, the hepatic fat fractions of the subjects were 3.4% and 21.2%, respectively. CSI has evolved significantly over the past decade due to strong motivation by clinical interest and research in obesity and its comorbidities, particularly fatty liver disease (Bley et al., 2010;Cassidy et al., 2009;Ma, 2008). More robust and improved techniques have been developed to incorporate B 0 inhomogeneity estimation into the water-fat separation algorithm. These variants typically require three or more TE measurements (e.g. three-point Dixon). Modern CSI approaches have also addressed the problem of water-fat signal ambiguity (Hussain et al., 2005), which refers to the inability of early two-point methods to determine whether fat or water is the predominant specie present in a voxel. In other words, a voxel contain x% fat and (100-x)% water can be indiscernible from a voxel containing x% water and (100-x)% fat (e.g. there is ambiguity about the 50% fat fraction apex). Many recent literature reports have validated CSI in measuring liver fat fraction (Kim et al., 2008;Guiu et al., 2009;Hussain et al, 2005;Meisamy et al., 2011;O'Regan et al., 2008;Yokoo et al., 2009). While CSI pulse sequences are not difficult to implement, they often require technician involvement and manufacturer support in establishing appropriate protocols. CSI software is available on new commercial MRI platforms as clinical or research-dedicated software. Recent advances in CSI have led to the development of comprehensive methods such as IDEAL (Iterative Decomposition with Echo Asymmetry and Least squares) . IDEAL is a generalization of the multi-echo Dixon model (a three to six-point Dixon approach) and has been successfully demonstrated at both 1.5 and 3.0 Tesla. At both field strengths, IDEAL is robust to B 0 inhomogeneity, addresses water-fat signal ambiguity such that percent fat fractions can be determined across the 0-100% range without ambiguity (Bernard et al., 2008;Hines et al., 2009), accounts for T 1 and T 2 relaxation of water and fat (Bydder et al., 2008;Chebrolu et al., 2010;Liu et al., 2007), and models the multiple peaks of fat with an accurate spectrum Yu et al., 2008). The latter is a critical methodological advancement in accurate ectopic fat quantification (percent organ fat www.intechopen.com fraction) as it accounts for nearly all of the minor fat spectral peaks in addition to the primary methylene peak. A single IDEAL acquisition yields not only reconstructed fat-only and water-only images and accurate fat fraction maps [F/ (F+W)], but also IP and OP series that are useful for anatomical definition. IDEAL has been rigorously validated against MRS and other CSI methods in the liver Reeder et al., 2009;Reeder & Sirlin, 2010;Meisamy et al. 2011). IDEAL and its variants have been used to study brown and white adipose tissues  and fat accumulation in the heart . IDEAL is a patented and proprietary software that is commercially available from GE Healthcare. Figures 12-14 highlight the robustness and utility of IDEAL in visualizing and quantifying tissue fat fraction and organ fat content.   At the University of Southern California, 3D IDEAL-MRI has been developed into a routine protocol and has been used by investigators to assess abdominal subcutaneous and visceral adipose tissue depot volumes and hepatic and pancreatic fat fraction in adolescents. The center has focused on comparing and contrasting these quantitative measures between genders and ethnicities. Figure 15 showcases results from a pilot study. In summary, MR imaging and spectroscopy provide sensitive methods for detecting and quantifying adipose tissue volumes and ectopic fat fractions. Table 1 summarizes these methodologies and their principles. The quantitative fat endpoints achievable with each method are also listed. The reader is referred to three extensive review articles for additional details (Bley, 2010;Cassidy et al, 2009;Ma, 2008

Image analysis
In this section, procedures for extracting adipose tissue volumes and organ fat fractions from abdominal MRI data are discussed and illustrated using SliceOmatic software. The materials are presented in the context of abdominal imaging, but can be easily translated to other anatomies. The workflow for extracting fat measures from MRI data remains a daunting and costly task. It requires substantial data post-processing and analysis by an experienced operator. The steps involve the transfer of images to an offline workstation, followed by the use of dedicated software that typically require user involvement (Bonekamp et al., 2008). Free packages such as ImageJ, Osirix, and jMRUI (Naressi et al., 2001) and commercial programs such as SliceOmatic (Tomovision, Inc.), Analyze (AnalyzeDirect, Inc.) and Matlab (The Mathworks, Inc.) are commonly used for MRS analysis and image segmentation. Based on the author's experience, post-processing of a 3D abdominal MRI volume consisting of 60-80 slices can take approximately 45-60 minutes.

Subcutaneous and visceral adipose tissue
Since adipose tissue (SAT and VAT) is composed primarily of fat and its composition can be quite uniform, their identification and quantification in T 1 -weighted, frequency-selective, and CSI data is straightforward. Single-voxel MRS can not be used since it does not provide anatomical data. For T 1 and frequency-selective approaches, a binary threshold is applied to the signal intensity of each voxel. Since fat is brighter, those voxels with intensities greater than the threshold are labeled as fat while those with lower intensities are excluded. Due to intensity variations that may arise across the image, the local threshold may need to be adjusted accordingly from region to region (Fig. 16). For CSI, the analysis can utilize the fatonly images and apply a similar threshold procedure. Alternatively, the analysis can exploit the fat fraction map where the signal intensity range is consistently fixed from 0-100%. Only voxels with fat fractions greater than a set value (e.g. 85%) are labeled as fat (Fig. 17). Fig. 16. Segmentation of SAT (red) and VAT (green) depots. Note the evident difference in VAT quantity between the two subjects. T 1 -weighted images are shown. Arrows highlight artificial bright intensities caused by close proximity of the anatomy to receiver coils.
Regardless of the MRI technique, the operator usually needs to delineate the intraabdominal boundary that separates SAT and VAT and the perimeter of the abdomen to exclude background air during segmentation. Additional exclusions of bowel contents, inter-and intra-muscular fat, spine and vertebrae, blood vessels, and other non-relevant structures are also required since some of these can have signal intensities that mimic fat. An area or volume measure is then computed for 2D multi-slice and contiguous 3D data based on the identified fat pixels/ voxels, respectively. With the exception of CSI fat fraction data that is always normalized between 0% and 100%, the operator must determine suitable thresholds on a subject-to-subject basis. This is necessary since signal intensities vary across www.intechopen.com anatomy, subjects, examinations, and equipment and is usually not consistent. In SliceOmatic and Analyze, the operator has control over a sliding threshold and an interface that provides real time visual feedback on the performance of the chosen threshold. Fig. 17. IDEAL-based segmentation from two subjects, one with a fatty (top row) and one with a non-fatty (bottom row) liver. Water-only, fat fraction (0% black, 100% white) and overlay of segmentation (red: SAT, green: VAT, blue: liver) are shown in the left, middle, and right columns, respectively. The fuzzy noisy background in the fat fraction images has not been removed. In the fatty liver subject, note that hepatic vessels and the gall bladder (blue arrows) are well delineated in both water and fat fraction images, whereas in the nonfatty liver subject, such structures are only observable in the water image. Note that segmentation labels of the liver exclude these non-liver structures. VAT labels do not include erroneous signals from empty bowels (green arrows).

Ectopic fat
Ectopic fat quantification requires approaches that can separate water and fat signals within each voxel. Fat accumulation in organs and muscles can also be heterogenous. Therefore, the aforementioned binary threshold approach of " all fat" or " no fat" voxel classification is inadequate and only CSI methods are appropriate. Rather than measuring area or volume, a fraction of water and fat is computed. For MRS, the areas under the water and fat peaks can be calculated with software such as jMRUI. The MRS fat fraction is defined as the ratio of area under the fat peaks to the combined area of the water and fat peaks. CSI provides a fat fraction map across the imaging volume. The map is computed from the ratio of the reconstructed fat-only images to the sum of the water and fat images. To determine the fat fraction at a particular location, the operator simply draws a region-of-interest (ROI). The mean and standard deviation within the ROI is then computed. To quantify an organ, the operator must perform manual segmentation. Unlike single-voxel MRS, a volumetric fat fraction map affords immense flexibility. One can determine the average fat fraction across arbitrary ROI, including different lobes of the liver and the head and the tail of the pancreas. For accurate fat fractions, operator expertise is required for drawing ROIs and segmenting organs. In some cases, the fat fraction signal contrast surrounding abdominal organs may be poor, limiting an operator's ability to directly segment structures from the fat fraction maps www.intechopen.com and visualize organ-muscle boundaries. This is particularly true for liver, pancreas, and kidneys with low fat content that appear visually similar to surrounding muscles. In these instances, the water-only, fat-only, in-phase, and out-of-phase data are used to provide anatomical landmarks and guide the segmentation. The operator can first segment from these images and transfer the ROIs to the corresponding fat fraction data (Fig. 18). The analysis of organ fat can account for more than 70% of post-processing time. The pancreas and the kidneys are also in close proximity to fat-rich VAT depots. An inaccurately drawn ROI meant to enclose only the organs may contain erroneous VAT voxels. In such cases, the high VAT fat fractions will significantly impact the apparent measured organ fat fraction. In summary, post-processing by a trained operator is needed for computing quantitative fat endpoints. For SAT and VAT volumes, strong tissue contrast can facilitate intensity-based thresholding. The measurement of ectopic fat requires MRS and CSI. Manual segmentation demands familiarity with software and anatomy, as well as significant operator input. Fig. 18. Manual image segmentation from the full spectrum of IDEAL MRI data to delineate red: SAT, green: VAT, blue: liver, yellow: pancreas, purple: kidneys. Left to right: wateronly, fat-only, in-phase, out-of-phase, fat fraction, and segmented labels from SliceOmatic.

Discussion
An investigator has several choices in MRI methods for body and organ fat assessment. In addition to selecting the appropriate technique, one must also consider tradeoffs between spatial resolution, scan time, and the need for breath-holds to minimize subject motion (Fig.  19). For MRS, 1-8 cm 3 voxels are commonly used. MRS requires a large voxel and several signal averages to yield a clean spectrum. Prescription of MRS voxels during an examination requires operator expertise such that placement of the voxel does not contain any undesired tissues. Motion from the subject or shifting of organs due to respiration or peristalsis after voxel placement can lead to spatial misalignments and unrealistic measurements . Respiration should be monitored with a bellows transducer for data synchronization. Alternatively, multiple and consistent breath-holds can be used with clear instructions given to the patient. In practice, these MRS approaches may not be possible in subjects with a large abdominal circumference or who have difficulties with breath-holds. Resolutions for imaging approaches are on the order of millimeters. While small voxels are desirable for visualizing anatomical detail, it leads to increased scan times and lowers image signal-to-noise ratio. Long scan times also increase the risk of motion and blurring artifacts. In CSI, blurring artifacts from respiration can further cause unrealistic fat fraction distributions within organs and at organ-fat interfaces. High spatial resolution scans also necessitate breath-holding, which may not be well-tolerated by children, uncooperative or obese subjects. Spatial resolution is determined by the FOV and the acquisition matrix, the number of slices, the slice thickness, and the inter-slice gap, if any. These parameters are freely chosen by the operator, can impact scan time, and are dependent on body habitus. Fig. 19. Practical imaging parameters and considerations in MRI. In designing an appropriate protocol, one must consider the desired quantitative endpoints and leverage spatial resolution, scan time, patient safety, and the need for breath-holding. Image pair shows a slice of the liver in the same subject with (left) and without breath-holding (right).
The investigator and operator must also choose between 2D multi-slice or 3D volumetric pulse sequences. With 2D techniques, data are acquired on a slice-by-slice format such that data obtained for one slice is independent of any other slice. Subject motion and artifacts are generally not an issue in 2D methods. The time needed to acquire each slice is very short, requiring sub-seconds to a few seconds. The technique also allows the operator to specify a gap between non-contiguous adjacent slices. While this appears attractive in terms of scan efficiency, large inter-slice gaps and thick (~10 mm) slices can lead to partial volume errors and impact quantitative accuracy, especially in SAT and VAT depots. They are also difficult to generate subsequent 3D rendered visualizations (Fig. 20). Fig. 20. Volume-rendered IDEAL MRI fat series, illustrating the contiguous 3D data set. The volume consists of 36 native 5-mm axial slices, obtained with three 15-sec breath-holds.
In contrast, 3D approaches acquire data that represent the entire volume. Volumetric imaging implies contiguous coverage and no inter-slice gaps. Given similar imaging parameters and coverage, 3D protocols often contain more slices, have longer scan times, www.intechopen.com and are more susceptible to motion than 2D approaches. However, since signals arise from a larger volume rather than a smaller slice, 3D methods exhibit greater signal-to-noise ratios. Breath-holding or respiratory gating is often required for abdominal 3D sequences. Threedimensional approaches also offer intrinsic advantages over 2D methods, such as greater parallel imaging acceleration rates and more robust parallel imaging performance to reduce scan time. Parallel imaging is a topic beyond the scope of this chapter, and the reader is referred to the literature for details (Margolis et al., 2004). Other 3D benefits include thinner slices (sub-millimeter) and the ability to accommodating preparatory RF pulses that further enhance T 1 and fat to lean tissue contrast. Lastly, for a given set of spatial resolutions and imaging parameters used in abdominal imaging, scan times in T 1 -weighted, frequency-selective, MRS, and CSI approaches should be comparable between 1.5 and 3 Tesla protocols. In some instances, safety and RF tissue heating limits may lengthen 3 Tesla protocol durations over their 1.5 Tesla counterparts. At higher B 0 field strengths, greater chemical shift differences can also cause CSI sequences to increase in scan time due to shorter in-phase and out-of-phase echo periodicities. In the literature, 2-4 mm in-plane resolution and a slice thickness between 5-10 mm have been reported for SAT and VAT quantification. For 2D multi-slice approaches inter-slice gaps as wide as 10 mm have been reported. Since the determination of volumes rely on thresholding and an " all fat" or " no fat" labeling each voxel, any incorrect labeling can lead to substantial quantification errors when using large voxels. Large voxels also generate partial volume effects where many can encompass both fat and lean tissues. These voxels exhibit ambiguous signal intensities that can cause threshold errors. Similarly, low spatial resolution and thick slices containing both organ and adjacent fat can exhibit unrealistic CSI fat fractions. There has been a transition in recent studies to adopt contiguous 2D multi-slice approaches with thin slices (Siegel et al., 2007) and 3D protocols (Berglund et al., 2010;Bornert et al., 2007;. For final consideration, modern MRI platforms are equipped with receiver arrays designed specifically for abdominal imaging. The array is strapped to the subject to provide high signal-to-noise ratios. However, they also create signal intensity variations across the imaging volume. If possible, these variations should be corrected with software from MRI vendors prior to image analysis. One attractive feature of CSI fat fraction maps is that they are generated as a ratio of water and fat images. While the individual source images may exhibit signal variations, their ratio removes the bias. The use of arrays may be limited in obese subjects as they are unable to fit within the magnet bore with the receivers strapped.

Future directions and conclusion
The design of appropriate MRI protocols for fat assessment requires considerations of the desired quantitative endpoints and tradeoffs in the imaging parameters. Other practical issues include body sizes of the cohort and the cost and throughput efficiency of the study. The usage cost associated with MRI can range from $300 to $600 per hour in a typical clinical radiology department. Regardless of the study, overhead time is always required for setup and for giving instructions to the subject. T 1 -weighted and frequency-selective approaches are very fast and typically require only a few seconds per slice for data acquisition. Wholeabdomen coverage with these sequences takes only a few minutes of actual scan time and is generally considered as a first option for SAT and VAT quantification. In our experience, www.intechopen.com utilizing only these sequences, an exam can be comfortably completed in 10 minutes. If ectopic fat fractions are needed, either a MRS or CSI protocol is then added. The MRS sequence necessitates significant user interaction, preparation work, and operator time. In our experience, MRS per single voxel takes approximately 5 minutes. The further addition of a 3D whole-abdomen CSI protocol such as IDEAL will require 5-7 more breath-holds, bringing the total examination time from setup to patient removal to 30-45 minutes.  Table 2. Percent difference in segmented volumes of fat depots and organs in two scenarios, as part of routine quality control performed at our institution. In the first, a single operator segmented 11 data sets, each 4 times, in random order. In the second, two independent operators, in a blinded format, segmented 7 data sets in random order. Note that the mean differences are all less than 2%, signifying excellent segmentation consistency.

Inter-Rater Comparison (7 subjects, 2 segmentations)
Despite its rapid growth, MRI remains an untapped resource in obesity research and has not yet reached its full potential. Many opportunities remain for future investigation. Several groups have recognized the time-consuming and labor-intensive work involved with manual image segmentation. This can be daunting in longitudinal studies, as the process must be performed for every scan at multiple time points across the same subject. Efficient semi-automated and automated algorithms have been proposed, but are usually limited to in-house usage (Armao et al., 2006;Demerath et al., 2007;Kullberg et al., 2010;Peng et al., 2007;Positano et al., 2004;Wilhelm Poll et al., 2003). Techniques that can automatically segment adipose tissue and organs from a subject's MRI data at subsequent time points while utilizing a priori information from the same subject's baseline segmentations are being explored. The capability of achieving rapid intra-subject 3D registration, segmentation, and quantification will provide investigators with detailed person-specific information reflecting the temporal change in fat distribution and volumes in response to intervention. Such capability will also exploit the richness of 3D MRI that is unavailable in gapped 2D multislice data. Quality control will be essential in assuring consistency and accuracy. Fat measurements should be routinely validated against phantoms, between multiple blinded observers, and between manual and automated segmentations (Table 2). There are only a few literature reports describing the accuracy and repeatability of quantitative fat MRI.
In conclusion, MRI is the most powerful and comprehensive imaging tool for fat quantification. With CSI methods, recent studies are beginning to demonstrate their usefulness and investigators are exploring new fat biomarkers and associations in obesity www.intechopen.com with MRI that were previously untapped. This chapter has provided introductory materials for investigators to survey, understand, and integrate MRI into their studies.