Chapter 5

Fat Segmentation in Magnetic

Resonance Images

David P. Costello and Patrick A. Kenny

5.1 Introduction

Over the past two decades, many authors have investigated the use of magnetic resonance imaging (MRI) for the analysis of body fat and body fat distribution. However, accurate isolation of fat in MR images is an arduous task when performed manually. In order to alleviate this burden, numerous automated and semi-automated segmentation algorithms have been developed for the quantification of fat in MR images. This chapter will discuss some of the techniques and models used in these algorithms, with a particular emphasis on their application and implementation.

When segmenting MR images the largest variable in the process is the image itself. What acquisition parameters give optimal image quality, in terms of signal to noise ratio (SNR), contrast, uniformity, and boundary definition? An appropriate MRI pulse sequence aims to generate adequate image contrast in the shortest imaging time. MRI can also introduce measurement errors and image artifacts as a result of the imaging process, which will complicate the segmentation process. The potential impact of artifacts such as intensity inhomogeneities and partial volume effect (PVE) on image segmentation will be discussed briefly.

Body fat volume and fat distribution provide key risk indicators for a number of diseases including non-insulin-dependent diabetes mellitus (NIDDM) and coronary heart disease (CHD) [1]. Traditionally, anthropometric measurements such as body mass index (BMI), waist to hip ratio, abdominal circumference, and caliper tests have been used to estimate total body fat and body fat distribution [2–4]. These methods indirectly infer an estimate of body fat based on a known correlation

D.P. Costello ( )

Mater Misericordiae University Hospital and University College, Dublin, Ireland e-mail: [e-mail]

G. Dougherty (ed.), Medical Image Processing: Techniques and Applications, Biological

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and Medical Physics, Biomedical Engineering, DOI 10.1007/978-1-4419-9779-1 5, © Springer Science+Business Media, LLC 2011

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with underwater weighing [3]. Obesity is defined in terms of BMI [5], which is expressed as:

BMI =	Weight (kg)	.	(5.1)
	Height2 (m2)

However, BMI is a metric that fails to distinguish fat mass from lean mass. This renders BMI ineffective as a metric for assessing sample groups such as athletes, who because of their increased muscle mass can be categorized as overweight or obese even if their percentage body fat is below normal.

Body fat distribution can be analysed using volumetric images of the body. Both MRI and computed tomography (CT) are sophisticated volumetric imaging techniques that facilitate delineation of regional fat deposits in the body [6]. It is difficult to justify whole body CT for fat quantification because of its high radiation dose, especially when MRI exists as a non-ionising alternative [7]. Seidell et al. [8] show a strong correlation between fat volumes measured using both modalities. As a result of this, radiation dose remains the main reasons for selecting MRI over CT to analyse body fat.

5.2 Imaging Body Fat

Image acquisition is the most important step when quantifying fat with MRI. Good contrast, resolution, SNR and homogeneity are required for accurate image segmentation. Contrast in MR images depends on proton density, longitudinal relaxation time (T 1) and transverse relaxation time (T 2). As a result of the significant difference in T1 between fat and soft tissue, T1-weighted (T1w) MR sequences are used to enhance fat contrast. Numerous MRI pulse sequences are used to generate high contrast images for fat analysis, including: spin echo (SE), gradient echo (GE), Inversion recovery, three-Point Dixon method and water saturated (WS) balanced steady-state free precession (WS b-SSFP or TrueFISP) pulse sequences fat [6,8–17].

Both SE and GE sequences are used to image body fat [9–15]. GE sequences are faster than SE but provide decreased homogeneity, lower SNR and reduced T1 contrast. However, in some circumstances short scan times are necessary to reduce motion artifact. Fast-SE1 (FSE) sequences reduce imaging time while maintaining homogeneity. Barnard et al. [9], compared fast and conventional spin echo pulse sequences and found good correlation between the two. However, when compared to GE sequences, FSE sequences are relatively slow for whole body imaging [18]. Brennan et al. [15], successfully used a T1w GE pulse sequence to demonstrate the relationship between whole body fat and BMI.

A number of authors have used advanced pulse sequences to improve contrast between fat and soft tissue. One variation on Gradient echo pulse sequence is a b-SSFP sequence. Both WS [19] and non-WS b-SSFP [6] pulse sequences have

1Fast spin echo techniques acquire between 2 and 16 lines of k-space during each TR.

5 Fat Segmentation in Magnetic Resonance Images

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Fig. 5.1 (a) Whole body MR scan acquired in 6 stations; (b) the stitched image after post processing and (c) stitched image with no post processing

been used to image body fat. Peng et al. [19] proposed using a WS b-SSFP pulse sequence for fat quantification. WS b-SSFP [16, 19] allows for rapid acquisition of high contrast images containing fat and soft tissue. Peng et al. [19] found that this sequence performed better than a WS Fast-SE sequence and simplifies the segmentation process (see Sect. 5.4.2) [16].

Once a pulse sequence is selected, sequence parameters (e.g. repetition time (TR), echo time (TE), number of signal averages (NSA) and echo train length) should be set to optimize both acquisition time and image contrast. Acquisition time is a key factor when selecting a pulse sequence, as long scan times increases the likelihood of motion artifacts. The presence of motion artifact in the visceral cavity can make it very difficult to quantify fat. Scan time can be reduced by shortening acquisition time, reducing the NSA and increasing the echo train length. Changing these factors can also lead to a decrease in image contrast and SNR, which should be investigated prior to scanning large cohorts of patients.

There are a number of other imaging parameters that must also be considered. These include field of view (FOV), scan orientation, matrix size, slice thickness, and slice gap. FOV size affects the severity of inhomogeneities across the image. A larger FOV results in a less homogeneous B–field (magnetic–field) across the image. Homogeneity is particularly important when acquiring coronal whole body data sets. Consequently, it is normal practice to split a whole body scan into a number of smaller sections, known as stations, to reduce inhomogeneities caused by

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Fig. 5.2 (a) Intensity corrected whole body image and (b) whole body image with no intensity correction

variation in the B–field. The resultant sections are then stitched together to form a whole body data set as illustrated in Fig. 5.1 and 5.2. Figure 5.1c and 5.2b illustrate the intensity differences that can occur between stations in the same slice.

Intensity differences between stations for tissues of the same class can hamper image segmentation. Brennan et al. [15] describe a method of histogram matching to compensate for intensity differences between each station. The first step is to identify the soft tissue peak in the histogram of each station. After which, all soft tissue peaks are aligning to match the gray-scale distribution across all stations. Images are then stitched according to the coordinates in the DICOM header information, illustrated in Fig. 5.1b. Transverse MR images of the body are often used in the literature to image body fat. Opting to use transverse orientation over coronal has a number of advantages. Transverse images allow the use of a smaller