Whole body magnetic resonance imaging (MRI)

10 Whole body magnetic resonance imaging (MRI) C. L. H OA D, L. M A R C I A N I, S. T. F R A N C I S and P. A. G O W L A N D, University of Nottingham, UK DOI: 10.1533/9780857097477.2.266 Abstract: This chapter gives an overview of some of the important advances made in abdominal and pelvic magnetic resonance imaging. The chapter focuses on four specific areas in the body that have seen major advances in the last ten years; the gastro-intestinal tract, liver, kidney, and foetus and placenta in the female pelvis. It describes some of the MRI techniques utilised to measure organ volume, understand organ physiology and structure in health and disease, and determine potential disease biomarkers. Key words: gastro-intestinal MRI, liver MRI, kidney MRI, foetus, placental MRI.

10.1

Introduction

The field of magnetic resonance imaging (MRI) in the body has made considerable advances during the last 10–15 years. Early research work focused on the interpretation of T1- and T2-weighted images, which look primarily for anatomical abnormalities or cancerous lesions, and much of this work is now used routinely in the clinical setting. More recently there has also been considerable interest in developing quantitative MRI techniques to provide biomarkers for disease, or look at physiological processes of the abdominal and pelvic organs. There are many factors that have contributed to quantitative body MRI becoming a rapidly-expanding area of research, and most stem from the advances made in increasing the speed at which images can be acquired – an important factor in quantitative MRI. High field strength (3.0 T and above) clinical scanners increase the signal-to-noise ratio (SNR) (Schindera et al., 2006); multi-channel phased array r.f. coils can be placed directly over the organs of interest and used in combination with parallel imaging techniques (Larkman and Nunes, 2007; Pruessmann, 2006) to reduce scan time whilst keeping the SNR of the images extremely high. The fast-scanning methods such as fast gradient echo (TurboFLASH, TFE, Fast GRE), fast spin echo (FSE, TSE) and steady-state sequences (TrueFISP, bTFE, FIESTA) are now standard on all clinical scanners and have brought scan times down from several minutes to a single breath-hold (<25 s) for some 3D gradient echo (GE) acquisitions, 266 © 2014 Woodhead Publishing Limited

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and under 1 s for many single-slice acquisitions. This is of particular importance when imaging the abdomen, as breathing, cardiac motion and gastro-intestinal (GI) peristalsis can cause motion artefacts during long scanning acquisitions which reduces image quality. New methods of data acquisition through k-space, such as radial acquisition (Altbach et al., 2002; Rasche et al., 1995), and k-space under-sampling methods such as k-t BLAST (Baltes et al., 2005; Tsao et al., 2003) have further reduced scan acquisition times for some dynamic sequences. For situations where breathholding is not appropriate (e.g. general anaesthesia, paediatrics, noncompliant patients), there are free breathing techniques such as navigator echoes (Ehman and Felmlee, 1989; Klessen et al., 2005) and post-processing methods (Brau and Brittain, 2006; Jin et al., 2009, 2011) to reduce or eliminate the problem of breathing artefacts. These technological and methodological developments mean that multiple-image data sets can now be acquired across the abdominal and pelvic organs in a very short time frame allowing for many parameters, such as tissue diffusion coefficients, NMR relaxation times, organ volumes and tissue perfusion to be measured, in clinically feasible imaging times – parameters that, historically, were predominantly measured in only brain tissue. This chapter describes some of the MRI techniques now utilised in the GI tract, liver, kidney and pregnant pelvis (foetal and placental) to determine organ volume, understand organ physiology and structure in health and disease, and determine potential disease biomarkers. Each section has its own introduction to give the reader an overview of the topics discussed for a particular organ, and further reading on all the topics is summarised at the end of the chapter.

10.2

Gastrointestinal (GI) magnetic resonance imaging (MRI)

10.2.1 Brief introduction to GI physiology, GI MRI, and motor function The anatomical regions of the gastrointestinal (GI) tract can be divided into oesophagus, stomach, small bowel, large bowel and anorectum. The different tracts of the GI system have an integrated function which has local, neural and hormonal control. The function includes complex patterns of motility (both for transport and mixing of the chyme), secretion, absorption and sensation. Despite the importance and size of these organs, advances in GI MRI were initially limited by the length of scan times and motion artifacts compared to imaging of other areas of the body. This was due to the challenging GI tract geometry, the varying distension of the lumen, and peristaltic and breathing motion. Developments in sequence design and

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parallel imaging rapidly overcame these limitations. GI MRI has come a long way since the early studies (Evans et al., 1988, 1993; Stehling et al., 1989; Schwizer et al., 1992, 1994a) and nowadays the number of successful abdominal imaging indications and protocols is expanding rapidly (Leyendecker and Brown, 2004; Semelka, 2006; Lomas, 2003). In many instances, MRI is now an accepted competitor to ultrasound and computed tomography imaging and it often complements endoscopic techniques. GI MRI benefits from the use of intravenous contrast agents to highlight the gut walls. Bowel preparation is often used and a wide range of intraluminal contrast agents are delivered orally or via catheters to distend the lumen and obtain better contrast against the walls. These include, for example, gases, baby milk, blueberry juice, polyethylene glycol, iron particles and gadolinium chelates (Giovagnoni et al., 2002). Peristalsis is often reduced by means of intravenous spasmolytic drugs. In addition to developments in ‘conventional’ GI MRI, some of the recent research focus is directed towards assessing the GI response to food and motor GI function (Marciani, 2011). The following paragraphs summarize briefly imaging advances in GI MRI for each tract of the GI system.

10.2.2 Oesophagus The assessment of adenocarcinoma represents an example of successful advances in MRI of the oesophageal tract, with the use of small FOV, thinslice, T2-weighted sequences (Riddell et al., 2007). Imaging, very frequently in ‘cine’ MRI fashion, has been recently used to investigate aspects of impaired oesophageal motor function, observing dynamic events such as from swallowing and reflux events (Barkhausen et al., 2002; KulinnaCosentini et al., 2007; Panebianco et al., 2006; Manabe et al., 2009; Curcic et al., 2010). The possibility of performing ‘endoscopic MRI’ by mounting a receiver coil on the tip of a MR-compatible endoscope has also been explored (Dave et al., 2004).

10.2.3 Stomach The use of MRI to evaluate neoplastic and non-neoplastic diseases of the gastric lumen has been increasing (Marcos and Semelka, 1999). A good distension of the lumen (usually obtained with oral water) is necessary. Visualisation of differential enhancement following intravenous contrast agents is often used. One of the more promising advances of MRI of the stomach is staging of gastric cancer. Although N-staging of it is still not very accurate (Motohara and Semelka, 2002), the data reported T-staging accuracies between 73% and 88% (Kim et al., 2000a; Sohn et al., 2000; Wang et al., 2000).

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Changes in gastric volumes in response to experimental meals are sampled in serial studies to measure gastric emptying curves (Schwizer et al., 1992; Goetze et al., 2007). Interesting comparisons with the sitting position in open design magnets were shown (Treier et al., 2006), and validation studies underpin the validity of the measurements though often carried out on limited numbers of subjects (Fruehauf et al., 2007; Fidler et al., 2009; Boulby et al., 1997; Feinle et al., 1999; Schwizer et al., 1992, 1994b). Frequent ‘cine’ MRI is used also to visualise the periodic contractions of the stomach walls (Wright et al., 1999; Marciani et al., 2001b; Schwizer et al., 1994a, 1996; Kunz et al., 1998). An example of an image of the stomach showing an antral contraction is shown in Fig. 10.1. Underpinned by initial simultaneous perfused catheter manometry studies (Wright et al., 1999), the technique is now used by various groups. The large amount of images collected with 2D (Kunz et al., 1998) or multislice 3D methods (Marciani et al., 2001b) is used to measure cross-sections of the stomach with semiautomated, in-house written software. This information is later compressed in a 2D plot from which contraction frequency, speed and % occlusion can be derived. Tagging methods have also been used to assess motility (Ajaj et al., 2004a) and the movement of digesta within the lumen (Issa et al., 1994).

10.1 True-FISP image of the stomach. The white arrow indicates a peristaltic wave after i.v. injection of metoclopramide. Image taken from Ajaj et al., 2004b.

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MRI of the stomach provides such detail of gastric geometry and motion that it is providing novel biomechanics insights in conjunction with simultaneous high-resolution manometry (Faas et al., 2001), fiber optic pressure measurements (Kwiatek et al., 2009) and computational modeling of the physics of intragastric flow (Pal et al., 2004). The high resolution available and the possibility to tune the MRI contrast to highlight different physico-chemical properties and to image selectively water or fat components have been recently exploited to advance in-body imaging of food materials and MRI of the pathophysiological response to food. For example, MRI has been used to assess the effect of meal viscosity and nutrients on intragastric dilution and gastric emptying, showing that viscous boluses are slowly diluted and suggesting that diluted, peripheral components of the digesta leave the stomach by elution (Marciani et al., 2001a). The fate of biopolymers designed to gel in the gastric lumen to increase satiety or prevent reflux has been studied (Hoad et al., 2004; Marciani et al., 2002). The intragastric distribution of fat emulsions of varying stabilities and its impact on gastric emptying, satiety and cholecystokinin duodenal response have been investigated (Marciani et al., 2003, 2007), as well as intragastric backwards and forwards flow events (Boulby et al., 1999).

10.2.4 Small bowel One of the most interesting recent advances of GI MRI is the evaluation and follow-up of inflammatory bowel disease (IBD) activity (Horsthuis et al., 2009; Laghi et al., 2003; Rieber et al., 2000a). MRI can complement traditional endoscopy by observing wall thickening, enhancement of the wall following intravenous contrast administration and stenosis. MRI compared well with other standard techniques (Low et al., 2000, 2002; Sinha et al., 2009; Rieber et al., 2000b). Early data showed that it is possible to observe small bowel motility (Stehling et al., 1989) (though it is difficult to do so when small bowel segments are not distended) and a good contrast against the walls is achieved. This can be obtained via naso-duodenal intubation for MRI enteroclysis (Umschaden et al., 2000) or by asking subjects to drink large amounts of water containing agents that avoid reabsorption of the liquid for MRI enterography (Patak et al., 2001). The oral delivery method has been used in conjunction with a single-slice gradient echo sequence to show changes in luminal diameter (Froehlich et al., 2005; Wakamiya et al., 2011) and the effect of akinetic agents (Fig. 10.2) (Froehlich et al., 2005, 2009). It has to be noted that small bowel motility was measured in selected distended locations without prior delivery of intraluminal contrast media (Patak et al., 2007). Given the geometry of the small bowel, the analysis and display of

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25 20 15 10 5 i.v. injection of 40 mg scopolamine 0 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115

Intraluminal diameter (mm)

Proximal and distal gut motility 30

2 images/sec

10.2 Small bowel motility measured from the mid-jejunum (crosses) and distal ileum (circles). The motion stops after Buscopan i.v. intervention (large arrow). Graph taken from Froehlich et al., 2005.

the results of a motility assessment can be more challenging than for the gastric antral motility. However, improved semi-automated segmentation could be useful, as shown in an animal model (Ailiani et al., 2009). Intraluminal small bowel flow has also been investigated (Gutzeit et al., 2010). The volume of freely mobile small bowel water content has also been measured, showing postprandial changes in response to feeding various foods and differences in irritable bowel syndrome with predominant diarrhoea (IBS-D) (Marciani et al., 2010). These measurements are underpinned by a naso-duodenal intubation validation study (Hoad et al., 2007). These novel MRI insights are also important to build better models of drug disintegration (Schiller et al., 2005; Sutton, 2009).

10.2.5 Colon MRI of colonic polyps has attracted considerable attention, and several studies have been dedicated to advance and evaluate such technique also with a view as a potential colonic cancer screening tool alternative to ionising examinations. Polyps are assessed mostly on fast T1-weighted sequences under a single breath-hold. Bright lumen (positive luminal fluid contrast) or dark lumen (negative luminal fluid contrast) techniques can be exploited (Debatin and Lauenstein, 2003). With bright luminal contrast, the polyps are detected as filling defects, whilst with dark luminal contrast they appear as enhancing lesions compared to pre-contrast images. The 3D data sets are often viewed in ‘virtual colonograpy’ mode (Debatin and Lauenstein, 2003). A similar negative luminal fluid contrast method in conjunction with

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intravenous contrast injection has been used in diverticulitis (Buckley et al., 2007; Heverhagen et al., 2008; Ajaj et al., 2005). Colonic inflammation is also evaluated by MRI in Crohn and ulcerative colitis (Maccioni et al., 2005; Horsthuis et al., 2009; Sinha et al., 2009). Colonic motor function was observed using MRI in early studies (Buhmann et al., 2005a, 2005b), though its measurement still remains to be developed. MRI has seen interesting developments in the measurement of colonic transit: from the enhancement of the chime on arrival of a gadolinium liquid swallow (Patak et al., 1999) to water-containing capsules (Buhmann et al., 2007; Schiller et al., 2005) and the use to image fluorine tracers, detected using 19F-MRI (Schwarz et al., 1999, 2002). The human body contains no high endogenous fluorine, hence this approach is potentially very powerful (Hahn et al., 2011) though technically challenging.

10.2.6 Anorectum Some of the first successful GI MRI indications were for the evaluation of the rectum and particularly its cancer. MRI’s exquisite softtissue contrast found numerous applications in rectal imaging. These include assessment of the integrity and atrophy of the anal sphincter and evaluation of perianal fistulas, tracks and abscesses (Beets-Tan et al., 1999). Endoanal coils improved quality of the images though they do not allow a large field of view and, for example, visualisation of the mesorectal fascia (Iafrate et al., 2006). MRI of cancer of the rectum is subject to continuous development and good accuracy for T-staging has been shown (Kim et al., 2000b). In the evaluation of anorectal function MRI, defecography has seen many indications for anorectal disorders (Mortele and Fairhurst, 2007), fully exploiting the multiplanar and dynamic ‘cine’ capabilities of MRI. Biomechanics evaluations of rectal wall strain have also been reported (Bharucha and Fletcher, 2007).

10.2.7 Future trends in GI imaging In conclusion, MRI advances have recently allowed unprecedented insights in GI physiopathology, GI motor functions, and in-body imaging of food. Future research directions will expand this body of work towards improved cancer staging, the GI response to drugs, MRI biomarkers of GI disease and disease activity, small bowel motility, colonic function (including relaxometry and spectroscopy of the chyme) and total gut transit. These advanced techniques will, however, need standardization of methods, automation of data analysis and robust validation to fully enter practice (Camilleri, 2006).

Whole body magnetic resonance imaging (MRI)

10.3

273

Liver imaging

10.3.1 Introduction to liver MRI The liver has been imaged using MRI for several decades, with much of the early work using MRI to detect cancerous lesions (Bartolozzi et al., 1999; Low, 2007; Vassiliades et al., 1991). However, with diffuse liver diseases such as non-alcoholic (NAFLD) and alcoholic fatty liver disease (ALFD) increasing in many western countries (Lazo and Clark, 2008; Williams et al., 2007), there is now considerable interest in using MRI parameters as biomarkers of diffuse liver disease. Blood supplied to the liver from the portal vein contains newly-absorbed nutrients, drugs and possibly microbes and toxins from the GI tract. The role of the liver is to absorb these into the hepatocytes and then secrete those nutrients which are needed by other cells back into the blood stream. Injury to hepatocytes from viruses, and deposition of fat and iron, can lead to tissue inflammation and fibrosis. Continued tissue injury over time can cause fibrosis to progress to cirrhosis. This can have significant complications, such as portal hypertension, ascites and hepatocellular carcinoma, which increase morbidity and mortality. The current ‘gold’ standard to determine fibrosis and cirrhosis, needle liver biopsy, is an invasive test with possible complications. It has the potential for sampling errors, as it samples only 1/50 000 of the entire liver volume (Bravo et al., 2001). However, this test can determine the amount of fat, iron, fibrosis, and inflammation in the liver tissue and any non-invasive tests must also be able to identify these key factors which influence the clinical symptoms or progression of a particular liver disease. This section examines three areas of quantitative MRI in the liver that relate to MRI biomarkers of diffuse liver disease; T2/ T2* relaxometry for measuring liver iron content, liver fat MRI, and MR elastography to determine liver fibrosis.

10.3.2 Liver relaxometry (T2/T2*) The measurement of hepatic iron concentration is of particular importance in patients with transfusion-dependent anaemias such as thalassemia and sickle cell disease, as well as genetic diseases such as haemochromatosis, where significant amounts of iron can be stored in the liver tissue leading to liver damage. MRI studies of iron in the liver have been on-going for several decades, with early reports of a reduction in the NMR relaxation time T2 for severe iron overload by Brown et al. (1985) in liver tissue samples. However, at that time, work focused on qualitatively showing iron in the liver by hypo-intense signal on T2- and T2*-weighted images compared to kidneys and spleen, as relaxation-time measurements were not

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seen as clinically relevant due to their long acquisition times and inconsistent results (Bottomley et al., 1987). More rigourous methods of relaxation-time quantification in the liver by Keevil et al. (1994) and Papakonstantinou et al. (1995) also showed a reduction in the T2 of liver tissue in iron overloaded livers. These methods used images generated from multiple echo times and fitted the resulting decaying signal from regions of interest (ROIs) in the images to models of the decay, the principles of which are also used in current T2 and T2* relaxometry methodology. The relationship between liver iron concentration and T2 and T2* is affected by the spatial distribution and type of iron particles (ferritin or hemosiderin) within the tissue (Ghugre and Wood, 2011; Jensen and Chandra, 2002). However, several experiments have shown an approximately linear relationship between R2 (1/ T2) and iron concentration (Fig. 10.3) (Clark et al., 2003b). T2 quantification There are several approaches given in the literature for the measurement of T2. T2 or R2 (1/ T2) can be measured from multiple echo times, either generated from single spin-echoes (Clark et al., 2003b; Wood et al., 2005) or

Mean transverse relaxation rate (s–1)

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Iron concentration (mg.g dry weight) –1

10.3 Plot of mean R2 vs hepatic iron concentration (HIC) from 32 pieces of tissue sectioned from a single iron-loaded human liver. HIC determined by atomic absorption spectrometry. Graph taken from Clark et al., 2003b.

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from a Carr–Purcell–Meiboom–Gill echo train (Papakonstantinou et al., 2009; Salo et al., 2002), where the T2 measured tends to be shorter due to imperfections in the 180° r.f. echo train. Data can be acquired using respiratory triggering (Papakonstantinou et al., 2009), breathholding (Wood et al., 2005, single breath per TE acquired) or data post-processing (Clark et al., 2004), to avoid motion artefacts in the images. The total acquisition time for the T2 maps is several minutes and normally covers only a few or a single slice through the liver. In the simplest mono-exponential model, T2 (R2) can then be determined from the following equation TE ⎞ S (TE ) = S0 ∗ exp ⎛⎜ − ⎝ T 2 ⎟⎠

[10.1]

where S(TE) is the signal intensity at time TE and S0 is the signal intensity at TE = 0 ms, if long repetitions are used which eliminate T1 effects on the signal decay. However, several authors have reported a bi-exponential decay in the presence of iron (Clark et al., 2003a), and hence more complex models can be used to fit the data. There are also authors who use nonexponential modelling to calculate T2 (Ghugre et al., 2005; Wu et al., 2010). Despite the modelling methods used to determine T2 (R2), generally data is fitted on a voxel-by-voxel basis and then regions of interest are drawn onto the maps to determine the mean tissue relaxation time.

T2* quantification T2* quantification of iron in the liver is perhaps more widely used now than T2 because of the shorter acquisition time and the additional advantage of simultaneous liver fat quantification (see Section 10.3.3). Multiple gradient echo sequences which allow for much shorter repetition times than spin echo sequences, can be performed in a single breathhold (Chandarana et al., 2009; Schwenzer et al., 2008). Again the simplest quantification of T2* is obtained from fitting the data to a mono-exponential decay curve (as Equation 10.1, but replacing T2 with T2*). It should be noted that the multiple gradient echo approach can easily be contaminated with T1 effects from the very short repetition times and if no fat saturation is used, the echoes will also be contaminated with effects of the fat signal coming in and out of phase with the water signal; both of which can cause errors in the quantification of T2*. These issues are dealt with in more detail in Section 10.3.3. This quantitative relaxation mapping technique is also being used to show changes in liver tissue T2* from the uptake of super paramagnetic iron oxide (SPI0) contrast agent, which is taken up predominantly in the

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Kupffer cells of the liver, reducing the T2* of the tissue. A combined example of liver fat and T2* measurements with the use of SPIO can be found in Section 10.3.3. Both T2 and T2* mapping show considerable promise as non-invasive methods for determining both the quantity and distribution of hepatic iron, either from disease (Sirlin and Reeder, 2010) or from uptake of an iron-rich contrast agent (Chung et al., 2010).

10.3.3 Assessing liver fat content using MRI Hepatic fat content in a small region of the liver can be measured accurately and non-invasively using proton magnetic resonance spectroscopy (MRS). Studies by Longo et al. (1993, 1995) and others (Szczepaniak et al., 1999; Thomsen et al., 1994) showed good correlation between biopsy data and that of MRS fat fractions. However, MRS, like liver biopsy, is limited to a small volume within the liver; careful analysis of the spectra is needed to calculate accurate fat fractions, and it has been shown recently that if the T2 decay of the liver tissue is not taken into account, inaccuracies in the calculated results may occur (Pineda et al., 2009; Sharma et al., 2009), particularly in the presence of iron in the hepatic tissue which may considerably alter the relaxation parameters from literature values. With these limitations in mind, there has been considerable recent interest in developing MRI techniques that can accurately determine liver fat fractions and show the distribution across the whole liver. Qualitative MR fat imaging The Dixon method (Dixon, 1984) of separating fat from water in MRI images was established over 25 years ago and utilises the difference in resonance frequencies of the water and fat proton signals. Acquiring images when the fat and water signal are approximately in-phase (IP) and out-ofphase (OP), normally acquired as a double echo gradient echo sequence (TEOP = 2.3 ms, TEIP = 4.6 ms at 1.5 T), allows for an approximate fat concentration or fat fraction (FF) to be calculated. However, this approach does not allow for a FF of > 50 % if the magnitude images are used for the calculation (although this amount of fat is uncommon in the liver (Szczepaniak et al., 2005)). Additional confounding factors such as T2* decay, T1 bias, and complex spectral distribution of the total fat signal have led to further advances of this simple technique which are described below. Chemical shift multi-echo imaging A more rigorous approach to creating a FF map is to use chemical-shift based fat separation techniques such as 3-point-DIXON (Glover, 1991) and

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IDEAL (Reeder et al., 2007), which use multiple echoes (>2) and allow water-only and fat-only images to be generated. These can then be recombined into a FF map (with dynamic range 0–100%). For liver imaging, a spoiled gradient multi-echo technique is generally used as this allows rapid image acquisition over a single breathhold (Reeder et al., 2007). Addressing confounding factors As stated earlier, even with a dynamic range of 0–100% for the FF, the accuracy of the FF maps may be poor if confounding factors are not addressed. To remove the T1 bias in the data, a low flip angle approach can be taken (Liu et al., 2007; Yokoo et al., 2009). Correction of T2* decay can be achieved using the multiple echo approaches of Bydder et al. (2008) and Yokoo et al. (2009), and finally the complex spectral distribution of the fat signal can be modelled (Yu et al., 2008, Reeder et al., 2009). Examples of FF maps generated with and without T2* correction are shown in Fig. 10.4 (Bydder et al., 2008), where a change to the T2* of the liver tissue was generated using a SPIO contrast agent. These advances to the simple IP/OP imaging have allowed for accurate and reproducible fat quantification of the liver to be achieved in just a single breathhold scan (Yokoo et al., 2009). However, further multi-centre studies are needed to assess the reproducibility and reliability of these imaging techniques before they can become part of routine clinical practice.

10.3.4 Measuring liver fibrosis using MR elastography Magnetic resonance elastography was put forward as a new technique by Muthupillai et al. in 1995 and uses quantitative mapping of a tissue’s physical response to harmonic mechanical excitation. This is achieved by using an external driver to oscillate the tissues of interest and acquire MRI data in the presence of magnetic field gradients which are also oscillating at the same frequency of the mechanical driver. This acquisition leads to a phase shift in the acquired MRI signal that is proportional to the scalar product of the displacement amplitude vector, the gradient vector, and the number of gradient cycles (Muthupillai et al., 1995). This means that the propagation of the shear waves generated by the driver can be seen visually on the MRI images if the phase information is displayed, and with appropriate calculations the tissue displacement can be determined; from this the tissue shear modulus and elasticity or shear stiffness can be quantified (Kruse et al., 2000; Oliphant et al., 2001, Mariappan et al., 2010). This technique has now been applied in the liver to non-invasively assess liver stiffness and determine whether this is related to the progression of liver fibrosis and cirrhosis (Huwart et al., 2006, 2008b; Rouviere et al., 2006;

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(a)

(b)

(c)

(d)

(e)

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10.4 In vivo fat fraction and T2* maps from a research subject who was scanned at five minute intervals during the infusion of a T2* contrast agent. (a) and (b) show fat fraction maps obtained with a 2-point Dixon model at the start and end of the infusion. There is a noticeable drop in the fat fraction (17.4 to 13.9 %). (c) and (d) show the same data processed with a more complex model in which the fat fraction is relatively stable (24.6 to 25.7 %). (e) and (f) show the T2* maps determined from the same model as c and d. Images taken from Bydder et al., 2008.

Yin et al., 2007). The typical experimental setup for this technique in the liver can be seen in Fig. 10.5, with the driver producing longitudinallyoriented vibrations into the liver tissue (Yin et al., 2008). The MR imaging method, a modified phase contrast sequence, can be incorporated into most imaging sequences available on standard MR systems, including spin echo (Huwart et al., 2008a), gradient echo (Rouviere et al., 2006), balanced steady state (Klatt et al., 2006) and echo planar imaging (Huwart et al., 2008a). To obtain unbiased elasticity maps, the motion-sensitising gradients are applied in three orthogonal directions and with different temporal offsets to the

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Position of the driver Passive driver

Active driver

60 Hz

Plastic tube

10.5 System for applying shear waves to the abdomen for MR elastography of the liver. Acoustic pressure waves are generated by the active audio driver, located away from the magnetic field of the MR imaging unit, and transmitted via a flexible tube to a passive pneumatic driver placed over the anterior body wall. The left diagram is a coronal illustration of the location of the passive pneumatic driver (circle) with respect to the liver. Figure taken from Yin et al., 2007.

mechanical excitation, which allows the amplitude and phase of the displacement to be determined in all directions. Elasticity or shear stiffness maps of the tissues are then generated from the displacement data using various approaches (Linear elastic: Oliphant et al., 2001; Viscoelastic: Kwon et al., 2009), and regions of interest are drawn on the maps to estimate tissue elasticity or stiffness (measured in kPa). These approaches have yielded significant results from several authors (Asbach et al., 2010; Huwart et al., 2008b; Rouviere et al., 2006; Yin et al., 2007) showing an increase in tissue stiffness with increasing liver fibrosis (Fig. 10.6). The major disadvantage to this technique is the need for additional hardware (i.e. mechanical driver) and analysis software that is not generally available for clinical use. However, this non-invasive technique has shown considerable early potential to monitor and discriminate the different stages of liver fibrosis (Bonekamp et al., 2009; Talwalkar et al., 2008).

10.3.5 Future trends in liver imaging Considerable advances have been made in using MRI to examine the liver, as described in the section above. However, there are still several emerging areas of research, which are described briefly in this section. Liver-tissue perfusion is becoming an area of interest with dynamic contrast imaging (Annet et al., 2003; Hagiwara et al., 2008; Patel et al., 2010) and arterial spin labelling (ASL) showing signs of promise for investigating the complex haemodynamic changes occurring in the liver in fibrotic and cirrhotic patients. Measurements of tissue diffusion co-efficients are also under investigation to determine whether this and related parameters can also be

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y = 3.1631 e0.2374x r2 = 0.9443

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8 7 6 5 4 3 2 1 0

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10.6 Mean liver stiffness increases with the increased fibrosis stage in patients. Shown is a summary of the mean shear stiffness measurements of the liver for 35 normal volunteers and 48 patients divided into the five different fibrosis stages which are indicated as F0, F1 … F4. Liver stiffness is significantly higher in patients than in the control group. Graph taken from Yin et al., 2007.

correlated to fibrosis, (Do et al., 2010; Lewin et al., 2007; Patel et al., 2010; Taouli et al., 2007). Finally, cardiac tagging has now been applied to the liver tissue. These applications used either respiration (Watanabe et al., 2010) or cardiac motion (Chung et al., 2011) to deform the liver tissue and determine whether this deformation changes with liver fibrosis and cirrhosis. This may become an alternative, more widely available technique compared with MR elastography in the staging of liver fibrosis, as no additional hardware is needed, although as it is currently an emerging application, considerably more development work is needed in the area. Quantitative MRI of the liver has shown significant progress over the last decade (Taouli et al., 2009) and, with further large scale research studies, many of the techniques described above have the potential to become part of routine clinical practice in the future.

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10.4

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Kidney imaging

10.4.1 Introduction to kidney physiology The primary role of the kidney is to maintain homeostasis of body fluids, filtering metabolites and minerals from the blood, and secreting them along with water in the urine. The kidney is divided into two distinct regions: an outer cortex comprising cortical nephrons and glomeruli, where blood is filtered, and an inner medulla, where loops of Henle and collecting ducts control the salt and water balance of the blood. Blood enters each kidney via the renal artery. This then separates, first into the segmental arteries, which then branch into interlobar arteries, dividing further into arcuate arteries, interlobular arteries, eventually forming afferent arterioles that deliver blood to kidney nephrons. The nephron is the functional unit of the kidney, with each kidney containing approximately one million nephrons. In the cortex, the increase in diameter of the afferent arteriole to that of the efferent arteriole that leaves the glomerulus, leads to a high pressure gradient which filters small molecules such as water, glucose, and urea out of the plasma into the Bowman’s capsule. The kidney tubules in the medulla selectively reabsorb water and small molecules from the filtrate into the blood, and secrete wastes from the blood into the urine. The reference standard clinical assessment of renal function is ascertained by calculating the glomerular filtration rate (GFR) using serum creatinine concentration. However, this is a late marker of renal dysfunction and cannot be used to assess single kidney function (Prigent, 2008). MRI provides the ability to assess both the structure and function of the kidney. This section will outline applications of MRI in the kidney to assess physiology; BOLD MRI for the assessment of oxygenation, blood flow in the kidney using phase contrast MRI, dynamic contrast enhanced MRI and Arterial Spin Labelling, and the application of diffusion weighted MRI to study molecular motion in the kidney.

10.4.2 Blood oxygenation level dependent (BOLD) MRI Blood flow to the renal cortex normally supplies oxygen far in excess of its metabolic needs. In contrast, blood flow to the renal medulla is low and it is poorly oxygenated due to considerable diffusion of oxygen from the arterial to the venous side, and the high oxygen demand required to generate an osmotic gradient to actively reabsorb sodium. Thus, the renal medulla functions in a hypoxic state (Brezis et al., 1984; Brezis and Rosen, 1995). Renal BOLD (Blood Oxygenation Level Dependent) imaging can be used to assess the heterogeneity of oxygenation in the kidney (Li et al., 2008), particularly the medulla.

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BOLD imaging is most widely associated with its application to functional MRI of the brain (Ogawa et al., 1993), but provides a non-invasive method to assess oxygenation of an organ. BOLD uses the dependence of the magnetic properties of haemoglobin on oxygenation state, deoxygenated blood being paramagnetic leading to shortening of the T2* relaxation time compared to diamagnetic oxygenated blood (Pauling and Coryell, 1936). The rate of spin dephasing, R2* = 1/T2*, therefore provides an index of tissue oxygenation. A multiple gradient-recalled echo (GRE) sequence is most widely used to assess renal BOLD (Prasad et al., 1997; Tumkur et al., 2006). Using this sequence, a number of echoes are collected during each breathhold, with the maximum TE being equal to the T2* of the medulla (∼ 50 ms at 1.5 T and 25 ms at 3 T). The scan is acquired over a number of breathholds to collect the entire of k-space. R2* can then be obtained from the line fit of the natural log of signal versus TE, and an R2* map formed (Prasad et al., 1996; Tumkur et al., 2006), (Fig. 10.7). BOLD MRI has been widely used for pharmacological/physiological challenges, such as the administration of furosemide (Li et al., 2004), and to study water loading which increases medullary oxygenation and lowers R2*

10.7 Top: Anatomical images acquired with the 3-D mGRE sequence. Bottom: The corresponding R2* maps. The images in the right column are same as the left column, but with some representative ROIs in the cortex marked. Image taken from Tumkur et al., 2006.

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(Tumkur et al., 2006; Li et al., 2004) (Fig. 10.8). Water loading has been shown to differentiate responses in diabetics compared to healthy young subjects (Epstein et al., 2002), with no significant increase in the oxygenation of the renal medulla in diabetic patients. This has been suggested to result from the deficiency of patients with mild diabetes to synthesize vasodilators. Renal BOLD can also detect the presence of renal hypoxia induced by renal artery stenosis (RAS) (Juillard et al., 2004), with an increase in R2* (a)

R2* (1/s)

R2* (1/s)

40 Water loading Medulla 37 Cortex 34 31 28 25 22 19 16 13 10 –4 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 Time (min) (b) 40 Medulla (subject 1) 37 Medulla (subject 2) 34 Cortex (subject 1) 31 Cortex (subject 2) 28 25 22 19 16 13 10 –4 4 12 20 28 36 44 52 60 68 76 84 92 100 108 116 124 Time (min)

10.8 Time course of response to waterload. (a) Time course of response shown for one representative subject. The first three time points represent baseline measurements. For this subject, the temporal response is evident within 30 min post-waterload. The error bars represent the standard deviation of the pixel data for all of the ROIs used to determine a single time point. (b) Combined plots from subjects No. 1 and No. 2. Note, for subject No. 2, the temporal response is slower, requiring approximately 50 min to reach equilibrium value. In both cases, the R2* values of the medulla begin to approach the values found in the cortex. Graphs taken from Tumkur et al., 2006.

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in conjunction with a decrease in renal blood flow. Following renal transplantation, BOLD has been shown to identify those kidneys with acute rejection from a measured decrease in medulla R2* (Sadowski et al., 2010). However, BOLD imaging comes with the caveat that it is an indirect marker of renal oxygenation, resulting from the complex interplay of blood flow, blood volume, and oxygen consumption (Buxton et al., 1998). Further, a number of other factors can alter R2* such as magnetic field homogeneity over the kidney, vessel geometry and the pulse sequence used.

10.4.3 Blood flow in the kidney: phase contrast MRI, dynamic contrast enhanced imaging and arterial spin labelling Methods to assess blood flow of an organ can be divided into the assessment of the total blood flow to the organ (in cm/s or mL/min), or measurements at the capillary level to assess organ perfusion (in mL/100 g/min). Bulk flow through vessels supplying an organ can be assessed using phase contrast MRI (PC-MRI) (Schoenberg et al., 1997; Debatin et al., 1994). In PC-MRI, velocity-encoding (VENC) gradients are added to the imaging sequence, resulting in a phase shift proportional to the velocity of the blood along the gradient’s direction in the phase image. Typically a VENC of 100 to 150 cm/s is used for the renal arteries (Jin et al., 2009). The data acquisition is synchronized to the cardiac cycle to allow the volume of blood flow in the renal artery per cardiac cycle to be measured. Having calculated the renal artery blood flow (mL/min), mean kidney perfusion (in mL/100 g/min) can be obtained by dividing by each kidney’s volume and multiplying the result by 100. Renal perfusion controls the delivery of nutrients to the tissue. It can be assessed on a voxel-by-voxel basis with MRI using either dynamic contrastenhanced MRI (DCE-MRI), which uses the administration of an exogenous contrast agent, or using arterial spin labelling (ASL), which uses endogenous water to provide contrast. DCE-MRI monitors the signal change as a function of time as the administered contrast agent passes through the capillaries, is filtered in the glomeruli, and then passes through the renal tubules or the renal veins (Grenier et al., 2003). Most DCE-MRI studies use the rapid acquisition of heavily T1-weighted 2D or 3D pulse sequences, following the injection of paramagnetic contrast agent (such as Gd-DTPA) (Hackstein et al., 2005). From the signal intensity curve following injection, the renal first-pass perfusion (blood flow, blood volume, mean transit time) and glomeruli filtration rate can be assessed (Sourbron et al., 2008). Various tracer-kinetic models have been proposed for DCE-MRI of the kidney (Buckley et al.,

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2006; Bokacheva et al., 2009). However, in the case of advanced renal insufficiency, the administration of contrast agents should be avoided due to the risk of nephrogenic systemic fibrosis (NSF) (Prince et al., 2009). This has led to the recent development of non-invasive perfusion methods. Arterial spin labelling (ASL) is an attractive alternative to DCE-MRI techniques for the non-invasive quantitative assessment of tissue perfusion. ASL does not require the need for contrast agent, but instead uses the magnetization of endogenous labelled blood as an internal contrast agent (Kim, 1995; Kwong et al., 1995). To accomplish this, the longitudinal magnetization of arterial blood water must be manipulated so that it differs from the tissue magnetization. Most renal ASL studies have used the pulsed flow-sensitive alternating inversion recovery (FAIR) sequence ASL scheme (Boss et al., 2005; Gardener and Francis, 2010; Martirosian et al., 2004) in which two image types are collected: a control image without labelling the arterial blood and a tag image in which inflowing blood is inverted. The subtle difference between images acquired with and without labelling is the perfusion weighted (PW) image. Due to the low SNR of ASL, multiple averages of the perfusion weighted images are needed. The PW signal can then be modelled to derive a calculated blood-flow image showing perfusion in mL/100 g/min at each voxel. For ASL quantification, knowledge or assumptions about the T1 values of blood and tissue, the labelling efficiency, and the arterial transit time are required (Buxton et al., 1998). Measured perfusion rates of the renal cortex are in the order of 300 mL/100 g/min, compared with approximately 100 mL/100 g/min in the medulla (Martirosian et al., 2004; Fenchel et al., 2006; Roberts et al., 1995). The use of gradient echo echo-planar imaging (GE-EPI), as often used for ASL image readout in the brain, is limited in the body due to the high susceptibility to magnetic field inhomogeneities. Spin echo based EPI techniques (Gardener and Francis, 2010) and non-EPI acquisitions such as True-FISP (Martirosian et al., 2004) or Fast Spin Echo (FSE) acquisitions (Robson et al., 2008) have been shown to provide high SNR for renal ASL without image distortions. With recent advances in parallel imaging, ASL methods have been used for multi-slice acquisitions (Gardener and Francis, 2010). Since ASL is a subtraction technique, a major challenge to abdominal ASL applications is movement arising from respiratory-related motion, leading to blurring or marked movement of the kidney between each image acquisition. Acquisition schemes have been used to reduce blurring and shot-to-shot variability, such as breath-hold, respiratory triggering (Robson et al., 2008; Gardener and Francis, 2010), navigator-echo methods (Song et al., 2008) and background-suppressed ASL data acquisition (Garcia et al., 2005). Alternatively, post-processing approaches have been followed,

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including retrospective sorting and discarding of images, or the use of realignment algorithms to correct respiratory-induced motion (Gardener and Francis, 2010) (Fig. 10.9). ASL provides a non-contrast technique to non-invasively assess the diagnosis of vascular diseases, kidney transplantation (Artz et al., 2011), and to study drug interventions and challenges where repeated measures are required.

10.4.4 Applications of diffusion weighted imaging in the kidney Diffusion weighted imaging (DWI) contrast (Binser et al., 2010) originates from the thermally-induced Brownian motion of water molecules. DWI collects at least two single-shot EPI images acquired with and without diffusion weighting gradients from which molecular diffusion, expressed as the apparent diffusion coefficient (ADC) in mm2/s, can be quantified. Often (a)

(i)

(ii)

(b)

(c)

(d)

10.9 True-FISP images. (a) Single-slice data acquired with no SENSE-free breathing (351 ms acquisition time): (i) control image, (ii) average ΔM image. Multislice SENSE 2 and half-scan image acquisition (1043-ms acquisition time for seven slices). (b) Control images for free-breathing no background suppression (BS) (images cropped for improved visualization of kidney). (c) Average DM image acquired for free-breathing no BS. (d) Spin echo average ΔM image acquired for free breathing with no BS. Image taken from Gardener and Francis, 2010.

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a range of b-values spanning up to 1000 s/mm are collected (Zhang et al., 2012; Thoeny et al., 2005) with high b-values reflecting solely diffusion effects (ADChigh), and low b-values being influenced by both diffusion and perfusion effects (ADClow). The ADC of the kidneys is high compared with other body organs, due to the high water content and high blood flow of the kidneys (ADClow values of (3.50 ± 0.47) × 10−3 mm2/s and (3.71 ± 0.43) × 10−3 mm2/s, ADChigh values of (1.67 ± 0.11) × 10−3 mm2/s and (1.54 ± 0.09) × 10−3 mm2/s for the cortex and medulla (Thoeny et al., 2005)). As with ASL, ADC measures are sensitive to respiratory motion artifacts, and so respiratory–triggered DWI (Binser et al., 2010) or realignment algorithms (Zollner et al., 2009) are often used. Renal ADC measures have been shown to be sensitive to renal ischaemia (Liu and Xie, 2003) and disease, with both the cortex and medulla of patients with acute and chronic renal failure showing reduced ADC (Thoeny et al., 2005; Namimoto et al., 1999). ADC mapping has also shown clear differences between renal lesions and normal tissue (Cova et al., 2004) and provided a non-invasive method to assess acute and chronic graft rejection after kidney transplantation (Thoeny et al., 2006). Diffusion tensor imaging (DTI) adds further information on renal microstructure by providing directionality to DWI measures. Typically, kidney DTI measurements are performed along six axes from which the degree of anisotropy can be measured as indexed by fractional anisotropy (FA) value. The FA of the medulla has been shown to be higher than that of the cortex, reflecting either the tubular structure or tubular flow (Notohamiprodjo et al., 2008; Ries et al., 2001), and is being applied clinically in the evaluation of transplanted kidneys.

10.4.5 Future trends in kidney imaging Renal MRI is a growing field of interest which has great potential, providing a non-invasive method with exquisite anatomic detail, and the unique ability to follow spatial and temporal responses to interventions. The techniques of BOLD, perfusion and diffusion of the kidney outlined in this chapter have shown considerable promise in providing not only morphological information, but also the non-invasive understanding of the renal pathophysiology. Since BOLD, ASL, and DWI do not require administration of exogenous contrast agents, they can be applied in patients with diminished renal function and can be used in longitudinal studies evaluating physiological or pharmacological manoeuvres. However, there is still a lack in standardization of these techniques, and so these methods need to be explored on a larger scale for broad clinical application. Other novel MRI techniques with potential in the kidney include pH measurements, using 31P or 1H resonances (van Sluis et al., 1999; Mori

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et al., 1998) or Gd complexes (Raghunand et al., 2003), and more recently sodium (Na) MRI (Maril et al., 2004, 2005). Pathologically altered renal physiology can manifest as perturbations in renal pH, and MR methods to image the spatial distribution of tissue pH would aid in the clinical assessment of disease extent, progression, and response to therapy. Na MRI provides a non-invasive measure of tissue sodium concentration, potentially allowing the urinary concentrating process to be followed directly. However, Na MRI is non-standard with lower signal-to-noise ratio than proton MRI, and necessitates custom RF coils. Through the development of MRI techniques, MRI will play an increasing role in the management of patients with renal disease and in the preoperative work-up of potential kidney donors, and it may lead to the identification of biomarkers to evaluate novel therapeutic and preventive manoeuvres.

10.5

Foetal and placental imaging

10.5.1 Introduction to foetal and placental imaging MRI was relatively late in entering the clinic for obstetrics compared to other areas of medicine. The main reason for this was because of the problems with unpredictable foetal motion. However, over the last two decades, the increasing availability of robust snap shot imaging techniques, particularly HASTE (Levine, 2001), has allowed MRI to enter the clinic as a tool for investigating foetuses who have been picked up as being at risk of having a congenital abnormality on ultrasound or from other investigations. However, MRI is also being increasingly used to understand the causes of compromised foetal growth and development. The following sub-sections will overview the developments in both placental and foetal MRI, as well as briefly discuss the safety issues surrounding MRI scanning in pregnancy.

10.5.2 Placental development and function The placenta is an organ in which exchange of nutrients, oxygen and waste products occurs between the mother and foetus, but without exchange of blood. The foetal blood is constrained to branching villi which are bathed in an extravascular pool of maternal blood contained within the intravillous spaces. The maternal blood is supplied from spiral arteries within the uterine wall, which are modified by the middle of the second trimester to become low-resistance vessels to provide a high blood flow to the placenta. The placenta continues to grow and develop during gestation to meet the changing requirements of the foetus. An increasing clinical indication for using MRI is to manage abnormalities of placental implantation, such as placenta previa and placenta acretia (Masselli et al., 2011; Pri-Paz et al.,

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2011). However, MRI can offer many methods of monitoring placental growth and development, particularly since, although the placenta is affected by maternal breathing, it is less affected by fetal motion, so parameters of interest in it can be measured using appropriately prepared EPI sequences or gradient echo sequences. Relaxation times and magnetization transfer rates relate to macromolecule concentration and water binding, and hence might be expected to correlate with changing villous density. The relaxation times have been shown both to decrease during gestation, consistent with ongoing villous development, and to be reduced in compromised pregnancies (Gowland et al., 1998b; Duncan et al., 1998). Magnetization transfer rates have been shown to correlate with placental histology (Ong et al., 2004). However, probably more importantly, MRI can be used to measure placental blood flow in several ways. MR contrast agents can show flow patterns very well in the placenta, but it remains to be seen whether MR contrast agents are safe for use in pregnancy since it is known that they cross the placenta and enter the amniotic fluid (Marcos et al., 1997). However, arterial spin labelling and intravoxel incoherent motion (modelling random motion of blood in the intravillous spaces as a diffusion process) can both be used to measure placental blood flow and have been used to show changes in maternal blood flow in pregnancies compromised by pre-eclampsia and intrauterine growth restriction (Moore et al., 2000a, 2000b; Francis et al., 1998; Gowland et al., 1998a). The Blood Oxygen Level Dependent signal modulation by inhaled oxygen has also been used to measure placental blood flow and has been shown to change with gestational age (Chalouhi et al., 2011; Morris et al., 2010).

10.5.3 Foetal growth and development Foetal growth is the primary method of assessment of foetal well-being and is an important determinant of health in later life (Barker et al., 1990). MRI can be used for morphometry of the foetus and the foetal organs (Duncan et al., 2005), including cortical folding. This has been applied to study foetuses affected by intrauterine growth restriction and pre-eclampsia (a condition related to poor placental implantation), and in other conditions that may affect differential growth of the foetal organs, such as diaphragmatic hernia (Kilian et al., 2009). MRI is arguably more time-consuming than ultrasound if you include both scanning and analysis time (and depending on how US data is acquired and analyzed), but the actual time required to acquire a MRI morphometry data set from a mother is only a few minutes. MRI is less affected by bowel gas, oligohydramnios and maternal obesity than ultrasound, and is less affected by signal drop-off away from the surface of the body, particularly at later gestation. However foetal motion can be a problem, even with multislice snap-shot imaging sequences; there

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may be no motion artefacts in individual images but motion between slices can affect volume measurements. Recently, new oversampling and image registration techniques have been introduced to overcome this problem (Studholme, 2011). MRI is a very versatile imaging technique and so can also be tuned to study the development of foetal organs by making use of changes in relaxation times, diffusion weighted imaging or magnetization transfer. Diffusion weighted imaging has been used to study lung development from solid organ to an organ filled with spaces (Moore et al., 2001), and signal intensities have been used to study changes in lung development in diaphragmatic hernia (Nishie et al., 2009). T2* weighted images have been used to study iron storage in the liver (Cassart et al., 2011; Duncan et al., 1997). T1 weighted images have been used to measure fat deposition in the foetus. The gall bladder has been shown to change characteristics on T1 and T2 weighted images during development (Brugger et al., 2010). Myelination has been measured by changes in diffusion restriction (Huang et al., 2009) and T1 (Almajeed et al., 2004).

10.5.4 Safety A second reason for the slow uptake of MRI in pregnancy is inevitable concerns over safety. The real risks for the fetus in an MR scanner relate to r.f. heating and acoustic noise, and these issues must be considered when selecting pulse sequences and designing scanning protocols (de Wilde et al., 2005). Various national guidelines exist related to this matter, for instance (HPA, 2008).

10.5.5 Future trends in foetal and placental imaging The number of publications related to foetal and placental MRI has increased rapidly in the last couple of years so that the strongest trend over the next few years is likely to be the establishment of MRI as a routine tool within the obstetrics clinic. However MRI will also have an important role to play in managing pregnancies in which foetal growth and development is compromised.

10.6

Conclusion

This chapter has presented a variety of applications for MRI in the abdomen and pelvis. Development of fast, quantitative imaging sequences and higher field strength scanners have seen a rapid expansion in the use of MRI for imaging the body’s organs, allowing the focus to shift towards physiological function and disease biomarkers. While this chapter focused on four specific

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areas, the GI tract, liver, kidney and pregnant pelvis, developments in body imaging have not been limited to only these organs. A wide variety of techniques and applications have been described which are applicable across the body, including organ volume measures, and assessment of blood flow and tissue perfusion, relaxometry and diffusion. The use of MRI in biomedical imaging of the body has great potential to continue expanding its applications to further exploit the new and emerging technological developments in both scanner hardware and software.

10.7

Sources of further information and advice

For gastro-intestinal applications of MRI, suggested additional reading is Marciani et al. (2012) and Spiller et al. (2009). For liver imaging application of quantitative MRI, suggested additional reading is Maniam S and Szklaruk J (2010), Reeder and Sirlin (2010), Sijens (2009) and Sirlin and Reeder (2010). For kidney imaging applications, suggested additional reading is Chandarana and Lee (2009), Nikken and Krestin (2007) and Notohamiprodjo et al. (2010). For placental and foetal imaging, suggested additional reading is Prayer (2011).

10.8

References

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