2.3.1 MRI safety for the fetus and pregnant patient
The reluctance to using MRI on pregnant patients is linked to the lack of confirming research in the area. It was initially thought that several mechanisms could potentially harm the fetus (10). MRI related heating is the main concern (particularly in the first trimester), and the rapidly switching gradient field’s acoustic noise is a concern during the second and third trimester. Tissue heating is greatest at the body’s surface, and since the fetus is at the body’s centre, the heating is not of great concern when scanning within SAR limits. The gradients can create 80-120 dB, and 90 dB is the acceptable upper limit before permanent fetal ear damage can be caused. However, the mother’s body reduces acoustic noise by at least 30 dB, and therefore acoustic noise is not a concern while performing fetal MRI. During the first trimester theoretical risks include disruption of cells undergoing implanting, and rapid dividing and organogenesis due to MRI related heating. Heating can be reduced by limiting specific absorption rate (SAR), which can be done by using low flip angles, large RF spacing, long repetition time (TR) and low B0
(32). Also, recent research shows that exposure to MRI compared to no exposure, during the first trimester is not associated with increased risk to the fetus or early childhood (11).
Today it is still recommended that each examination should be reviewed on a case-by-case basis, to assess the relative risk versus the benefit of the examination, or if the examination can be delayed until after or later in the pregnancy (10). Additionally, the risk must be weighed against alternative diagnostic tests that may involve ionization (33). Furthermore, the use of Gd should be avoided if possible due to the risks mentioned in the introduction (11).
2.3.2 Balanced Fast Field Echo
The balanced fast field echo (bFFE) sequence is well suited for placental imaging, because it results in fewer motion–related artefacts than other sequences, due to its relative resistance to maternal and fetal motion. It also provides reasonable
differentiation between the placenta and underlying myometrium. The sequence is often combined with parallel imaging that reduces acquisition time, increases sharpness and reduces SAR (5). The bFFE is a gradient echo (GRE) MRI sequence with balanced rewinding gradients in three directions (33). The sequence is termed balanced because the net gradient induced dephasing during one TR is zero. This means that both the free induction decay (FID) and echo components are refocused at the centre of the TR
interval resulting in a signal (Figure 4). The figure shows that the gradients are balanced on both sides of the signal (34). Because the balanced gradients maintain both
transverse and longitudinal magnetization, the resulting image contains both T1 and T2 contrast. Therefore the images have increased signal from fluid, but at the same time contain T1 weighted tissue contrast (35).
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The bFFE sequence requires high-performance gradient systems to obtain the needed short TE and good shimming (33). The bFFE sequence goes by several names, depending on the manufacturer: True FISP (Siemens), FIESTA (GE), BASG (Hitachi), True SSFP (Toshiba) and bFFE (Philips). The generic name of the sequence is coherent GRE with balanced “FID/echo” refocusing (36).
2.3.3 Diffusion, perfusion and MRI
Molecules in gasses and fluids have random translational motion. They will randomly collide and therefore continually change direction. This phenomenon is called diffusion (1), also known as Brownian motion (37). The continuous collisions and change of direction are described as a random walk (38).
Diffusion is an essential transportation method in the human body, allowing substances such as gases, nutrients and waste products to be effectively transported from one area to another. The net effect of diffusion is transport of substances from an area with high concentration to an area with low concentration, in order to even out the concentration differences, which happens via capillaries. How quickly a substance can be transported from the capillary to the surrounding area depends on the difference in concentration and the capillary wall’s permeability for a certain substance. Additionally, gases diffuse from areas with higher partial pressure to areas with lower partial pressure. An example of this can be seen in tissue fluid, where the partial pressure of oxygen is lower than within the capillaries. The cells within the tissue fluid have an even lower partial pressure. Therefore oxygen will diffuse from the blood within the capillaries, via the tissue fluid, to the cells (1).
Figure 4: bFFE sequence diagram showing that all gradients are balanced over one TR period, and the echo is formed in the middle of the sequence. The various parts of the diagram are:
=flip angle, RF is the radiofrequency pulse, Gss is the slice-selective gradient, GPE is the phase encoding gradient, and GFE is the frequency-encoding gradient (33).
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Several substances are transported via diffusion, but when using MRI to detect the diffusion within the body it is the diffusion of water molecules that is studied (39). Since water accounts for 70% of body weight (1), there is a lot of hydrogen available for diffusion MRI. One typically investigates the self-diffusion of water molecules in tissue fluid. When water molecules interact with cell membranes through diffusion, MRI can be used to provide information about the functional architecture of tissues (39).
DWI is an MRI technique that allows us to evaluate several aspects related to the motional properties within a tissue. In the 1990s the technique was primarily used for brain imaging, but it has in later years been applied to the rest of the body, since technological innovations now enable it (14).
To obtain DWI, a spin-echo (SE) echo-planar imaging (EPI) sequence was used as an MR-signal readout sequence, due to its rapid acquisition time that minimizes the chances of motion-induced artefacts (33). When using SE over GRE, phase shifts resulting from magnetic field inhomogeneities, static tissue susceptibility gradients and chemical shifts are cancelled by SE’s refocusing pulse. This is not the case for the refocusing gradient.
Furthermore, image contrast in SE allows for true T2 relaxation rather than T2*. All of these factors lead to SE being less frequently troubled by susceptibility and chemical shift artefacts, but they do however take longer than GRE (40). EPI’s short acquisition time is a result of several k-space lines being filled during a single TR. In blipped EPI, the
frequency-encoding gradients oscillate rapidly from positive to negative to form a train of gradient echoes, and each echo is phase encoded differently by phase encoding blips (41). The EPI sequence is suitable for fetal imaging because it reduces motional blurring as a result of fetal movement (42).
The pulsed gradient spin echo (PGSE) method is one of the most commonly used
methods for obtaining diffusion-weighted contrast. The sequence consists of a 90°-180°
spin echo pair of RF pulses, where two equal gradients are placed on either side of the 180° pulse (Figure 5). This will precede the EPI acquisition. To control the degree of motion sensitivity in the image we can manipulate the diffusion-weighting gradient strength (G), and the timing elements pulse width (δ) and center-to-center spacing (Δ) (33).
In terms of these parameters we define the b-value, the diffusion sensitivity factor, as:
𝑏 = 𝛾2𝐺2𝛿2(∆ −𝛿 3)
γ is the gyromagnetic ratio (43), G is the gradient amplitude, δ is the duration of the gradient. The trailing-to-leading edge separation can be described with (Δ-δ) (33). The higher the chosen value, the stronger the diffusional effect will be. To increase the b-value, the gradient (G) amplitude and duration (δ) must be increased in addition to widening the interval between gradient pulses (the center-to-center spacing (Δ)) (44).
The b-value reflects the strength and duration of the pulsed diffusion gradients, is
expressed in units of s/mm2 (33), and is typically between 0 and 4000 s/mm2 on modern MRI scanners (44). The last part of the equation, the quantity (Δ-δ/3), is the diffusion time (τ) which is related to molecular motion (33). It is related to the time diffusion gradients are active before the refocusing gradient is switched on again (43).
Equation 1
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The resulting image contrast is affected by the diffusional properties in the tissue.
Tissues with very mobile molecules result in a lower MR signal, while more static
molecules give a stronger signal (33). The mobility of molecules is characterized by the diffusion coefficient D, which describes their mean square displacement (R2) during a given time interval (T), see Figure 6 (45). For example, the diffusion constant of pure water at body temperature is 3.0×10-3 mm2/s (37), which gives a displacement of 17 µm in 50 msec (45). The diffusion signal intensity is described by Equation 2 (33):
𝑆𝑏= 𝑆0𝑒−𝑏𝐷
Sb is the signal for a particular b-value (33), S0 is the signal at baseline, D is the diffusion coefficient and b is the chosen value (44). From Equation 2 we can see that the b-value is a decisive factor in the outcome of the signal strength, and that higher b-b-values result in lower signal and vice versa.
Diffusion MRI is based on Einstein’s diffusion equation, which assumes free diffusion of water and Gaussian distribution (39). The Stokes-Einstein equation, see Equation 3, shows how the diffusion coefficient D is estimated:
𝐷 = 𝑘𝐵𝑇 6𝜋𝜇𝑅0
Figure 5: PGSE sequence for DWI, which would precede an EPI acquisition. G is the magnitude of the diffusion-weighting gradients, δ is the gradients duration, and Δ is the gradient center-to-center spacing in time (33).
Equation 2
Equation 3
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kB is Boltzmann’s constant, T is temperature, µ is the solvent viscosity and R0 is the solute radius (46). Since the human body has a temperature of 37ºC, T can be
considered constant (47). However, in biological tissues the diffusion is hindered due to obstacles such as cell membranes, fibres and macromolecules, and is considered non-Gaussian diffusion. Therefore, the diffusion coefficient derived from DWI is not the free diffusion coefficient of water. To emphasize that the resulting information differs from the true free diffusion coefficient, the apparent diffusion coefficient (ADC) was introduced (39).
From DWI it is possible to calculate the tissue’s ADC which is a quantitative measurement of tissue diffusivity (14). To calculate ADC, two images with different b-values are
needed. The calculation uses Equation 4 (48):
𝐴𝐷𝐶 = 𝑙𝑛 (𝑆0
𝑆1) (𝑏1− 𝑏0)
In this case S0 and S1 are the signal intensities achieved with b-values b0 and b1, respectively. The ADC is displayed as a parametric map that reflects the degree of diffusion of water molecules. ADC measurements can be taken by placing regions of interest (ROI) on a map, and are measured in units of mm2/s. Typical values vary depending on the imaged organ or pathology (49). Additionally, the calculated ADC values can vary depending on the choice of b-values used (b0 and b1). Due to this it is important to make the appropriate choice of b-values for the chosen tissue. In brain imaging for instance, the appropriate b-values are b0=0 and b1=1000 (48), with typical ADC values of 2.94×10-3 mm2s-1 for CSF, 0.76×10-3 mm2s-1 for grey matter and
0.45×10-3 mm2s-1 for white matter (33). The average ADC value for placentas has been shown to be 1.827 ± 0.19×10-3 mm2s-1 (50) and 1.77 ± 0.19×10-3 mm2s-1 in another article, but it varies depending on gestational age (7).
Since the ADC value is affected by all motions within imaged voxels (perfusion and
diffusion), the term IVIM imaging emerged in addition to diffusion imaging (51). The ADC Figure 6: Illustration showing how diffusion (A) and perfusion (B) both resemble intravoxel incoherent motions. The illustration on the right shows the square displacement (R2) during a given time interval T, of the diffusion coefficient D. The illustration on the left shows how the pseudorandom orientation of the capillaries can resemble incoherent motion at voxel level (45).
Equation 4
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integrates the effects of both diffusion and perfusion, and quantifies the IVIM images, which can be used to obtain separate images of diffusion and perfusion (45).
Perfusion is the delivery of blood via capillaries (microcirculation), and is closely related to the delivery of nutrients and oxygens to tissues. It is however important to distinguish perfusion from bulk blood flow, which is the flow that occurs in major arteries and veins.
Perfusion is measured in units of millilitres per 100 gram per minute (ml/100g/min) (52).
As an example, perfusion is 50 ± 15 ml/100g/min in grey brain matter, and
22 ± 5 ml/100g/min in white brain matter (53). In comparison, the average maternal blood perfusion in the human placenta has been shown to be 176 ± 24 ml/100g/min using EPI sequences (54), and ASL in healthy pregnancies has shown placental perfusion of 188 ± 40 ml/100g/min. With US the perfusion was 177 ml/100g/min and
145 ml/100g/min in two separate studies (55). Perfusion can be measured by MRI, with or without the use of contrast agent. Dynamic susceptibility contrast MRI and dynamic contrast enhanced MRI depend on contrast agent enhancement, while arterial spin labelling (ASL) (52) and IVIM (38) provide measures of perfusion without the use of contrast agents.
2.3.4 Intravoxel incoherent motion (IVIM) imaging
The diffusional signal is influenced by both the molecular diffusion of water and perfusion within the voxels, see Figure 7. At voxel level they can both mimic incoherent
movements, see Figure 6. These movements within the voxels are defined as IVIMs (51), and during the imaging process, the IVIMs cause spin dephasing and signal attenuation (13). To sensitize the DWI to the IVIMs, and separate the diffusion and perfusion, several low b-values, also called “motion” probing gradients pulses, must be used (51). With high b-values the signal loss is mainly due to diffusion. With lower b-values however, both diffusion and perfusion contribute to signal attenuation (13). Exploiting this makes it possible to distinguish perfusion and diffusion from each other (45). The IVIM-technique was originally proposed by Denis Le Bihan (13).
Another approach to describe D is based on molecular mobility, as shown in Equation 5 (45):
𝐷 = 𝒍𝒗/6
𝒍 is the mean vector length of “molecular jumps” due to Brownian motion (or capillary segments in IVIM imaging), while 𝒗 is the mean molecular velocity vector. Both play an important role in distinguishing perfusion from diffusion using the IVIM technique.
Previously, Equation 2 was used to describe the diffusion signal strength, but it does not take into consideration the perfusion part of the signal attenuation. The attenuation factor F must also be taken into account, as shown in Equation 6 (45).
𝑆𝑏= 𝑆0𝑒−𝑏𝐷∗𝐹
F is 1 or less, and depends on the mean length of 𝒍, and the mean velocity 𝒗 of blood in capillaries, in addition to the mean measurement of time T, which is approximately TE in MRI. When using the assumption that the capillary network can be modelled with a network made of a succession of straight segments, the perfusion in the capillaries can affect the MRI signal in two ways, depending on how fast (𝒗) the blood travels and how many capillary segments (𝒍) are traversed during a set T. Therefore, there are two scenarios for how F can be expressed. In the first scenario, when several capillary segments are traversed during one T, making the water molecules within the capillary mimic diffusion, F will be as shown in Equation 7:
Equation 5
Equation 6
14 𝐹 = 𝑒−𝑏𝐷∗
D* is the pseudodiffusion coefficient, which reflects dephasing caused by perfusion in semi-randomly organized capillaries, which can be approximated by Equation 5. In the first scenario, this results in the value of D* being about ten times higher than D measured in biological tissues. Hence, the perfusion part of the signal attenuation will always be larger than the diffusional attenuation. In the second scenario, when capillary segments are not changed due to slower blood flow, longer segments or shorter T, the expression for F will change. There are several equations available for calculating F in this scenario, but they also result in the same: the signal attenuation due to perfusion being greater than diffusion. In conclusion, both scenarios end up with the same result,
regardless of capillary geometry or blood velocity. This differential effect of diffusion and perfusion on the signal attenuation makes it possible to separate them on a quantitative basis, and makes IVIM imaging possible (45).
When separating diffusion from perfusion, several terms are used to describe IVIMs within the voxels. The measured water flowing through perfused capillaries is called the perfusion fraction f, which describes the fraction of a voxel occupied by capillaries, and its values range from 0 to 1 (13). (1-f) characterizes the static, diffusing only, intra- and extracellular water (45), which is where diffusion effects D take place (13). From this we can see that (1-f) represents the opposite of f, i.e. the fraction of the voxel not occupied by capillaries, and also has a maximum value of 1, see Figure 7. Furthermore, f can give
an indication of the amount of blood flowing, rather than the flow velocity (17). The pseudodiffusion coefficient D* is also used. D* is normally 5-10 times greater than D, but how much depends on the steepness of the initial part of the curve (see Figure 8). The steepness is determined by the capillary density and perfusion (13). It is also associated with larger scale movements and characterizes incoherent blood flow (19), such as blood within the intervillous spaces and in fetal capillaries within villi (12). D, D*, f and (1-f) are known under other names also, depending in which programs and
literature are used2 (13).
When only diffusion is taken into account, the semi-logarithmic plot of signal
attenuation versus b-value results in a straight line with slope D (Equation 2), the straight black line in Figure 8 (13).
But at low b-values the signal can also be affected by perfusion, and the IVIM effect causes deviation from the straight line whereby the signal drops faster (as shown
2 D is also known as ADCslow or ADClow, D* can be referred to as ADCfast or ADChigh, whereas f is also known as perfusion fraction or volume fractionfast, and (1-f) is also known as the diffusion in the extravascular space fraction or volume fractionslow (13).
Equation 7
Figure 7: Illustrates IVIMs within a voxel.
Perfusion fraction f can be seen as the perfusion through the blood vessel (burgundy arrows), while the diffusion in the extravascular space (1-f) can be seen as the green arrows in the image (45).
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in the red-shaded area at the top-left of Figure 8). At the other end of the scale, for high b-values, the kurtosis effect causes a deviation whereby the signal drops more slowly (as shown in the green-shaded area at the bottom-right of Figure 8). The non-Gaussian behaviour of water molecules in complex biological tissues leads to the kurtosis effect.
The effect is most prominent with b-values above 1500 s/mm2 (56). These variations in signal attenuation affect the calculated ADC value, with greater signal attenuation leading to a higher ADC values and vice versa (13).
A more advanced bi-exponential model, made by Le Bihan et al (45), also includes perfusion (13), and can be used to describe IVIM (17). The signal can be described by the bi-exponential model of IVIM in Equation 8:
𝑆𝑏/𝑆0= 𝑓𝑒−𝑏(𝐷+𝐷∗)+ (1 − 𝑓)𝑒𝑏𝐷
The tissue diffusion and blood flow component separately affect the signal, which results in a bi-exponential shape. The first part of Equation 8, 𝑓𝑒−𝑏(𝐷+𝐷∗), is related to the
pseudodiffusion (perfusion) signal attenuation, while the second part of the equation, (1 − 𝑓)𝑒𝑏𝐷, is related to the diffusion attenuation (38). To separate the
perfusion from the diffusion, DWI images with several b-values must be acquired. From them it is possible to both estimate and create maps of D, D*, f and (1-f), by fitting the Figure 8: Relationship between signal attenuation ln(S/S0) and the b-value. The figure shows the deviation of the expected MRI signal in DWI from a mono-exponential model. The straight black line shows the theoretical relationship, with the slope of the line indicating the ADC (apparent diffusion coefficient). The dotted blue line shows the actual relationship, when IVIM effects (red area) at low b-values and kurtosis (green area) effects at high values are taken into account (13).
Equation 8
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data to Equation 8 (13). Only at low b-values can the IVIM effects be considered
significant. If b-values exceed a threshold, the perfusion fraction is considered negligible.
The cut off threshold, typically in the range 200-400 s/mm2 (see Figure 8), is expected to differ depending on which organ or pathology is being imaged (16). At least half of the
The cut off threshold, typically in the range 200-400 s/mm2 (see Figure 8), is expected to differ depending on which organ or pathology is being imaged (16). At least half of the