DOI:10.2214/AJR.06.1403
AJR 2007; 188:1622-1635
© American Roentgen Ray Society
Diffusion-Weighted MRI in the Body: Applications and Challenges in Oncology
Dow-Mu Koh1,2 and
David J. Collins1,2
1 Cancer Research UK Clinical Magnetic Resonance Research Group, Institute of
Cancer Research, Sutton, Surrey, United Kingdom.
2 Academic Department of Radiology, Royal Marsden Hospital, Downs Rd., Sutton,
Surrey SM2 5PT, United Kingdom.
Received October 22, 2006;
accepted after revision January 26, 2007.
Supported by Cancer Research UK Grant C1060/A5117.
Address correspondence to D. M. Koh.
Abstract
OBJECTIVE. In this article, we present the basic principles of
diffusion-weighted imaging (DWI) that can aid radiologists in the qualitative
and quantitative interpretation of DW images. However, a detailed discussion
of the physics of DWI is beyond the scope of this article. A short discussion
ensues on the technical aspects of performing DWI in the body. The emerging
applications of DWI for tumor detection, tumor characterization,
distinguishing tumor tissue from nontumor tissue, and monitoring and
predicting treatment response are highlighted. The challenges to widespread
adoption of the technique for cancer imaging in the body are discussed.
CONCLUSION. DWI derives its image contrast from differences in the
motion of water molecules between tissues. Such imaging can be performed
quickly without the need for the administration of exogenous contrast medium.
The technique yields qualitative and quantitative information that reflects
changes at a cellular level and provides unique insights about tumor
cellularity and the integrity of cell membranes. Recent advances enable the
technique to be widely applied for tumor evaluation in the abdomen and pelvis
and have led to the development of whole-body DWI.
Keywords: abdominal imaging • cancer • diffusion-weighted imaging • MRI • oncologic imaging • pelvic imaging • whole-body imaging
Introduction
For two decades, diffusion-weighted imaging (DWI) has been applied to the
evaluation of intracranial diseases, such as cerebrovascular accidents,
trauma, epilepsy, depression, dementia, and neurotoxicity. DWI has been shown
to be capable of detecting early or subtle changes within the brain before any
visible abnormality can be seen on conventional morphologic imaging
[1,
2].
In the 1990s, a series of technologic advances made it possible to
translate DWI measurements to extracranial sites, including the abdomen and
pelvis. The developments of echo-planar imaging (EPI), high-gradient
amplitudes, multichannel coils, and parallel imaging have been instrumental in
extending the applications of DWI. In particular, the introduction of parallel
imaging, which enabled reduction in the TE, the echotrain length, and the
k-space filling time, led to substantially less motion artifact at image
acquisition, thus enabling high-quality DW images of the body to be
obtained.
DWI is increasingly used for the evaluation of extracranial diseases. There
is growing interest in the application of DWI for the evaluation of the
patient with cancer. DWI measurements are quick to perform (typically
1–5 minutes) and do not require the administration of exogenous contrast
medium. Thus, these imaging sequences can be appended to existing imaging
protocols without a significant increase in the examination time. Furthermore,
DWI yields both qualitative and quantitative information that can be helpful
for tumor assessment.
Principles and Concepts
In the following section, we summarize the key concepts of DWI, which may
aid radiologists in the interpretation of DW images. However, an in-depth
discussion of the physics and mathematics of DWI is beyond the scope of this
review. The interested reader may refer to articles related to that subject
[3,
4].
Diffusion of Water Molecules in Tissue
DWI explores the random motion of water molecules in the body. Water
molecules held in a container outside the body are in constant random brownian
motion. This uninhibited motion of water molecules is free diffusion. By
contrast, the movement of water molecules in biologic tissues is restricted
because their motion is modified and limited by interactions with cell
membranes and macromolecules.
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Fig. 1A —Diffusion of water molecules. Restricted diffusion:
cellularity and intact cell membranes. Drawing represents 1 voxel of tissue
evaluated by diffusion-weighted imaging (DWI) containing cells and blood
vessel. Note water molecules (black circles with arrows) within
extracellular space, intracellular space, and intravascular space, all of
which contribute to measured MR signal. In this highly cellular environment,
water diffusion is restricted because of reduced extracellular space and by
cell membranes, which act as barrier to water movement.
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Fig. 1B —Diffusion of water molecules. Free diffusion: low cellularity
and defective cell membranes. In less cellular environment, relative increase
in extracellular space allows freer water diffusion than more cellular
environment would. Defective cell membranes also allow movement of water
molecules between extracellular and intracellular spaces.
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Fig. 2 —Measuring water diffusion. Stejskal and Tanner
[10] adopted T2-weighted
spin-echo sequence for measuring water diffusion. They applied symmetric
diffusion-sensitizing gradient around 180° refocusing pulse. On this
schematic drawing, stationary molecules are unaffected by gradients and
measured signal intensity is preserved. By contrast, moving water molecules
acquire phase information from first gradient, which is not entirely rephased
by second gradient, thereby leading to signal loss. Hence, water diffusion is
detected as attenuation of measured MR signal intensity. RF = radiofrequency
pulse.
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In biologic tissue, the DWI signal is derived from the motion of water
molecules in the extracellular space, the intracellular space, and the
intravascular space [4] (Fig.
1A,
1B). Not surprisingly, given a
unit time, water molecules in the intravascular space will have a greater
diffusion distance because of blood flow than those in the extracellular and
intracellular spaces. Clearly, the contribution of intravascular water
diffusion to the measured DWI signal can vary among tissues. In tumors showing
increased vascularity, the contribution of intravascular water diffusion to
the MR signal may account for a significant proportion
[5].
The degree of restriction to water diffusion in biologic tissue is
inversely correlated to the tissue cellularity and the integrity of cell
membranes
[6–9].
The motion of water molecules is more restricted in tissues with a high
cellular density associated with numerous intact cell membranes (e.g., tumor
tissue). The lipophilic cell membranes act as barriers to motion of water
molecules in both the extracellular and intracellular spaces. By contrast, in
areas of low cellularity or where the cellular membrane has been breached, the
motion of water molecules is less restricted. A less cellular environment
provides a larger extracellular space for diffusion of water molecules, and
these molecules may also freely transgress defective cell membranes to move
from the extracellular into the intracellular compartment (Fig.
1A,
1B).
Measuring Water Motion (Apparent Diffusion) Using DWI
Stejskal and Tanner [10]
described an MR experiment that could be applied to the detection and
quantification of water diffusion in vivo. They adapted a standard T2-weighted
spin-echo sequence by applying a symmetric pair of diffusion-sensitizing
(bipolar) gradients around the 180° refocusing pulse. That approach is now
the basis of many DWI sequences in clinical use today. Static molecules
acquire phase information from the first diffusion gradient, but information
will be rephased by the second diffusion gradient without a significant change
in the measured signal intensity. By comparison, moving water molecules
acquire different phase information from the first gradient, but because of
their motion, their signal will not be completely rephased by the second
gradient, thus leading to a signal loss
(Fig. 2). Hence, the motion of
water molecules is detected as attenuation of the measured signal intensity at
DWI. The degree of water motion has been found to be proportional to the
degree of signal attenuation.
The sensitivity of the DWI sequence to water motion can be varied by
changing the gradient amplitude, the duration of the applied gradient, and the
time interval between the paired gradients. On clinical MR scanners, the
diffusion sensitivity is easily varied by changing the parameter known as the
"b value," which is proportional to these three factors. When the
b value is changed, it is usually the gradient amplitude, rather than the
duration or time intervals between gradients, that is altered.
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Fig. 3A —Tissue characterization by diffusion-weighted images.
Diffusion-weighted MR images in 55-year-old man with liver metastasis obtained
at different b values show large heterogeneous metastasis within right lobe of
liver. Necrotic center of metastasis (squares) shows attenuation of
signal intensity with increasing b values, indicating less restricted
diffusion. By comparison, rim of tumor (rectangles) is more cellular
and shows little signal attenuation with increasing b value.
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Fig. 3B —Tissue characterization by diffusion-weighted images.
Diffusion-weighted MR images in 55-year-old man with liver metastasis obtained
at different b values show large heterogeneous metastasis within right lobe of
liver. Necrotic center of metastasis (squares) shows attenuation of
signal intensity with increasing b values, indicating less restricted
diffusion. By comparison, rim of tumor (rectangles) is more cellular
and shows little signal attenuation with increasing b value.
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Fig. 3C —Tissue characterization by diffusion-weighted images.
Diffusion-weighted MR images in 55-year-old man with liver metastasis obtained
at different b values show large heterogeneous metastasis within right lobe of
liver. Necrotic center of metastasis (squares) shows attenuation of
signal intensity with increasing b values, indicating less restricted
diffusion. By comparison, rim of tumor (rectangles) is more cellular
and shows little signal attenuation with increasing b value.
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Water molecules with a large degree of motion or a great diffusion distance
(e.g., within the intravascular space) will show signal attenuation with small
b values (e.g., b = 50–100 s/mm2). By contrast, large b
values (e.g., b = 1,000 s/mm2) are usually required to perceive
slow-moving water molecules or small diffusion distances because these show
more gradual signal attenuation with increasing b values.
The root mean square displacement of water molecules that occurs during a
diffusion measurement is estimated to be approximately 8 µm
[11]. By comparison, the mean
size of cells in the human body measures about 10 µm. Hence, the
information provided by DWI reflects changes that are occurring at a cellular
spatial scale. For this reason, DWI is perceived as an advantageous tool for
evaluating changes in the tumor microenvironment, both before and after
treatment.
Interpretation of DWI
Qualitative Assessment of DWI
DWI is typically performed using at least two b values (e.g., b = 0
s/mm2 and other b values from 0 to 1,000 s/mm2) to
enable meaningful interpretation. Generally, the larger the b value, the
greater the degree of signal attenuation from water molecules. By observing
the relative attenuation of signal intensity on images obtained at different b
values, tissue characterization based on differences in water diffusion
becomes possible. For example, in a heterogeneous tumor, the more cystic or
necrotic fraction of the tumor will show greater signal attenuation on high
b-value images because water diffusion is less restricted. By contrast, the
more cellular solid tumor areas will continue to show relatively high signal
intensity (Fig. 3A,
3B,
3C). Visual assessment of the
relative tissue signal attenuation at DWI is being applied for tumor
detection, tumor characterization, and the evaluation of treatment response in
patients with cancer.
When diffusion measurements are being performed, the direction of water
diffusion along the three orthogonal directions of the magnet (phase select,
frequency select, and slice select) can be assessed independently by applying
diffusion gradients in each of these directions. DW images that are the sum of
the directionally acquired DW images are known as trace or index DW
images.
One of the pitfalls of visual assessment of directional or index DW images
is that the signal intensity observed depends on both water diffusion and the
T2 relaxation time. Consequently, an area with a very long T2 relaxation time
may remain high signal at DWI and be mistaken for restricted diffusion. This
is known as the "T2 shine-through" effect (Fig.
4A,
4B,
4C). This effect can be
sometimes reduced by the choice of an appropriate TE (a short one) and b value
(a large one), but it cannot be easily avoided. The relative contribution of
T2 signal intensity to DW images is a potential source of error in image
interpretation and can limit their usefulness when comparing results among
studies performed using different imaging protocols. A simple solution to the
problem of T2 shine-through can be found using the exponential image. The
exponential image is formed by taking the ratio of a DW image divided by an
unweighted image (b = 0 s/mm2) from the same image series and slice
position.
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Fig. 4A —T2 shine-through. Diffusion-weighted images of liver in
62-year-old man with liver metastasis obtained at different b values show
ill-defined high-signal-intensity metastasis in right lobe of liver
(arrow, C). However, note gallbladder (arrowhead,
C) also shows high signal intensity, even on image obtained with b
value of 500 s/mm2. In this case, high signal intensity of
gallbladder is not due to restricted water diffusion but to T2 shine-through.
Note intrinsic high signal intensity of gallbladder on T2-weighted (b = 0
s/mm2) image (A) due to its long T2 relaxation time.
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Fig. 4B —T2 shine-through. Diffusion-weighted images of liver in
62-year-old man with liver metastasis obtained at different b values show
ill-defined high-signal-intensity metastasis in right lobe of liver
(arrow, C). However, note gallbladder (arrowhead,
C) also shows high signal intensity, even on image obtained with b
value of 500 s/mm2. In this case, high signal intensity of
gallbladder is not due to restricted water diffusion but to T2 shine-through.
Note intrinsic high signal intensity of gallbladder on T2-weighted (b = 0
s/mm2) image (A) due to its long T2 relaxation time.
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Fig. 4C —T2 shine-through. Diffusion-weighted images of liver in
62-year-old man with liver metastasis obtained at different b values show
ill-defined high-signal-intensity metastasis in right lobe of liver
(arrow, C). However, note gallbladder (arrowhead,
C) also shows high signal intensity, even on image obtained with b
value of 500 s/mm2. In this case, high signal intensity of
gallbladder is not due to restricted water diffusion but to T2 shine-through.
Note intrinsic high signal intensity of gallbladder on T2-weighted (b = 0
s/mm2) image (A) due to its long T2 relaxation time.
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Another phenomenon that can be encountered is that of diffusion anisotropy.
Diffusion anisotropy refers to unequal directional diffusion, which occurs as
a result of tissue or structural organization. A good example of diffusion
anisotropy is seen along the white matter tracts of the internal capsule in
the brain. The diffusion motion appears relatively free in the head–foot
direction along the long axis of the white matter tracts, but appears
restricted in the anteroposterior and right–left directions across the
neuronal fibers. In the assessment of tumors, diffusion anisotropy can help to
identify tumor invasion of adjacent structures
[12]. However, diffusion in
tumors is usually isotropic because malignant cells typically grow in a
disorganized fashion.
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Fig. 5A —Apparent diffusion coefficient (ADC). Simplified schematic
shows derivation of ADC. Logarithm of relative signal intensity is plotted on
y-axis against values on x-axis. Slope of line fitted
through plots is ADC. In this example, slope of line (ADC) is smaller for
tumor (gray line) than for normal liver (black line).
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Fig. 5B —Apparent diffusion coefficient (ADC). Tumor area with low ADC
(gray outline) is darker than normal liver with higher ADC (black
outline). Note contrast on ADC map is opposite that seen on
diffusion-weighted image. On diffusion-weighted image, tumor showed less
signal attenuation and appeared higher signal intensity than normal liver.
B and C obtained in 45-year-old man with liver metastasis.
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Fig. 5C —Apparent diffusion coefficient (ADC). Tumor area with low ADC
(gray outline) is darker than normal liver with higher ADC (black
outline). Note contrast on ADC map is opposite that seen on
diffusion-weighted image. On diffusion-weighted image, tumor showed less
signal attenuation and appeared higher signal intensity than normal liver.
B and C obtained in 45-year-old man with liver metastasis.
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Quantitative Analysis of DWI
By performing DWI using different b values, quantitative analysis is
possible. This analysis is usually performed at a push of a button on the
scanner or workstation that results in the calculation of the apparent
diffusion coefficient (ADC) (Appendix 1). One simplified method of visualizing
this process is by considering the signal attenuation of a tissue with
increasing b values. By plotting the logarithm of the relative signal
intensity of the tissue on the y-axis against the b values on the
x-axis, a line can be fitted through the plots (exponential
function). The slope of the line describes the ADC (Fig.
5A,
5B,
5C). Not surprisingly, the fit
can be improved by using more b values to reduce the error in ADC
calculation.
The ADC is independent of magnetic field strength and can overcome the
effects of T2 shine-through, thus allowing more meaningful comparison of
results. The ADC is calculated for each pixel of the image and is displayed as
a parametric map. By drawing regions of interests on these maps, the ADCs of
different tissues can be derived.
Areas of restricted diffusion in highly cellular areas show low ADC values
compared with less cellular areas that return higher ADC values. At this
point, it is important to mention that although areas of restricted diffusion
will appear to be higher in signal intensity on the directional or index DW
images, these areas will appear as low-signal-intensity areas (opposite to DW
images) on the ADC map (Fig.
5A,
5B,
5C).
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Fig. 6 —Diffusion-weighted imaging in liver. Graph shows signal
attenuation of normal liver with increasing b values (thin dashed
line). Note there is initial rapid attenuation of signal intensity with
small increase in b value from zero. This is due to nulling of signal
contribution from capillary perfusion. Slope of line (thick dashed
line) fitted through all b values describes apparent diffusion
coefficient (ADC). However, slope of line (thick solid line) fitted
through only higher b values (e.g., 150–500 mm2/s) can be
used to describe perfusion-insensitive ADC. Error bars show 95% CI of pixel
values.
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Fig. 7A —Diffusion-weighted imaging (DWI) performed during
free-breathing of 65-year-old man with rectal cancer. Thin-partition DWI can
be achieved using free-breathing technique. In this example, axial image with
b value of 750 s/mm2 (B) is displayed using inverted gray
scale; image shows areas of restricted diffusion corresponding to area of
rectal tumor (arrows) and 4-mm mesorectal lymph node
(circles). Thinner slice partition allows multiplanar reformats for
anatomic localization. Addition perirectal nodes (arrowheads,
D) are also visible on sagittal reformatted image (D). Note
corresponding features on T2-weighted MRI (A).
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Fig. 7B —Diffusion-weighted imaging (DWI) performed during
free-breathing of 65-year-old man with rectal cancer. Thin-partition DWI can
be achieved using free-breathing technique. In this example, axial image with
b value of 750 s/mm2 (B) is displayed using inverted gray
scale; image shows areas of restricted diffusion corresponding to area of
rectal tumor (arrows) and 4-mm mesorectal lymph node
(circles). Thinner slice partition allows multiplanar reformats for
anatomic localization. Addition perirectal nodes (arrowheads,
D) are also visible on sagittal reformatted image (D). Note
corresponding features on T2-weighted MRI (A).
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Fig. 7C —Diffusion-weighted imaging (DWI) performed during
free-breathing of 65-year-old man with rectal cancer. Thin-partition DWI can
be achieved using free-breathing technique. In this example, axial image with
b value of 750 s/mm2 (B) is displayed using inverted gray
scale; image shows areas of restricted diffusion corresponding to area of
rectal tumor (arrows) and 4-mm mesorectal lymph node
(circles). Thinner slice partition allows multiplanar reformats for
anatomic localization. Addition perirectal nodes (arrowheads,
D) are also visible on sagittal reformatted image (D). Note
corresponding features on T2-weighted MRI (A).
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Fig. 7D —Diffusion-weighted imaging (DWI) performed during
free-breathing of 65-year-old man with rectal cancer. Thin-partition DWI can
be achieved using free-breathing technique. In this example, axial image with
b value of 750 s/mm2 (B) is displayed using inverted gray
scale; image shows areas of restricted diffusion corresponding to area of
rectal tumor (arrows) and 4-mm mesorectal lymph node
(circles). Thinner slice partition allows multiplanar reformats for
anatomic localization. Addition perirectal nodes (arrowheads,
D) are also visible on sagittal reformatted image (D). Note
corresponding features on T2-weighted MRI (A).
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However, it is simplistic to assume that the attenuation of the signal
intensity with increasing b values occurs linearly in tissues. In the liver,
the signal intensity can be seen initially to attenuate rapidly with low b
values, followed by a more gradual signal reduction
(Fig. 6). The initial
reduction in signal intensity is believed to be due to vascular capillary
perfusion, where the signal from fast-moving water molecules is rapidly
attenuated by low diffusion weighting
[4]. Thus, diffusion-weighted
data acquired over a range of b values that includes low b values (< 50
s/mm2) are sensitive to signal attenuation from perfusion.
From the previous discussion, it stands to reason that deriving a
flow-sensitive ADC value and a flow-insensitive ADC value is possible
[4]. An exponential function
fitted across all the b values describes the conventional ADC (flow
sensitive). However, an exponential function fitted through only the high b
values (e.g., b = 150–500 s/mm2) can be used to describe a
flow-insensitive ADC. The flow-insensitive ADC may potentially provide a more
accurate estimate of the cellularity of the tumor microenvironment by
minimizing any vascular contributions
(Fig. 6). As a further
refinement, a biexponential function may be used to calculate conventional ADC
from DWI that includes low b values
[4].
In the brain, the presence of highly organized anatomic structures allows a
particular quantitative diffusion technique, diffusion tensor imaging, to be
used to advantage. Diffusion tensor imaging yields information about both the
rate and the direction of water diffusion by sampling water motion in at least
six directions. The potential application of diffusion tensor imaging in
organs with discernible structural organization, such as the prostate and
kidneys, is currently being evaluated
[13–15].
Clinical DWI in the Body: Technical Issues
A range of imaging sequences and techniques are now available for
performing DWI studies in the body. The principles of these imaging sequences
will not be discussed in this review, and the reader may want to refer
elsewhere [16]. The range of
imaging techniques includes conventional spin-echo and stimulated echo, fast
spin-echo, gradient-echo (e.g., steady-state free precession), EPI, and line
scan diffusion imaging [16].
Each of these techniques has its advantages and limitations
[16].
In the implementation of DWI in the body, two main strategies can be
pursued: breath-hold imaging and non-breath-hold imaging. Breath-hold imaging
allows a target volume (e.g., liver, kidney, and elsewhere in the abdomen) to
be rapidly assessed. The images retain good anatomic detail and are usually
not degraded by respiratory motion or volume averaging. Small lesions may be
better perceived and the quantification of ADC is theoretically more accurate
than with a non-breath-hold technique. One example of such a technique is
breath-hold single-shot spin-echo EPI combined with parallel imaging (e.g.,
sensitivity encoding) and fat suppression
[17–22].
The image acquisition time at each breath-hold is 20–30 seconds, and
imaging is typically completed in a few breath-holds. The disadvantages of
breath-hold imaging include a limited number of b-value images that can be
acquired over the duration of a breath-hold, poorer signal-to-noise ratio
compared with multiple averaging methods, and greater sensitivity to pulsatile
and susceptibility artifacts.
Non-breath-hold spin-echo EPI combined with fat suppression is a versatile
technique that can be used as a general purpose DWI sequence in the body and
for whole-body imaging [23].
Multiple slice excitation and signal averaging over a longer duration improve
the signal-to-noise and contrast-to-noise ratios
[23]. Thin partitions can be
achieved (4–5 mm), thus improving spatial resolution and enabling
multiplanar image reformats (Fig.
7A,
7B,
7C,
7D). Furthermore, the longer
acquisition time with non-breath-hold imaging provides flexibility in the use
of multiple (> 5) or of high b values; for example, a b value of 1,000
s/mm2 is optimum for optimum background suppression in wholebody
imaging. However, the image acquisition time using this technique is longer
compared with breath-hold imaging, typically 3–6 minutes depending on
the coverage required and the number of b values used, and evaluation of tumor
heterogeneity may be compromised by the degree of motion and volume averaging.
Table 1 provides illustrative
examples of breath-hold and non-breath-hold imaging sequences that may be
applied for tumor evaluation in the body.
Despite the numerous advances in hardware and software, meticulous
technique is still needed to minimize bulk motion artifacts that can
significantly degrade image quality. When performing DWI in the body, the
acquisition time should be kept as short as possible. Depending on the tissue
being investigated, the TR should be long enough to minimize T1 saturation
effects. However, it should be borne in mind that DWI sequences applied to
imaging in the body may have inherent tradeoffs because of the need for short
acquisition times, and radiologists should be acquainted with these when
prescribing their use and when interpreting images.
Applications of DWI in the Body for Tumor Assessment
DWI yields qualitative and quantitative information that provides unique
insight into tumor characteristics, and there is growing evidence for its use
in the assessment of the patient with cancer.
Tumor Detection
Tumors are frequently more cellular than the tissue from which they
originate and thus appear to be of relatively high signal intensity
(restricted diffusion) at DWI.
DWI is being applied for the detection of liver metastases. In the liver,
low b-value images (e.g., b = 50–150 s/mm2) that suppress the
high-signal flow from the hepatic vessels, resulting in black blood images,
have been found to be useful for lesion detection
[24] (Fig.
8A,
8B,
8C,
8D,
8E). Metastases appear as
high-signal-intensity foci at DWI.
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Fig. 8A —Lesion detection in 58-year-old woman with metastatic
disease. Diffusion-weighted images show small hyperintense metastasis in right
lobe of liver (arrowhead, C). Lesion is not easily seen on
unenhanced T1- and T2-weighted images (A and B). At
diffusion-weighted imaging (DWI), high signal (arrow, C) from
intrahepatic vessels is suppressed by application of diffusion gradient
(arrow, D) on image obtained with b value of 150
s/mm2. Such black-blood DW images can facilitate lesion detection.
However, note also susceptibility and cardiac motion artifacts (black
lines) over left lobe on images obtained with b values of 150
(D) and 500 (E) s/mm2.
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Fig. 8B —Lesion detection in 58-year-old woman with metastatic
disease. Diffusion-weighted images show small hyperintense metastasis in right
lobe of liver (arrowhead, C). Lesion is not easily seen on
unenhanced T1- and T2-weighted images (A and B). At
diffusion-weighted imaging (DWI), high signal (arrow, C) from
intrahepatic vessels is suppressed by application of diffusion gradient
(arrow, D) on image obtained with b value of 150
s/mm2. Such black-blood DW images can facilitate lesion detection.
However, note also susceptibility and cardiac motion artifacts (black
lines) over left lobe on images obtained with b values of 150
(D) and 500 (E) s/mm2.
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Fig. 8C —Lesion detection in 58-year-old woman with metastatic
disease. Diffusion-weighted images show small hyperintense metastasis in right
lobe of liver (arrowhead, C). Lesion is not easily seen on
unenhanced T1- and T2-weighted images (A and B). At
diffusion-weighted imaging (DWI), high signal (arrow, C) from
intrahepatic vessels is suppressed by application of diffusion gradient
(arrow, D) on image obtained with b value of 150
s/mm2. Such black-blood DW images can facilitate lesion detection.
However, note also susceptibility and cardiac motion artifacts (black
lines) over left lobe on images obtained with b values of 150
(D) and 500 (E) s/mm2.
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Fig. 8D —Lesion detection in 58-year-old woman with metastatic
disease. Diffusion-weighted images show small hyperintense metastasis in right
lobe of liver (arrowhead, C). Lesion is not easily seen on
unenhanced T1- and T2-weighted images (A and B). At
diffusion-weighted imaging (DWI), high signal (arrow, C) from
intrahepatic vessels is suppressed by application of diffusion gradient
(arrow, D) on image obtained with b value of 150
s/mm2. Such black-blood DW images can facilitate lesion detection.
However, note also susceptibility and cardiac motion artifacts (black
lines) over left lobe on images obtained with b values of 150
(D) and 500 (E) s/mm2.
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Fig. 8E —Lesion detection in 58-year-old woman with metastatic
disease. Diffusion-weighted images show small hyperintense metastasis in right
lobe of liver (arrowhead, C). Lesion is not easily seen on
unenhanced T1- and T2-weighted images (A and B). At
diffusion-weighted imaging (DWI), high signal (arrow, C) from
intrahepatic vessels is suppressed by application of diffusion gradient
(arrow, D) on image obtained with b value of 150
s/mm2. Such black-blood DW images can facilitate lesion detection.
However, note also susceptibility and cardiac motion artifacts (black
lines) over left lobe on images obtained with b values of 150
(D) and 500 (E) s/mm2.
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Nasu et al. [25] found that
DWI was more accurate than superparamagnetic iron oxide (SPIO)–enhanced
MRI for the detection of liver metastases. In that study, the sensitivity and
specificity of SPIO-enhanced MRI were 66% and 90%, respectively. By
comparison, DWI was found to have a higher sensitivity of 82% and specificity
of 94%. In another study, DWI was found to have a similarly high sensitivity
of 86% and specificity of 94% for the detection of colorectal hepatic
metastases [26].
Some of the challenges encountered in DWI of the liver are cardiac motion
and susceptibility artifacts that can obscure or diminish visualization of the
left lobe (Fig. 7A,
7B,
7C,
7D). The susceptibility
effects may result from air in the adjacent stomach or colon. Artifacts
resulting from cardiac motion can be reduced by triggered acquisition by ECG
or a peripheral pulse unit, thus improving image quality and signal-to-noise
ratio in the left lobe of the liver
[27,
28]. Images may also be
acquired with the aid of respiratory triggering to minimize inadvertent
breathing motion. However, these techniques increase the image acquisition
time, which can render the examination more susceptible to bulk motion.
Tumor Characterization
Tumors differ in their cellularity, and this difference may reflect their
histologic composition and biologic aggressiveness. The use of DWI for tumor
characterization was first shown in brain tumors. The interested reader might
like to refer to a more in-depth account of the use of DWI in the evaluation
of intracranial tumors [29],
which is beyond the scope of this article.
To characterize lesions in the liver using DWI, b values ranging between 0
and 500 s/mm2 are appropriate
[25,
30]. Qualitative visual
assessment can help to distinguish cystic from solid lesions (Fig.
9A,
9B,
9C,
9D,
9E,
9F). However, it is often
difficult to distinguish different types of solid lesions from one another in
the liver by visual assessment alone. For example, a hemangioma will exhibit
restricted diffusion and can mimic the appearance of a metastasis at DWI (Fig.
9A,
9B,
9C,
9D,
9E,
9F).
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Fig. 9A —Lesion characterization in 48-year-old man with liver cancer.
Diffusion-weighted images show cyst (arrow, A) and metastasis
(asterisk, A) in right lobe of liver. Signal from cyst is
attenuated with increasing b value, whereas cellular tumor maintains high
signal intensity.
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Fig. 9B —Lesion characterization in 48-year-old man with liver cancer.
Diffusion-weighted images show cyst (arrow, A) and metastasis
(asterisk, A) in right lobe of liver. Signal from cyst is
attenuated with increasing b value, whereas cellular tumor maintains high
signal intensity.
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Fig. 9C —Lesion characterization in 48-year-old man with liver cancer.
Diffusion-weighted images show cyst (arrow, A) and metastasis
(asterisk, A) in right lobe of liver. Signal from cyst is
attenuated with increasing b value, whereas cellular tumor maintains high
signal intensity.
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Fig. 9D —Lesion characterization in 48-year-old man with liver cancer.
Other solid lesions can mimic appearance of metastasis. Hemangioma
(circle, D) shows restricted diffusion on image obtained with
b value of 500 s/mm2. However, note typical high T2 signal
intensity of lesion. Hence, it is useful to interpret diffusion-weighted
imaging sequences with other imaging sequences.
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Fig. 9E —Lesion characterization in 48-year-old man with liver cancer.
Other solid lesions can mimic appearance of metastasis. Hemangioma
(circle, D) shows restricted diffusion on image obtained with
b value of 500 s/mm2. However, note typical high T2 signal
intensity of lesion. Hence, it is useful to interpret diffusion-weighted
imaging sequences with other imaging sequences.
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Fig. 9F —Lesion characterization in 48-year-old man with liver cancer.
Other solid lesions can mimic appearance of metastasis. Hemangioma
(circle, D) shows restricted diffusion on image obtained with
b value of 500 s/mm2. However, note typical high T2 signal
intensity of lesion. Hence, it is useful to interpret diffusion-weighted
imaging sequences with other imaging sequences.
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Using quantitative evaluation, investigators have found that benign liver
lesions, such as cysts and hemangiomas, have higher mean ADC values (e.g.,
2.45 x 10–3 mm2/s) than malignant lesions,
such as metastases and hepatocellular carcinoma (e.g., 1.08 x
10–3 mm2/s)
[31,
32]. The ADC has also been
used to distinguish abscesses, which have low ADC values, from cystic and
necrotic metastases, which have higher ADC values
[33]. However, although the
ADC values differ in summary statistics between benign and malignant lesions,
using an individual ADC value to characterize lesions prospectively can be
difficult. This difficulty is due to the considerable overlap in the ADC
values of benign and malignant abnormalities. The wide range of ADCs in tumors
may be explained by biologic variations of tumors, which become even more
obvious across studies. For example, although some researchers have reported
that metastases have lower ADC values than liver
[31], others have found the
ADCs of metastases to be higher than that of liver
[34,
35]. Another possible reason
for the wide range of ADC values may be errors in the measurements and image
analysis. Hence, for the time being, DWI of the liver for lesion
characterization should be combined with all available imaging to make the
optimal assessment.
In the abdomen, DWI has also been applied to characterize focal renal
lesions [18,
36]. Because of the
organization of the renal tubules, water diffusion in the normal kidney is
anisotropic [13,
37]. DWI in the kidney can
readily distinguish between cystic from solid renal lesions
[18]. However, it is not yet
possible to confidently distinguish malignant from benign renal neoplasms on
the basis of qualitative assessment or ADC measurements
[18]. Elsewhere, the ADCs of
malignant breast lesions have been found to be lower than the ADCs associated
with benign diseases [6,
38,
39], and DWI was able to
distinguish cystic soft-tissue sarcomas from solid types
[40]. More recently, DWI has
also been found to be useful in detecting colorectal carcinoma
[41] and showing cystic
lesions of the pancreas [42]
and ovaries [43].
Distinguishing Tumors from Nontumors
In prostate cancer, differentiating tumor from other causes of a
low-signal-intensity lesion in the prostate gland is difficult on conventional
T2-weighted MRI. Recently, DWI has shown potential for tumor identification
[44,
45]. The normal central gland
of the prostate has a lower ADC than the peripheral zone
[44,
45]. Prostate cancers, which
appear as low-signal-intensity foci on ADC maps, typically show lower ADC
values than the peripheral zone and the transitional zone and central gland
[44,
45]. However, there is
significant overlap in the ADC values of prostate cancer and benign prostate
changes [44]. Interestingly,
investigators have recently observed that low ADC in the central gland and
transitional zone accompanied by the loss of glandular anisotropy is more
suggestive of tumor than glandular hyperplasia
[46].
In the spine, the ability to distinguish a malignant from a nonmalignant
cause of vertebral collapse remains challenging. Malignant vertebral
infiltration and fracture frequently appear as high-signal-intensity areas on
DWI compared with nonmalignant causes
[22,
47–51].
A sensitivity of 42–100% and specificity of 92–94% have been
reported using visual qualitative assessment
[22,
47,
48], although one study found
DWI to be unhelpful [52]. By
quantitative analysis, malignant vertebral body infiltration and vertebral
body fracture return lower ADC values than benign causes
[48,
49,
53–56].
A clear separation of ADC measurements between malignant and benign groups has
been observed [48,
53], but significant overlap
has also been reported [56].
Unfortunately, the threshold ADC values that can be applied to discriminate
between benign and malignant vertebral disease vary among studies because of
differences in the imaging sequences and parameters. Hence, the reader should
exercise caution when adopting these criteria for clinical practice.
Furthermore, inflammatory conditions, such as tuberculous infection
[49] and osteomyelitis
[57], can mimic malignant
disease on DWI.
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Fig. 10 —Schematic diagram shows variation in tumor apparent diffusion
coefficient (ADC) with treatment. Soon after initiation of chemotherapy or
radiation therapy, cell swelling occurs, which can lead to decrease in tumor
ADC. This is followed by cell necrosis and lysis, resulting in rise in ADC.
Treatment can also induce tumor apoptosis, resulting in cell shrinkage and
increased ADC. These apoptotic tumor cells may also undergo secondary lysis
(dotted arrow). After completion of treatment, there is
process of reequilibrium with resorption of extracellular fluid, leading to
decrease in ADC. Tumor regrowth (black curved arrow) can
also result in decreased ADC. (Schematic adapted from Moffat et al.
[59])
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One potential use of DWI is in the identification of recurrent or residual
tumor after treatment. A recent pilot study in patients with laryngeal cancer
showed the potential of the technique to differentiate residual or recurrent
tumor from postchemoradiation changes
[58]. What was particularly
interesting was the fact that DWI could be used to correctly identify a
patient as showing posttreatment changes even when PET suggested recurrent
disease [58]. Clearly, the
promising utility of DWI for this purpose requires further evaluation.
Monitoring Treatment Response
There is increasing interest in the application of DWI for detecting tumor
response. Effective anticancer treatment results in tumor lysis, loss of cell
membrane integrity, increased extracellular space, and, therefore, an increase
in water diffusion [59,
60]. The proposed mechanisms
in the variation of tumor ADC after the initiation of treatment are summarized
in Figure 10
[59].
However, this scheme does not consider the contribution of intravascular
perfusion to the diffusion measurement, which may be substantial in a tumor
[61]. Hence, therapies that
are targeted against tumor vasculature may also result in a reduction in the
ADC, especially when the DW images are acquired using low b values, which are
sensitive to vascular perfusion effects
[62].
The results of animal studies have confirmed that after the initiation of
chemotherapy, radiation therapy, or novel therapy, an increase in the ADC
value may be observed in those responding to treatment
[62]. Furthermore, treatment
effects can be observed within the first 24 hours after initiating treatment
[5,
62] due to cell swelling,
which results in a transient decrease in the ADC
[5,
62].
In human studies of vertebral metastases
[50] and brain tumors
[63], investigators have
reported that treatment response can be observed as a reduction in high signal
intensity at DWI. However, such visual appraisal may be confounded by T2
shine-through effects. Using ADC measurements, researchers who have studied
hepatocellular carcinoma [64],
cerebral gliomas [65], and
soft-tissue sarcoma [66] have
found that individuals who respond to treatment show a significant rise in the
ADC values after therapy. In a recent study of colorectal hepatic metastases,
an increase in ADC was observed in patients with at least a partial response
to treatment [67]; however, an
ADC increase was not observed in the nonresponders
[67].
Predicting Treatment Response
One of the most intriguing findings associated with the use of DWI in
cancer patients has been that ADC measurements appear to be able to predict
the response of tumor to chemotherapy and radiation treatment.
Table 2 summarizes the findings
of selected clinical DWI studies with results that appear to document this
phenomenon.
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TABLE 2: Clinical Diffusion-Weighted Imaging (DWI) Studies Reporting Baseline
Apparent Diffusion Coefficient (ADC) Values or Early Changes in ADC Values May
Be Used to Predict Response to Treatment
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Studies in rectal carcinoma
[68,
69], cerebral gliomas
[59,
63,
70], and colorectal hepatic
metastases [67] have shown
that cellular tumor with low baseline pretreatment ADC values respond better
to chemotherapy or radiation treatment than tumors that exhibit high
pretreatment ADC values. One possible explanation is that tumors with high
pretreatment ADC values are likely to be more necrotic than those with low
values. Necrotic tumors frequently are hypoxic, acidotic, and poorly perfused,
leading to diminished sensitivity to chemotherapy and to radiation
therapy.
Studies in cerebral gliomas
[59,
63,
71] and breast carcinoma
[72] have also shown that an
early increase in the ADC after commencing treatment was predictive of better
treatment outcome. In two of the studies
[71,
72], researchers found that an
increase in the ADC within 1 week of initiating treatment was predictive of at
least a partial response, with response being defined by tumor size reduction
at the end of therapy. The increase in the ADC preceded any reduction in tumor
size. The use of ADC to evaluate and predict response has also been assessed
in a number of animal studies
[62,
73–79]
(Table 3).
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TABLE 3: Assessing Treatment Response of Tumors Using Apparent Diffusion
Coefficient (ADC) Measurements in Animal Studies
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Many of the clinical studies evaluating DWI for assessing treatment
response have been performed in relatively small numbers of patients.
Nevertheless, the body of evidence suggests that ADC measurement is a
potentially useful tool that provides unique prognostic information and should
be more widely investigated in large clinical studies in the future.
Whole-Body Imaging
Whole-body DWI is a recently developed application of DWI that, as
previously described, is performed using a STIR EPI diffusion-weighted
technique with a high b value of 1,000 s/mm2 for background
suppression. By performing imaging at multiple stations in the body, a
composite image of the whole body can be constructed. The images are processed
using maximum intensity projection and are usually displayed using a reversed
black-and-white gray scale. Signals from normal tissue such as blood vessels,
fat, muscle, and bowel are suppressed. However, other normal structures such
as the spleen, prostate, testes, ovaries, endometrium, and spinal cord remain
visible [23]. Areas showing
restricted diffusion—for example, highly cellular lymph nodes—are
strikingly depicted (Fig. 11A,
11B). Not surprisingly, the
technique has been applied to evaluate lymphadenopathy in patients with
lymphoma and other cancers. Exquisite images displaying small foci of tumors
within the abdomen or peritoneum have also been presented in the literature
using this technique [23].
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Fig. 11A —T2-weighted and segment of whole-body diffusion-weighted
images. T2-weighted image (A) and diffusion-weighted inverted
gray-scale maximum-intensity-projection (b = 1,000 s/mm2) image
(B) of pelvis show nodal disease along both pelvic sidewalls in
63-year-old man with colon cancer. By performing imaging at multiple stations,
whole-body diffusion map can be constructed.
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Fig. 11B —T2-weighted and segment of whole-body diffusion-weighted
images. T2-weighted image (A) and diffusion-weighted inverted
gray-scale maximum-intensity-projection (b = 1,000 s/mm2) image
(B) of pelvis show nodal disease along both pelvic sidewalls in
63-year-old man with colon cancer. By performing imaging at multiple stations,
whole-body diffusion map can be constructed.
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The true utility of whole-body imaging requires further validation. One
potential application of DWI is the detection of tumors. This application
would be analogous to using whole-body STIR technique for tumor detection.
However, because DWI can detect abnormalities on the basis of tissue
cellularity, it is unclear whether abnormalities depicted using this technique
are necessarily malignant. There is currently a great deal of interest in the
development of whole-body DWI with a background-suppression technique.
However, more research is needed to establish the sensitivity and specificity
of the technique to detect nodal malignancy. Another challenge that must be
overcome for DWI to be used for tumor detection is accurate quantification of
the ADC.
Current Challenges and Future Developments
One of the greatest challenges to widespread adoption of DWI in the body
for tumor assessment is the lack of standardization. The techniques applied to
acquire DW images, including the choice of b values, vary considerably.
Consequently, considerable differences in the ADC values of similar diseases
have been reported using different techniques. Clearly, future standardization
of protocols (e.g., type of sequence, number of motion-probing gradient
directions, b values, and TRs and TEs) for both image acquisition and data
analysis across imaging platforms is important.
Current software tools that are available for quantitative analysis on most
commercial platforms are fairly basic and do not allow more complex
processing. DW images are inherently noisy, and the ability to perform noise
filtration may be helpful. Image registration can also help to reduce errors
in ADC calculations and further improve the quality of the ADC data. In
addition, other metrics, such as fractional anisotropy and perfusion fraction,
should be investigated in imaging the patient with cancer. The development and
availability of software that allows more sophisticated data analysis would be
welcomed.
The tumor microenvironment is both spatially and temporally heterogeneous.
By drawing a region of interest around a tumor, the summary statistical value,
such as the mean or median value, does not adequately reflect lesion
heterogeneity. Clearly, more sophisticated methods of describing the data
should be presented, such as the use of histogram analysis or of percentage
change
Top
Abstract
Introduction
