Dr. Le, Dr. Bhosale, Dr. Skalruk, Dr. Ng, and Dr. Tamm are from the Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX.
tomography (CT) imaging has evolved markedly since its inception in the
1970s. Such advancements as spiral CT, followed by
multislice/multidector CT, created new applications because of
increasing speed and thinner-section imaging. The most recent advances
in CT—dual-energy imaging, perfusion imaging, iterative reconstruction,
and better postprocessing techniques—also promise new opportunities for
imaging. This review article will provide an overview of the potential
impact of these new technologies on oncologic imaging.
CT (DECT) originated in the 1970s, but its routine clinical
implementation was not practical until more recent developments were
made in computer software and hardware. The principle of DECT is that by
utilizing 2 energy settings, materials can be differentiated based on
their attenuation characteristics.1 Attenuation depends on
the spectrum of energy used and the material of the object itself. A
material’s density (Hounsfield Unit, HU) varies with tube energy
secondary to a basic linear combination of the photoelectric effect and
Compton scattering for a given material.
Tube energy is commonly
referred to in terms of kVp and keV. The much more common polychromatic
energy beam of conventional multidetector CT (MDCT) scanners is kVp (the
maximum energy in a polychromatic spectra; ie, 140 kVp, meaning a
spread of photon energies, with 140 being the highest), and keV
represents the monochromatic beams (by definition, a monochromatic beam
has only a single energy; ie, 70 keV represents the single energy
present within that beam). Therefore, any material’s HU can be plotted
on a curve as a function of kVp or keV (Figure 1). Furthermore, any
material density’s curve can be represented by the combination of 2
other materials’ known curves. These other materials are known as basis
materials. Two of the most commonly used basis materials are iodine and
water, since they are sufficiently different in atomic number to
represent the range of densities commonly used in medical imaging.
imaging typically obtains the density of any given voxel at energies of
80 kVp and 140 kVp from a single or dual-source CT scanner. How this
information is utilized depends on the manufacturer. Our experience is
with the single source General Electric (Milwaukee, WI) HD750 CT
scanner. With this scanner, information is then used to create basis
pair material-density images (ie, images consisting of water without
iodine or iodine without water). In addition, using software, a variety
of other material density images can be created, such as calcium without
iodine, or iodine without calcium, the latter useful in creating
angiographic images without calcified plaque.
DECT software can
also be used to mathematically create monochromatic energy images
(Figure 2). These images represent voxels as if they had been imaged
with a beam of only a single energy; ie, 70 keV. This eliminates the
problem of beam hardening, a phenomenon where the weaker energies of a
polychromatic beam are “filtered out.” As a polychromatic beam
penetrates deeper into an object, weaker energy photons are increasingly
absorbed, progressively “filtering” the beam to leave behind higher
energy photons. This may lead to inconsistent density measurements (HUs)
for a given material depending on where within an object a material is
located. In contrast, monochromatic energy images should provide more
consistent density measurements across an image and potentially minimize
shifts in density between pre- and postcontrast scans. Additional
benefits of DECT include the ability of such software to view
monochromatic energy images anywhere across the range from 40 to 140
keV. Low keV images enhance the presence of iodine, thereby improving
DECT has been found to be useful in several
applications. These include eliminating calcium-containing plaques from
arteriograms, minimizing metal artifacts, eliminating barium contrast
from the GI tract to enhance detection of bowel lesions, enhancing or
minimizing the conspicuity of iodine (creating virtual noncontrast
images), differentiating materials containing a variety of elements (the
urate crystals of gout versus the calcium pyrophosphate crystals of
pseudogout and distinguishing the various types of ureteral calculi by
chemical composition (Figure 3).2-4 Simultaneously,
sophisticated workstation software provides an understanding of the
meaning of this burgeoning data through the use of such tools as scatter
plots, density versus keV curves, contrast-to-noise curves, and virtual
atomic number graphs. A full discussion of such tools is beyond the
scope of this paper; the effective use of such tools is rapidly evolving
(See Figure 4 for an overview of dual-energy imaging).
applications of DECT are evolving in a variety of areas as well. We will
describe potential applications for the kidney, liver, and pancreas.
CT has been the primary imaging tool at many institutions for the
workup of renal masses. A typical MDCT exam includes noncontrast,
postcontrast, nephrographic, and delayed-phase images, including
thin-section image sets, to characterize renal masses. Thin-section
images can be postprocessed to elucidate vascular anatomy and the
relationship of tumor to vessels. Such information has helped to answer a
variety of questions, such as the extent of tumor thrombus that may be
present within the renal veins and inferior vena cava and for surgical
One challenge in renal imaging has been evaluating
cystic lesions to determine whether they demonstrate enhancement with
intravenous contrast, an indicator of cystic neoplasms. The data
provided by a DECT single acquisition can be used postscan to create a
wide range of monochromatic images and an essentially unlimited number
of types of material density images, limited only by the capabilities of
the workstation processing that data. Low monochromatic energy images
may also help with determining the presence of subtle enhancement,
particularly in such problematic lesions as hemorrhagic or proteinaceous
cysts. Water (minus iodine) material density images permit creation of
virtual noncontrast images, thereby potentially decreasing radiation
dose by avoiding a separate precontrast acquisition. Graser et al5
found that a virtual noncontrast phase provided by DECT yields a
reasonable approximation to a conventional true noncontrast enhanced
image, with an additional radiation dose reduction of 35%, since only 2
phases are needed on the DECT. The converse—iodine images (minus water)—
also enhance the conspicuity of iodine and, therefore, the detection of
enhancement. We have found these types of material density images in
conjunction with low monochromatic energy images to be useful and to
increase our confidence when assessing enhancement, such as in the case
of problematic hemorrhagic or proteinaceous cysts (Figure 5). We would,
however, caution that water and iodine basis pair material-density
images represent the amount of water and iodine necessary to mimic the
behavior of the material in a given voxel at different energies. They do
not represent the actual amount of iodine or water present within a
given voxel. The same applies for other types of material-density
The authors typically utilize DECT
during the late arterial phase of a multiphasic imaging protocol. This
is particularly useful for evaluating pathology manifesting as commonly
hyperdense on this phase of imaging. It has been shown that hyperdense
liver lesions, such as hepatocellular carcinoma and hypervascular
metastasis, are better visualized on DECT.6 Altenbernd et al6
found that low-energy images of DECT are more sensitive in detecting
hypervascular liver lesions (Figure 6). This is probably because a lower
KvP or keV enhances the conspicuity of iodininated contrast.7
Thicker-section maximum intensity projection (MIP) images, in our
experience, may further increase conspicuity. The tradeoff of a lower
keV is a decrease in the subjective image quality. DECT, as noted
earlier, allows for creation of angiographic images (higher
monochromatic energy) that can be utilized to assess, for example,
hepatic vascular anatomy for surgical planning. High- and low-energy
images can be created from the same imaging acquisition, eliminating the
need to choose between high contrast and low noise
Another issue of liver imaging is the frequently
encountered small, incidental, nonspecific lesion that statistically
most likely represents a benign lesion, such as a cyst.8 In
an oncologic patient, any improvement in the ability to differentiate
such cysts from small metastatic lesions would greatly aid treatment
planning. DECT may help improve such lesion characterization by making
subtle enhancement more conspicuous with low-energy monochromatic images
and iodine (minus water) material-density images. Similar to renal
cysts, hepatic cysts will appear relatively hypointense compared with
adjacent liver parenchyma on water material-density images and display a
lack of iodine enhancement on iodine material density images.
Metastatic disease should appear isodense relative to the adjacent liver
parenchyma on the water material-density images and demonstrate iodine
uptake on the iodine material-density images (Figure 7).1
has potential uses across the range of solid and cystic lesions that
may manifest within the pancreas. A recent study analyzed 35 patients
with 39 proven pancreatic neuroendocrine lesions who went to surgery.
Twenty-three were imaged by DECT (dual phase), and 11 were imaged by
single-energy (dual phase) CT. DECT showed a sensitivity of 96% for
lesion detection with the use of monochromatic images and iodine
material-density images. In contrast, single energy had a detection rate
of 69% (Figure 8).9
DECT may also provide better
conspicuity for typically hypodense pancreatic ductal adenocarcinomas
through the use of monochromatic images and iodine material density
images. Pancreatic adenocarcinomas are typically hypodense on the
arterial or pancreatic parenchymal phase and appear less conspicuous on
the portal phase where they can be isodense (Figure 9).10 Macari et al11
showed that with lower, 80 kVp vs. 120 kVp data generated from DECT,
hypodense and isodense pancreatic tumors are more conspicuous when
compared with higher peak kilovoltage images (Figure 10). While 80 kVp
images typically are possible only in patients with small body habitus
for single-energy CT scanners, DECT makes the entire range of energies
available from only a single-imaging acquisition. The use of
contrast-to-noise curves can help the radiologist identify energies at
which a tumor may be most conspicuous
DECT can also be used to reduce metal artifacts,
such as those that result from imaging adjacent to certain surgical
clips or stents, to better visualize extent of tumor or potentially
subtle recurrence, employing such special techniques as the metal
artifact reduction sequence (MARS,
General Electric, Milwaukee, WI) (Figure 12).
cystic lesions are problematic because of their high incidence, and
high likelihood of being benign. Identifying potentially solid
components can be helpful in targeting these lesions for endoscopic
ultrasound evaluation. DECT’s ability to potentially improve detection
of subtle enhancement in a cystic lesion will be useful in these
circumstances (Figure 13).
Potential future directions
is a relatively new technological development. It has been shown to
have potential utility in a wide variety of diseases. Recent advances by
manufacturers are decreasing the impact of such techniques on radiation
dose. Yu et al12 found that DECT used in adult imaging can
produce a set of images for routine diagnostic interpretation that are
of similar or improved quality compared to conventional single-energy
120-kVp scans with the same radiation exposure. Future developments for
DECT may include dose reduction by automated tube current modulation and
user-modifiable iterative reconstruction. More accurate, or
quantitative/semi-quantitative, approaches to identifying enhancement
with iodinated contrast would have significant advantages over
conventional CT density numbers (HU) derived by polychromatic beam
imaging, which are not absolute and can vary by scanner, reconstruction
technique, patient size, and the x-ray tube potential.13
Material-density images give the opportunity to potentially
quantitatively or semi-quantitatively determine the amount of iodine and
may, therefore, be helpful in providing additional characteristics for
identifying tumor treatment response.
DECT can detect and differentiate between iodine contrast and calcium based on their HU curves. Tran et al14
has proposed that the accuracy depends on the CT density of tissue and
is limited when CT attenuation is low. Future improvements in this area
are expected. The possibility of even more advanced techniques, such as
photon counting and better discernment of even lower energies, may
improve characterization of materials within tissues and thereby offer
additional benefits for oncologic imaging.
is a complex process involving a variety of cells and pro- and
antiangiogenic factors. Vascular endothelial growth factor (VEGF) and
fibroblast growth factor (FGF) are just 2 well-known proangiogenic
cytokines produced by tumors. In contrast to normal physiologic
angiogenesis, malignant tumor angiogenesis is often a disordered and
chaotic process, typically creating fragile vessels, arteriovenous
shunting, with high vessel permeability, unstable blood flow, and
heterogeneity of vascular density.15 Tumor progression is often associated with neovascularization via angiogenesis.16
normal physiologic processes and tumor growth and survival rely on
angiogenesis CT perfusion imaging, otherwise known as functional CT or
cine CT, which provides a noninvasive method to evaluate the variables
that provide insight into angiogenesis.
Perfusion CT or
functional multidetector-row CT (f-MDCT) with appropriate software can
be performed with contrast medium to assess vascular characteristics,
such as blood flow (BF), blood volume (BV), mean transit time (MTT), and
permeability surface area product (PS) in a variety of organs and
tumors.17,18 Perfusion multidetector-row CT study techniques
are continually evolving, but they are typically performed with a cine
(continuous scan) technique after intravenous contrast injection of
nonionic iodinated contrast. For example, a dual-phase approach can be
used for pathology in the abdomen and pelvis. With such an approach, a
phase I cine mode is performed over a 30 to 40 sec breath hold while the
scanner is stationary. In phase II, a delayed scan is obtained
consisting of short intermittent helical scans, each obtained during a
7-sec breath hold obtained at 15 sec intervals. The CT perfusion data
are then analyzed on an imaging workstation where a region of interest
(ROI) can be drawn over a pixel, artery, or vein to obtain time
contrast-enhancement curves. The software can then be used to generate
and depict parameters, such as CBF, CBV, or MTT as color-coded images
Prior studies have demonstrated increased
microvascular permeability in aggressive tumors with typically a
decreased permeability in response to antiangiogenic treatment. This has
been shown to correlate with decreased tumor growth.19,20
Other clinical applications for f-MDCT include potentially early
detection of treatment response, assessing tumor grade and prognosis,
and evaluating the utility of antiangiogenesis drugs21 (Figure 15). Hoeffner et al19
have shown in their initial studies that squamous cell carcinomas of
the head and neck demonstrated elevated CBF, CBV, and PS when compared
to normal tissue. CT perfusion parameters have also been shown to
correlate with FDG-PET measurements in assessing solitary pulmonary
nodules.22 Occult hepatic metastases have also been revealed with f-MDCT where there is high perfusion.23,24 Perfusion CT has also been shown useful in monitoring treatment response on the basis of angiogenesis.21
One challenge of f-MDCT is misregistration secondary to respiration or other motion. Studies at our institution25
have shown that absolute values and reproducible CT perfusion
parameters were markedly influenced by motion and data acquisition time
for liver tumors. Thus, additional work is needed to correct for motion
misregistration. Inconsistency in defining the ROI by those performing
the measurements is also a concern.26 It has also been estimated that a CT perfusion acquisition results in an additional 1 to
5 mSv in radiation dose, depending on the region imaged.21
continue to work toward resolving such challenges. In addition,
attempts are being made to increase the region of anatomic coverage.
Manufacturers such as Toshiba have developed 320-detector row scanners
that permit 16 cm of coverage by perfusion imaging. Other manufacturers,
such as Siemens and General Electric, utilize a “shuttle” or “rocker”
technique that can greatly increase the coverage of perfusion imaging
over that of a traditional stationery approach to cine data acquisition.
use has dramatically increased in recent years, owing to its wide
availability, speed, and robust diagnostic capabilities. Almost 69
million CT examinations were performed in 2007.27 It has been estimated that more than two-thirds of radiation exposure associated with medical imaging is due to CT.28,29
This has resulted in widespread public concern, making CT dose
reduction a priority at many institutions. This has especially become a
consideration particularly for cured, or likely to be cured, cancer
patients who may require frequent follow-up CT scans to exclude
CT reconstruction has traditionally been
performed by filtered back projection (FBP). Dose reduction is difficult
with this technique, as reduction results in readily perceived
increased noise. An alternative is iterative reconstruction (IR): iDose
(Philips), IRIS (Siemens), AIDR (Toshiba), and ASiR (GE). Iterative
reconstruction is an algorithm whereby image data are modified through
the use of advanced mathematical models.30 More specifically,
IR is a method to reconstruct 2-dimensional and 3-dimensional images
from measured projections of an object, beginning with an initial
“guess” of the object composition and iteratively improving on it by
comparing a synthesized projection from the object estimate with the
acquired projection data and making incremental changes to the previous
“guess.”31 Unlike traditional FBP, IR has a high
computational cost. IR improves image quality by reducing image noise,
thereby facilitating images at lower radiation doses. Schindera et al32
demonstrated in a phantom study that a 100-kVp abdominal CT protocol
with iterative reconstruction algorithm for simulated intermediate-sized
patients increased image quality and maintained diagnostic accuracy at a
lower radiation dose compared with a 120-kVp imaging protocol with an
FBP algorithm. Hara et al 33 found that a CT dose index
reduction of 32% to 65% could be obtained with iterative reconstruction
with no significant change in image quality compared to routine-dose CT
imaging. Prakash et al34 also demonstrated a 9.2% decrease in
image noise with ASiR 20% blend and a 37.2% decrease with ASiR 40%
blend. However, they noted that ASiR 40% or > can produce artifacts
at the tissue interfaces (Figure 16). A new technique known as model
based iterative reconstruction promises further noise reduction through
the use of even more advanced, and more computationally intensive,
Current MDCT can provide
isotropic datasets that can be postprocessed in a variety of ways. These
include multiplanar reformations, minimum intensity projections (MinIP)
to show low-density structures like the biliary tree, MIP for high
density structures like vessels, curved planar reformations to simplify
depiction of curving structures, such as ducts or vessels, and volume
rendering. These techniques can be applied to such data for problem
solving or to facilitate communication of findings. Segmentation
techniques, which allow for various objects (tumor, vessels, bone, etc.)
to be distinguished from one another and treated differently with
postprocessing techniques, have also advanced with increasing computer
Multiplanar reconstruction permits creation of
images that display anatomy and pathology in an orientation other than
that of the source images.35 This relatively simple
postprocessing technique, easily created on modern CT scanners, has been
described as a powerful tool to display and communicate complex
anatomic and pathologic information. It also has shown to help improve
the visualization of abdominal anatomy and pathology.36 In
the pancreatic cancer patient, our experience has shown that this
technique is particularly useful in staging and preoperative planning
(Figure 17). To see Figure 17 in our DICOM viewer, click here.
New advances include the ability to segment
structures from different phases of a multiphasic exam, and to “glue” or
register these objects to create a single 3D model. For example, the
arteries and tumor from the pancreatic parenchymal phase of a dual phase
pancreas protocol CT can be segmented and rendered together with the
portal vein from the portal venous phase of the same study (Figure 18).
Previously, only poorly opacified veins from the pancreatic parenchymal
phase could be depicted together with arteries and tumor, leading to a
suboptimal depiction of tumor, arteries, and veins. It is notable,
however, that techniques that rely on editing, such as segmentation for
volume rendering and plotting of points for curved planar reformations,
depend on a thorough understanding of anatomy and the ability to
discriminate pathology from normal anatomy. Therefore, having images
obtained from such postprocessing checked by a radiologist to confirm
that they accurately reflect the patient’s findings is important.
Simpler techniques, such as MIP images can provide an overview of
vascular structures, while minIP images can provide an overview of, for
example, biliary obstructive changes from Klatskin tumors, or the
relationship of pancreatic cystic tumors to adjacent ducts (Figures 19
One of the challenges has been workflow. Traditionally,
these large datasets had to be pushed to a workstation, often remote
from where a radiologist was reading studies. Recent developments, such
as thin-client architecture, allow for a small computer program, a
“thin” client, to be installed on a reading station. This client
software communicates with a server with sophisticated hardware
(graphics cards, advanced processors, and large amounts of RAM and hard
disk space), the large patient dataset, and software. The thin-client
allows the data to be manipulated with a speed and responsiveness
similar to that of the older workstation model.
An even newer
development is that of “cloud computing.” Cloud computing has been
described as an approach to computing in which resources and information
are provided through services over the Internet, in which the network
of services is collectively known as “the cloud.”37 In short,
data are moved from the local desktop or hardware to large centers
elsewhere, potentially far outside the medical institution. Benefits
include the medical institutions’ ability to scale to adjust their need,
growing when needed and only when needed. The software can be kept
continuously up to date if the company provides such resources.
Challenges that remain include issues of security as well as dependence
on very robust networks.
tomography continues to evolve at a rapid pace. Recent advances include
dual-energy imaging, perfusion imaging, iterative reconstruction, and
evolving postprocessing techniques. With the generation of
material-based images acquired through DECT, the detection of subtle
enhancements, and with improved artifact reduction, MDCT techniques have
the potential to significantly impact the diagnosis, staging, and
management of cancer.
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