Introduction

Material identification with conventional computed tomography (CT) is based on x-ray attenuation as quantified in Hounsfield Units and displayed in shades of gray at different window levels. However, material identification with conventional CT is limited by the dependence of the linear attenuation coefficient μ – and subsequently the CT number – on mass density, material type (effective atomic number, Z), beam energy, and CT scanner. Above all, CT number only provides an estimated average of the x-ray attenuation of different materials at a given kVp within a selected voxel, and different materials with similar attenuation coefficients (e.g., calcium and iodine) have similar CT numbers, even if they differ in mass attenuation coefficients and elemental compositions [1].

In contrast to a single-energy acquisition, dual-energy CT (DECT) allows for the selective identification of materials in tissues. This has important clinical implications as precise material identification with DECT is nowadays a mainstay for detection and characterization of different diseases, from head (e.g., differentiation of intracranial hemorrhage from iodinated contrast material staining) to toe (e.g., characterization of monosodium urate deposits in tophaceous gout) [2]. A more recent approach for material identification is represented by photon-counting computed tomography (PCCT), which is currently under research and technological development, and is being translated into clinical studies.

Material Identification: General Principles

The attenuation coefficient μ of any material can be described as the linear combination of two independent functions – the photoelectric effect and the Compton (incoherent) scattering. Compton scattering is relatively similar across most materials; conversely, the likelihood of photoelectric absorption varies considerably, depending mainly on the material and on the x-ray energy. When an atom undergoes the photoelectric effect , the innermost shell (the k-shell) electron is ejected via the incident photon. Photoelectric absorption depends strongly on the unique binding energy of any k-shell electron for a particular material. The k-edge is the minimum energy required for the photoelectric event to occur with a k-shell electron. The k-edge varies for each material and is higher with increasing atomic numbers (Z). The closer the x-ray energy level is to the k-edge of a material, the more the material attenuates. By analyzing photoelectric absorption properties of different materials at different x-ray energies, it is possible to identify materials with sufficiently different atomic numbers Z irrespective of their material density (Fig. 16.1) [3]. This basic principle, known as material decomposition, can be exploited for material identification using either DECT or PCCT.

Fig. 16.1
figure 1

Graph of linear attenuation coefficients for iodine at two different densities (assuming ρ = 1 g/cm3) (white), calcium (assuming ρ = 1 g/cm3) (yellow), and soft tissue (assuming ρ = 0.1 g/cm3) (violet) as a function of energy (in kiloelectron volts). (Adapted from McCollough et al. [2])

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Dual-Energy CT: Material Identification with Single- and Dual-Source Systems

DECT operates with low-energy (between 70 kVp and 100 kVp depending on the dual energy platform) and high-energy (between 140 kVp and 150 kVp depending on the dual energy platform) x-ray spectra, obtaining the relative attenuation of voxels in the imaged volume at these two energies [2]. DECT technology includes simultaneous (i.e., dual-layer detector DECT) and near-simultaneous (i.e., single-source rapid KV-switching DECT [rsDECT] and dual-source DECT [dsDECT]) low- and high-energy acquisition. The relative difference in attenuation at the two energies forms the basis for material decomposition with DECT. The ability of DECT to discriminate between two materials depends strongly on the difference between the characteristic CT number ratio (or index) for each material defined as the low- to high-kVp CT number ratio of a given material (Fig. 16.2) [4].

Fig. 16.2
figure 2

(a) CT Number of iodine (yellow), calcium (green), and water (violet) at different x-ray energies (a). Each material has its own characteristic changing pattern at low and high kilovoltage: at a low kilovoltage, materials with a high molecular weight (mainly iodine) show greater x-ray absorption due to a greater likelihood of the photoelectric effect occurring. The change in measurement of CT attenuation between low and high kilovoltage will enable differentiating iodine and calcium as demonstrated in (b) and (c). Color-coded dual-energy post-processed axial CT scan (b) demonstrates the bone in red owing to the presence of calcium and the vessels in blue owing to the presence of iodine. In the left femoral artery, the color-coded images allow to easily recognize the presence of calcified parietal atherosclerosis in red. The presence of calcified parietal atherosclerosis shows a similar hyperattenuation as compared to the lumen of the vessel in the axial CT scan in the arterial phase (c), thus preventing from easy differentiation between calcium and iodine on conventional CT

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Single-source DECT systems create material-specific images using a two-material decomposition algorithm. Though any two materials can be selected for two-material decomposition, if the basis pair includes two materials with similar spectral properties the resulting information will be limited as the mass density images would be nearly identical. In clinical practice, the typical basis pair for two-material decomposition comprises water and iodine because of the large difference in the attenuation-energy curves between these two materials.

Dual-source DECT systems typically create material-specific images using a three-material decomposition algorithm. Owing to the assumption of volume and mass conservation, the three-material decomposition algorithm allows to estimate the concentration of three materials with known elemental compositions in a voxel. In the post-processing, the three-material decomposition algorithm allows the suppression or the enhancement of one of the materials, depending on the desired clinical application. In particular, by erasing iodine contrast material, it is possible to produce a virtual non-contrast (VNC) image ; conversely, the enhancement of iodine contrast material will result in a color-coded iodine series. More recently, a multimaterial decomposition algorithm has been developed for fast kilovolt-peak-switching DECT and seems to be promising for fat quantification [5].

One of the main questions regarding VNC has been its clinical acceptability as a replacement of true non-contrast images. It is important to realize that the necessary assumption for accepting VNC in clinical practice is demonstration of an equivalence of these two image reconstructions. Previous studies have shown that VNC can provide reliable quantitative information regarding baseline tissue attenuations in HU, suggesting that these images may obviate the need of a separate nonenhanced acquisition during multiphasic CT protocols with substantial radiation dose savings (Fig. 16.3) [6, 7]. However, DE-based iodine subtraction is not always perfect. In the assessment of renal enhancement, lower attenuation values may be encountered in the dorsal portion of the kidneys next to the spine, owing to beam hardening affecting the low-kilovoltage images. Additional potential drawbacks of the VNC include erroneous subtraction of calcium (e.g., small renal calculi), as well as higher image noise.

Fig. 16.3
figure 3

Post-processed virtual non-contrast axial CT images of a simple renal cyst (a) and of an hemorrhagic renal cyst (b) show almost the same density as compared to the respective true non-contrast images (c and d, respectively), thus allowing a confident diagnosis

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Color-coded iodine images have several potential benefits. First, because these images are reconstructed from the same contrast-enhanced dual-energy dataset, they are not prone to motion and/or breathing artifacts and eliminate any potential variability that can occur during two separate acquisitions with a single-energy conventional multiphasic CT protocol. As such, quantitative information regarding the iodine uptake can be conveniently obtained from a single region of interest manually placed on the color-coded iodine map.

In addition, DECT allows for reconstruction of simulated monoenergetic images , which can be viewed through a broad range of energies, yielding increased consistency and precision of calculated CT numbers. Simulated monoenergetic images are generated through basis material decomposition, but the approach depends on the DECT platform – including an image-based method for the dsDECT and a projection-based method for rsDECT platform [8]. The different DECT hardware implementations may lead to potential significant differences in virtual monochromatic CT numbers from the same lesion examined with these two different methods [8]. In clinical practice, 70–75 KeV has been validated for routine adoption in many clinical contexts [9]. The adjustment of window and level values of simulated monoenergetic images may improve perceived conspicuity of lesions and the perception of image quality [10].

Unfortunately, one of the main limitations of DECT for material identification is represented by a high degree of overlap between the x-ray energy spectra generated at low (from 70 to 100 kVp) and high (140 or 150 kVp) peak tube potentials [11]. In addition, accuracy of material quantification may be significantly impacted by patient size and DECT parameters [12].

Photon-Counting CT: An Emerging Approach for Material Identification

PCCT is an emerging technology based on the adoption of photon-counting detectors (PCDs) to directly count individual photon interactions [13]. While in energy-integrating detectors (EIDs)  – currently used in clinical CT scanners – each detector element measures the total x-ray energy deposited in the detector during each measurement interval, PCDs directly convert individual x-ray photons into an electric pulse in the detector readout electronics, with the height of each pulse proportional to the individual photon energy. This improved PCCT technology potentially yields improved contrast-to-noise ratio, dose reductions of x-ray radiation and contrast agents, improved spatial resolution, reduction of beam hardening artifacts, and, above all, an improved material identification owing to a more accurate K-edge imaging compared to DECT [14]. However, the clinical adoption of PCCT is limited by technical challenges (i.e., cross talk, pulse pileup, charge charging, K-escape x-rays, and charge trapping) resulting in a performance degradation of PCDs [13, 14].

Clinical Applications of Material Identification with DECT

Post-processed images from DECT – including virtual monochromatic images, color-coded iodine overlay, and iodine-only images – may improve the detection and characterization of diffuse and focal diseases and contrast enhancement with a wide spectrum of potential benefits in different clinical settings.

Material Identification with DECT: Hepatic Applications

DECT has proved its role in the identification and characterization of focal and diffuse liver diseases . The exact knowledge of the number of hepatic lesions may modify the therapeutical approach at diagnosis and at follow-up for both primary and secondary tumors. Low-energy simulated monoenergetic and material density iodine DECT images outperform conventional single-energy CT yielding increased conspicuity and detection rates for both hypervascular and hypovascular hepatic lesions , including metastases and hepatocellular carcinoma [15,16,17].

The use of DECT iodine maps allows for improved diagnostic accuracy in the diagnosis of neoplastic portal vein thrombosis. According to one study, an iodine concentration of 0.9 mg/ml during the late hepatic arterial phase results results in a sensitivity of 100% and specificity of 95.2% in the diagnosis of neoplastic thrombosis in patients with hepatocellular carcinoma [18]. Owing to the possibility of quantifying iodine content in the iodine maps, DECT may be also used for the characterization of small incidental hypoattenuating hepatic lesions deemed indeterminate with conventional CT images [19]. In our experience, an iodine concentration of 1.2 mg I/mL may differentiate benign from malignant indeterminate hypoattenuating hepatic lesions between 5 and 20 mm with a sensitivity of 94% and a specificity of 93% (Fig. 16.4) [19]. Finally, iodine maps may help in the assessment of intratumoral vital tumor burden after different therapies for primary or secondary liver tumors (Fig. 16.5) [20, 21].

Fig. 16.4
figure 4

Iodine material attenuation map (a) shows absence of significant iodine uptake of a 0.9 cm hypoattenuating lesion on contrast-enhanced CT on portal venous phase (b) deemed indeterminate 68-year-old male with history of previous malignancy. Based on the optimized thresholds of 1.2 mg I/mL, the lesion is correctly diagnosed as benign

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Fig. 16.5
figure 5

Post-processed contrast map (a) shows peripheral viable tissue (arrow) in a hypodense lesion in the left liver lobe, improving the detection of the peripheral enhancement compared to contrast-enhanced CT in the arterial phase (b)

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In addition to the detection and characterization of focal hepatic lesions, DECT material decomposition analysis may have a clinical role in the assessment of diffuse liver diseases such as steatosis, liver fibrosis, and hemochromatosis. Quantification of hepatic fat is becoming increasingly important as it represents the reversible stage in nonalcoholic fatty liver disease before progression to fibrosis and cirrhosis. Owing to the decreased attenuation at lower energy levels in presence of fat content, the spectral curve for hepatic steatosis shows an increase in the attenuation of fat with an increase in tube potential. An attenuation change greater than 10 HU between 80 and 140 kVp may be suggestive of at least 25% fatty infiltration. To date, unenhanced single-energy CT remains more accurate than unenhanced DECT for hepatic fat quantification , probably because of the small difference in the attenuation-energy curves between water and fat [22]. Nowadays, it is possible to generate the fat volume fraction (i.e., fat content in units of percentage volume) from the contrast-enhanced scan potentially eliminating the need for a separated non-contrast CT scan. The technical solutions for the fat volume fraction quantification include a three-material decomposition algorithm which assumes fat, soft tissue, and iodine as the main components [4] or a multimaterial decomposition algorithm using fast-kilovolt-peak switching DECT which allows to quantify fat with a convex constrained least-squares problem [5].

In addition to fat quantification, other biopsy-related indicators – including lobular inflammation, hepatocyte ballooning, and fibrosis – are often necessary for proper diagnosis and management in patients with chronic liver disease. The increase in collagen deposition – which is typical in patients with chronic liver disease with progressing fibrosis – results in the expansion of the extravascular extracellular space with a larger volume distribution for the contrast agent within the liver parenchyma. Using iodine maps, DECT may be used to estimate the increased distribution of the contrast agent in the extracellular space. During the equilibrium phase, the concentration of contrast agent within the intravascular space approximately equals that within the extravascular extracellular space. Iodine concentrations on the equilibrium phase images correlates with higher stages of fibrosis, and the iodine concentration (liver/aorta) ratio may be used for differentiating quantitatively healthy liver from cirrhotic liver and to estimate the severity of the disease [23, 24]. Estimated cutoff values that yield good accuracy for discriminating METAVIR fibrosis stages in chronic liver disease are 0.260 for F1 stage (sensitivity 71%, specificity 100%), 0.274 for F2 (sensitivity 79%, specificity 77%), 0.286 for F3 (sensitivity 76%, specificity 82%), and 0.299 for F4 (sensitivity 90%, specificity 73%) [24].

The detection and quantification of hepatic iron overload in primary or secondary hemochromatosis are clinically relevant because iron accumulation may cause oxidative hepatocellular injury, progressive fibrosis, and, ultimately, hepatocellular carcinoma. MR imaging based on the transverse relaxation rates (R2∗ = 1/T2∗) is routinely used to quantify liver and heart iron concentrations. Owing to the possibility of three-material decomposition algorithm, DECT may identify hepatic iron overload over 3.2 mg of iron per gram or higher with a diagnostic performance comparable to R2∗, yielding 100% specificity at thresholds of 7.0 mg of iron per gram of dry tissue or higher [25].

Material Identification with DECT: Pancreatic Applications

Pancreatic cancer is the fourth leading cause of cancer death, with better survival in patients diagnosed at an early stage. Conventional CT has been considered for years the most accurate imaging modality for diagnosis and staging of pancreatic cancer, with a sensitivity for small tumors ranging from 63% to 85%. The adoption of a low-tube-voltage (80 kVp) DECT technique and iodine-specific images may increase the detection rate by improving tumor conspicuity and tumor-to-parenchyma contrast and reducing streak artifacts caused by metal implants (Fig. 16.6) [26, 27]. Nonetheless, these improvements come at the cost of decreased perceived image quality owing to increased image noise [26]. DECT may also prove to be useful in differentiating an enhancing pancreatic mass from a cystic lesion and in avoiding false-positive diagnosis of pancreatic mass in cases of pseudolesions, such as focal fatty change [28]. Finally, VNC images obtained from contrast-enhanced images can be used to identify calcifications and necrosis without additional unenhanced scans, thus reducing dose exposure in patients undergoing repetitive examinations.

Fig. 16.6
figure 6

Iodine material attenuation map (a) post-processed axial CT scan demonstrates a pancreatic head carcinoma (a) which corresponds to the hypoattenuating lesion in the contrast-enhanced axial CT scan in the portal venous phase (b)

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Material Identification with DECT: Genitourinary Applications

Material decomposition analysis for genitourinary diseases represents a major area of opportunity of DECT. VNC images allow the detection of urinary calculi with a sensitivity of 53–87%, without the need for a true unenhanced phase [4, 29]. However, the main benefit of DECT in the assessment of urinary calculi is the possibility of ascertain their mineral composition using the CT number ratio:

  • A CT number ratio <1.13 guides for uric acid stones.

  • A CT number ratio between 1.13 and 1.24 guides for cystine stones.

  • A CT number ratio >1.24 guides for calcium oxalates/phosphate stones.

Nonetheless the detection and characterization of urinary calculi is limited by the size and attenuation of the calculi, with decreased accuracy for smaller (<3 mm) stones, and by noise and artifacts occurring in large patients [29].

DECT proves to be particularly useful in the characterization of indeterminate renal masses [30]. VNC images can be confidently used to appreciate baseline characteristics of the mass, and virtual monochromatic images at an optimal energy level can overcome renal cyst pseudoenhancement [31]. Color-coded iodine overlay images allow for qualitative and quantitative assessment of contrast enhancement. An iodine concentration >2 mg/mL for rsDECT and >0.5 mg/mL for dsDECT is defined as contrast enhancement. Color-coded iodine overlay images and iodine maps can be used not only for the differentiation of benign and malignant renal lesions (Fig. 16.7) but also for the differentiation between clear cell and papillary renal cell carcinoma as well as for the assessment of response to treatment for metastatic renal cell carcinoma [29, 32, 33]. However, optimal cutoffs to discriminate clear cell and papillary renal cell carcinoma differ among different DECT systems, thus rendering difficult their routine adoption.

Fig. 16.7
figure 7

Blended 120-kVp (a), contrast map (b), and iodine material attenuation (c) CT images show a hypervascular lesion in the right kidney, with an iodine uptake of 1.4 mg I/ml, accurately classified as renal cell carcinoma

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Material Identification with DECT: Other Abdominal Applications

Incidental adrenal nodules are detected in approximately 4–5% of patients undergoing CT, with most of these nodules being benign adenomas. However, if the nodule has a density over 10 UH on unenhanced scan or a relative or absolute washout less than 40% and 60%, respectively, then the lesion is indeterminate, and further work-up – including biopsy or MR – is needed. DECT may overcome the need for an true unenhanced scan for adrenal lesions and offers the great advantage of differentiating both lipid-rich and lipid-poor adenomas from nonadenomas using material density analysis with a 96% sensitivity and 100% specificity (Fig. 16.8) [34, 35].

Fig. 16.8
figure 8

Virtual non-contrast (a), contrast-enhanced CT in the venous phase (b), and post-processed iodine map (c) show a spontaneously hyperattenuating right adrenal lesion without significant contrast enhancement or iodine uptake (0.1 mg I/mL), accurately diagnosed as adrenal hematoma

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DECT may be used as a further aid to evaluate various bowel disorders [36]. The increased conspicuity of subtle enhancement differences on iodine maps, overlay images, or low-kiloelectron volt imaging may allow for identification in patients with inflammatory, ischemic, or neoplastic bowel diseases – allowing accurate differentiation of tumor from stool – as well as depiction of active gastrointestinal bleeding as intraluminal hyperattenuation that persists on the iodine map but disappears on VNC [37]. Owing to the increased detectability of parenchymal lesions, it is expected that DECT may also have a great potential to assist the interpreting radiologist in the detection of solid organ injuries [37]. Though there is scant literature on DECT detectability of traumatic abdominopelvic lesions, post-processed DECT images have also the potential to detect areas of altered bowel wall enhancement, as well as vascular injuries and active extravasation [37]. Iodine-selective , simulated monoenergetic, and VNC images may potentially improve the detection of the so-called posttraumatic “shock bowel” by differentiating intramural hemorrhage, mucosal hyperenhancement, and hyperattenuating ingested content in the bowel lumen [37]. By analyzing VNC and iodine overlay images, active extravasation and its origin may also be confidently detected. However, further studies on applicability of DECT in bowel disorders and abdominopelvic injuries are still needed.

Material Identification with DECT: Chest Applications

The main application of DECT in chest pathologies is pulmonary embolism. Though widely used for detection of pulmonary embolism, conventional CT pulmonary angiography has a limited sensitivity (83%) for pulmonary emboli [38], with major challenges for small segmental and subsegmental emboli. Compared to CT pulmonary angiography, DECT iodine maps show a small incremental benefit in sensitivity for the detection of occlusive small (i.e., segmental and subsegmental) pulmonary emboli [39]. The simultaneous detection of a clot in a pulmonary artery on pulmonary conventional CT pulmonary angiography and of a corresponding wedge-shaped perfusion defect on DECT-based blood pool imaging indicates occlusive pulmonary embolism . However, other potential causes of perfusion defects on iodine maps – including aberrant vascular supply, beam-hardening artifacts or motion artifacts, or parenchymal diseases – must be kept in mind to avoid false-positive diagnosis of pulmonary embolism [40]. Iodine-selective and simulated monoenergetic images at low keV may also improve the detection of aortic dissection, aortic endoleaks, arteriovenous malformations, and contrast uptakes in lung malignancies [40,41,41]. DECT has also huge potential for evaluating the hemodynamic significance of coronary disease (i.e., myocardial perfusion) using myocardial iodine map as an addition to coronary artery assessment with plaque characterization as lipid-rich, fibrous-rich, calcified, or noncalcified plaque.

Material Identification with DECT: Musculoskeletal Applications

Though a comprehensive evaluation of CT in musculoskeletal applications goes beyond the purpose of this chapter, the application of the material decomposition algorithms for material identification is increasingly used for detection and characterization of musculoskeletal diseases. Applications of DECT in musculoskeletal imaging include gout, bone marrow edema, tendons, and ligaments and the use of monoenergetic techniques to minimize metal prosthesis beam-attenuating artifacts. DECT provides good diagnostic accuracy for detection of monosodium urate deposits in patients with gout, with a sensitivity of 90% [42]. False-negative diagnosis may occur in patients with recent-onset disease or on allopurinol with serum uric acid <6 mg/dl) [42]. To date, DECT may be indicated as second-line diagnostic technique in patients with possible gouty arthritis in whom polarizing microscopy of synovial fluid has failed to confirm a diagnosis.

More recently, DECT proved its clinical importance in traumatic patients. The detection of bone marrow edema (i.e., “bone bruise”) in traumatic patients is of utmost importance. Single-energy CT is limited by overlying trabecular bone, and MR imaging is considered the reference standard for the detection of bone marrow edema. However, thanks to the possibility of removing the confounding effect of trabecular bone, DECT provides a visual representation of the bone marrow composition with a sensitivity of 82% for bone marrow edema [28]. This has been shown to be particularly beneficial for non-displaced fractures, such as the knee and hip or spine [43, 44]. In order to preserve the anatomical information of the CT scan, the VNC image may also be presented as a color-coded map superimposed on the conventional CT image. Some limitations pertain to the DECT analysis of bone marrow edema including the difficulties in demonstrating bone marrow lesions adjacent to cortical bone owing to “masking” of the nearby cortex or in significantly sclerotic vertebral bodies [44].

The huge possibility offered by DECT to reduce streak artifacts caused by metal implants is of fundamental importance in the study of bone and soft tissue diseases in patients with metallic implants – i.e., in patients with knee or femoral prosthesis – yielding improved detection of lesions obscured by the metal artifacts [44].

Soft tissue characterization of collagenous structures  – including ligaments, tendons, and intervertebral disks – has generally been limited with conventional CT due to lack of attenuation contrast and in some cases increased beam hardening artifacts obscuring fine details. Due to the presence of dense hydroxylysine and hydroxyproline side chains within the collagen molecules, DECT can be potentially used to differentiate soft tissue collagenous structures (ligaments and tendons) due to their relatively high density. Once identified, they can be color-coded and combined with gray-scale CT images to help with anatomic localization and detection of pathologic conditions [44].

Material Identification with DECT: Head and Neck Applications

Though the literature on DECT for neck imaging is still scanty, DECT seems to be promising also in the detection of cartilage invasion by laryngeal and hypopharyngeal squamous cell carcinoma, a fundamental information for determining the appropriate treatment strategies for laryngeal and hypopharyngeal cancer [45].

Promising applications of DECT in brain diseases include bone removal in CT angiography of intracranial vessels and evaluation of vascular malformations, an accurate differentiation of high attenuation areas after mechanical revascularization in acute ischemic stroke, which may be related to contrast staining or brain hemorrhage. High keV monoenergetic images are also advantageous during the evaluation of brain aneurysms remnants after surgical clipping owing to the possibility of reducing metal artifacts.

Clinical Applications of Material Identification with PCCT

PCCT scanners are not yet commercially available, because their clinical use is still limited by many intrinsic technical challenges above described. However, the gaining obtained with PCCT will likely allow for improved spatial resolution with already proven added value for breast CT and potential value for cardiac, lung, orthopedic, and vascular applications, reduced iodine concentrations – which could be particularly important in patients with impaired renal function – and use of contrast agents other than iodine or barium, including gadolinium, gold, and platinum, which could be beneficial for patients with iodine allergies. Compared to conventional CT, PCCT offers lower noise with better contrast-to-noise resolution, reduction of beam-hardening and blooming artifacts allowing for more accurate estimation of HU value and improved luminal vessel evaluation, and decrease of radiation doses by at least 30–40%. The potential possibility of simultaneous multi-contrast agent imaging with PCCT could further reduce radiation doses and provide the possibility to match perfectly images from different phases enabling the detection and characterization of small lesions.

Discussion and Conclusion

Material density analysis through DECT overcomes ambiguity of HU measurements using single-energy CT. Material-specific images obtained with DECT provide qualitative and quantitative information about tissue composition and contrast agent distribution. The main contribution of DECT-based material characterization comes from iodine-specific images, which yield increased tissue contrast facilitating discrimination between normal and abnormal tissues. This will ultimately result in increased sensitivity for lesion detection and better characterization of tissue composition compared to single-energy CT. Several other materials, including calcium, fat, and uric acid, can be characterized using material decomposition techniques, which expands the clinical utility of DECT. The ability of DECT to discriminate different tissues is largely dependent of the energy spectral separation of the two energies, as well as the patient body size. These limitations will likely be mitigated with the potential introduction into clinical practice of PCCT systems.