MedicaMundi

D.P Cormode - Translational and Molecular Imaging Institute, Mount Sinai School of

Z.A. Fayad - Medicine, New York, NY, USA.

X-ray computed tomography (CT) was invented in the late 1960s and early 1970s by Godfrey Hounsfield and Allan McLeod Cormack [1]. It has now become a very valuable medical imaging technology with applications in the diagnosis of many different conditions and injuries. The number of CT scans performed worldwide per year is now numbered in the hundreds of millions. Every material or substance has a characteristic, energy dependent X-ray attenuation profile, which influences the attenuation or image contrast observed in CT images acquired at different X-ray tube voltages. For example, see Figure 1, where the energy attenuation profiles of gold and iodine are presented. In Hounsfield's original paper on CT, he raised the possibility that materials could be identified via two CT scans performed at different X-ray tube voltages and the comparison of the data thus acquired [2], a technique sometimes referred to as “dual energy” CT. This is normally carried out with X-ray tube voltages of 80 and 140 kV. There are many applications of dual energy CT, for example in atherosclerosis [3], the diagnosis of gout [4], bone density determinations [5], iron quantification [6] and detection of urinary calculi [7]. This technique was first explored in the late 1970s and 1980s [8, 9], but the scans had to be performed at the two energies one after the other, which led to problems in image registration and other difficulties that discouraged development in this area [10].

Figure 1 The energy dependence of X-ray attenuation of gold and iodine. The k-edges of iodine and gold are marked. Data downloaded from the National Institute of Standards website (www.nist.gov/physlab/data/xraycoef/index.cfm).

In the last two to three decades CT technology has rapidly advanced due to the introduction of multi-slice scanners and the exponential growth in computing power. [11] With this improvement in technology, dual energy CT has regained favor in the past five years, with clinical scanners now available that acquire the two sets of images at different energies simultaneously, either through the use of two X-ray tubes mounted on the same running at different energies [12] or by fast voltage switching of a single X-ray tube [13], thus avoiding the prior issues of image registration (Table 1).

Table 1 Comparison of the specifications of several dual energy and multi-color CT systems.

While these dual energy CT scanners can produce impressive results, the ability to distinguish between tissues and materials is still relatively limited as the analysis is performed with only two data points, the high and low tube voltage scans. Making multiple data points could make better distinctions between materials, and is something that multi-color CT can do. In the rest of this article, we will discuss the basis of multi-color CT, highlight some examples of the results acquired with this type of imaging technique so far and discuss the advantages and disadvantages of the technique.

Development and uses of multi-color CT

Philips has developed a pre-clinical CT imaging system, known as multi-color or spectral CT, which can distinguish several different tissues/materials simultaneously (Table 1, Figure 2). [18] Multi-color CT also exploits the energy dependent attenuation of X-rays, but instead of scanning with two energies, it scans at one energy and uses a novel “photon counting” detector that can determine the energy of incident X-rays. This offers the possibility of new applications and diagnoses, such as enhanced characterization of hemorrhage [19] or atherosclerotic plaque [16].

Figure 2. The Philips multi-color CT scanner. The object table is in the foreground. The photon-counting detector is at the top right-hand side of the rotating gantry.

The novel detector used in multi-color CT is composed of cadmium telluride. When an X-ray hits the detector, a current is produced, whose magnitude is related to the energy of the photon. The system allocates the photons to one of six bins, e.g. 25-30 keV, 30-40 keV, 40-50 keV, 50-60 keV, 60-80 keV and 80-100 keV [18]. The limits of the bins can be freely adjusted. Contrast agents can be more easily detected when the limit of two of the bins is placed at the k-edge of that contrast agent.

The attenuation for a given energy (μ(E)) in any pixel of the image can be described as a linear combination of the attenuation arising from the Compton effect (C), the photoelectric effect (P), and any contrast agents present (CA1, CA2… CAn) to give the following equation:

μ(E)=a(C).μ(C, E)+a(P).μ(P, E)+a(CA1).μ(CA1, E)+ a(CA2).μ(CA2, E)+…

a(CAn).μ(CAn, E). a(C), a(P), a(CA1), etc. are weighting factors reflecting the contribution of that component to the attenuation and, in the case of contrast agents, reflect their concentration. As the attenuation due to the different factors for a particular energy or energy range is known, with enough data points (energy measurements) one can solve for the weighting factors and thus determine the concentrations of contrast agents. This process is performed for the whole field of view scanned and results in several images, the 'photoelectric' image, the 'Compton' image and images arising from any contrast agents used, e.g. an 'iodine' image. These images are displayed in different colors to aid interpretation and can be overlaid, hence the name 'multi-color' CT.

In the first experimental report of the use of multi-color CT, Schlomka et al. scanned a phantom that contained iodine, gadolinium and calcium-hydroxyapatite, in order to test the ability of the scanner to distinguish these materials [18]. They showed that these materials could be identified by the scanner in concentration ranges from 30-150 mM. Feuerlein et al. subsequently published a study where an artery phantom was scanned [20]. This phantom simulated a stented artery with a calcification and a gadolinium based contrast agent in the lumen of the artery, where the concentration of the gadolinium was such that its attenuation was the same as the calcification. The multi-color CT scanner was able to correctly identify the location of the gadolinium and hence the extent of the perfused lumen. The calcification appeared in the photoelectric image, as did the stent. The photoelectric images had much reduced beam hardening artifacts compared to conventional CT images, resulting in better image quality of the stent.

Boll et al. have applied multi-color CT to image cystic renal lesions [19]. With current diagnostic technology it is hard to determine if a lesion is high in protein content, undergoing hemorrhage or undergoing a contrast-enhancing transformation, with leads to difficulties in establishing an appropriate treatment plan [21]. The authors found that each type of lesion had a distinct spectral signature, which could lead to the ability to characterize them in patients. Last, in unpublished work, Pan et al. have used multi-color CT to detect bismuth nanoparticles targeted to thrombi in a model of atherosclerosis [22].

In addition to the Philips system, a team based at the University of Canterbury in New Zealand has developed a similar multi-color CT scanner (MARS), whose detectors are based on the Medipix 2 chip (Table 1). This chip was designed at CERN and can perform photon counting [23]. In a paper published in 2010, this team examined the ability of the system to distinguish iodine and barium from the tissues of a mouse. This was a difficult aim as the k-edges of iodine and barium are close, i.e. 33.0 keV and 37.4 keV respectively [17]. Phantoms of contrast agents and mice injected with contrast agents were scanned using four energy bins. The system was able to detect the iodine contrast agent in the heart and major blood vessels and barium that had been introduced into the lungs, which was a significant result.

Multi-color CT in atherosclerosis

In this section, we discuss in detail a recent study where multi-color CT was applied in atherosclerosis, and the potential advantages of such as approach.

Atherosclerosis is an inflammatory disease where there is a build-up of fatty tissue in the arteries known as plaques. Certain plaques are classed as 'vulnerable' to rupture. The consequences of such rupturing are thrombus formation, occlusion of the artery and potentially a heart attack or stroke [24]. A characteristic of vulnerable plaques is a high macrophage density [25]. In the field of atherosclerosis imaging, therefore, there is a lot of work being done in order to image macrophages and hence identify vulnerable plaque [26].

Multi-slice CT is a highly regarded technique for imaging the coronary arteries, as the whole heart can be imaged in less than five seconds, thus reducing the motion artifacts that affect imaging with MRI or PET, which have long acquisition times (in the region of ten minutes). From these CT images, the presence of occlusive plaques in the coronaries that threaten the blood flow to a patient‟s heart can be detected via the use of iodine contrast agents [27]. Also, the level of calcification in the arteries can be determined, which is a useful indicator of a patient's risk of a heart attack [28].

Gold nanoparticles were first reported as contrast agents for X-ray based imaging techniques in 2006 [29]. Gold nanoparticles have recently been developed that are coated with a mixture of phospholipids and proteins that is similar to high density lipoprotein (Au-HDL) [30]. In atherosclerotic mice, these nanoparticles were shown to specifically accumulate in macrophages in the arteries, and to have potential as macrophage imaging CT contrast agents. Despite this advance, conventional CT cannot distinguish the attenuation produced by Au-HDL accumulations in arteries from the calcifications formed in advanced atherosclerosis. While it is possible to identify sources of gold accumulation through comparison of pre-and post-contrast agent injection images, precise localization by image co-registration would be very difficult on scans performed at separate times, and it would be preferable to avoid the need for more than one scan.

In 2010, a report was published on the use of Au-HDL in conjunction with multi-color CT [16]. First, the authors evaluated the ability of the system to distinguish between Au-HDL, an iodine-based contrast agent and calcium phosphate, as a simulation of the clinical question (Figure 3a). Data were acquired using the following six energy bins: 25-34 keV, 34-50 keV, 50-80 keV, 80-91 keV, 91-110 keV and 110-130 keV (Figure 3b). These bins were chosen with the k-edges of iodine (33.2 keV) and gold in mind (80.7 keV). The data was processed to create four images: gold, iodine, photoelectric and Compton (Figure 3c). In the gold and iodine images, the system was shown to detect concentrations of contrast agent as low as 20 mM. Calcium phosphate was observed in the photoelectric images, with some degree of cross-talk into the gold and iodine images. The signal from the plastic of the rack and the water the contrast agents were dissolved in was found in the Compton image. From this initial experiment it seemed likely that distinguishing gold, iodine and calcifications could be possible in animals.

Figure 3. Scans of a phantom performed with a multi-color CT scanner. Reproduced, with permission, from D.P. Cormode et al. [16].

Figure 3a. Conventional CT scan of the phantom.

Figure 3b. Images of the different parts of the phantom derived from the different energy bins.

Figure 3c. “Gold”, “iodine”, “photoelectric” and “Compton” images of the phantom produced from the energy weighted data.

The experiments were continued with apolipoprotein E knockout (apoE KO) mice on a high cholesterol diet, an accepted model of atherosclerosis. First, some mice were injected with Au-HDL alone. These mice were scanned 24 hours post-injection and, excitingly, accumulations of gold were observed in the aortas of these mice, typically in areas such as at the aortic arch or by the renal arteries, areas where it is known that large plaques often develop (Figure 4a, b, c). In comparison, little gold was observed in the aortas of wild-type mice injected with Au-HDL. Next, mice were injected with Au-HDL and 24 hours later injected with an iodinated emulsion contrast agent. These mice were scanned and the data processed into gold, iodine, photoelectric and Compton images (Figure 4d and 4e).

In the iodine images it was possible to observe the arteries and the veins of the mice highlighted by the presence of the iodine contrast agent. In gold images, agent accumulation was observed in the aorta wall, while the bones of the mice were found in the photoelectric image. The tissues of the mice produced signal in the Compton image. Microscopy techniques performed on aortic sections confirmed this agent to be macrophage specific, hence the multi-color CT system can detect macrophages, stenosis and calcification in a single scan.

Figure 4. Multi-color CT images of mice. Reproduced, with permission, from D.P. Cormode et al. [16].

Figure 4a. Conventional CT image of the thorax of an apoE KO mouse injected with Au-HDL.

Figure 4b. Gold multi-color CT image of the same mouse.

Figure 4c. Overlay of Figures 4a and 4b.

Figure 4d. Conventional image of an apoE KO mouse injected with Au-HDL and an iodine contrast agent.

Figure 4e. Overlay image of the multi-color CT gold, iodine, photoelectric and Compton images of this mouse.

Discussion

It is clear that multi-color CT possesses some significant advantages over conventional CT for atherosclerosis imaging as well as for a number of other diseases. The study on AuHDL [16] and other recent studies[31-33] point to the potential of performing molecular imaging, in vivo detection of biological processes at the molecular level, [34] with CT. There are two significant advantages multi-color CT brings to CT molecular imaging. The first advantage is certainty that the contrast observed is due to the contrast agent and not due to some biological fluctuation. Second, only one scan is required with multi-color CT, as it is a form of hotspot imaging, whereas normal CT or MRI, for example, would require pre-and post-injection scans to be done. In addition to reducing the number of scans to be performed, the image analysis is made easier, as there is no need to compare two datasets acquired at different times.

It is clear that multi-color CT can identify gold, iodine, gadolinium, barium, calcifications and bismuth. It is likely that these systems can also identify many other elements that have a k-edge in the relevant region of the X-ray spectrum, i.e. 30-120 keV. This means that there are many possibilities for novel contrast agents for this new type of imaging. The two main considerations for designing and developing these novel contrast agents are achieving low detection limits and high biocompatibility. As such, these new agents should have high contrast density, sensitivity of detection, stability, low toxicity, and be excreted. Fortunately, the area of CT contrast agents is currently undergoing a revival with tremendous growth in the number of reports of new, nanoparticle-based agents. For example, novel nanoparticle-based contrast agents based on gold [35] iodine [36] and bismuth [37] have all recently been reported.

The main limitation to clinical translation of multi-color CT systems at this time is the relatively slow count rate of the CdTe detectors. This means that the scan times of multi-color CT scanners are slow compared to current clinical scanners. There are a number of approaches that are being pursued to overcome this problem. First, the material that the detectors are composed of is being refined to allow increased count rates [38]. Secondly, iterative or interior ROI image reconstruction methods are being used in place of the traditional back-filter projection methods used in conventional CT [39]. Thirdly, detectors with a smaller pixel area [20], or that are stacked in multiple layers, are being considered [40]. Lastly, other techniques such as bow-tie shaped filters are being pursued to allow improved scan times [39]. However, a multi-color clinical scanner is yet to be made available.

Conclusion

The multi-color CT systems developed by Philips and others have distinct advantages over conventional scanners in some disease settings such as atherosclerosis, especially when combined with nanoparticle contrast agents. However, the multi-color systems that have been reported so far are for pre-clinical work and, in addition, some of the contrast agents that have yielded exciting results are not yet FDA approved. Nevertheless, considerable interest is developing in this field and we expect that the issue of scan time will be overcome. Clinical multi-color scanners are expected to be on the market in three to five years time. When these scanners are more widespread, applications that are currently not thought of will likely be conceived and established. To conclude, we are optimistic about the potential for spectral CT to have a substantial impact on radiology and human health in the medium term.

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